AVAHO

MINNEAPOLIS -- Ignore the big health claims about vitamin supplements, pork, and nitrate-free food products. Meet patients “where they are,” even if that means you focus first on helping a morbidly obese patient maintain her weight instead of losing pounds. And use nutrition to empower patients and reduce the risk of cancer recurrence.

Dianne Piepenburg, MS, RDN, CSO, a certified oncology nutritionist at the Malcolm Randall VA Medical Center in Gainesville, Florida, offered these tips and more in a presentation about nutrition for cancer survivors. She spoke at the annual meeting of the Association of VA Hematology/Oncology (AVAHO).

According to the National Institutes of Health, an estimated 17 million cancer survivors live in the US, accounting for 5% of the population. Nearly two-thirds are aged ≥ 65 years.¹

Piepenburg highlighted the existence of certified specialists in oncology nutrition (CSOs). To be certified, registered dietitian nutritionists must have worked in that job for at least 2 years, have at least 2,000 hours of practice experience within the past 5 years and pass a board exam every 5 years.

Oncology nutritionists seek to empower cancer survivors to regain equilibrium in their lives, she said. “When a patient is told what scan to have next, what blood work they have to have, what treatment they need to be on, they feel they’re losing control,” she said. “Nutrition gives the power back to them, and they feel like there’s something they can do that’s in their control.”

Piepenburg urged colleagues to “meet patients where they are.” She gave the example of a patient with breast cancer whose body mass index is in the 50s, making her morbidly obese. “Our discussion wasn’t, ‘Let’s start [losing weight] today.’ Instead, I said, ‘Can we at least prevent you from gaining any more weight?’ She thought she could at least do that, try to recuperate a bit, and then start looking at a healthy weight loss. We’ll start there and circle back in a few months and see where we’re at.”

Piepenburg urged colleagues to bring exercise into the discussion. “We need people to be physically active no matter what phase of their survivorship journey they are in,” she said.

What about people who say, “I’ve never exercised a day in my life”? Her response: “I tell folks that we need them to move more. Maybe they’re walking to the mailbox or 3 laps around the house that day.”

Oncology patients should also watch sugar, meat, and processed foods. Refined sugar, fast food and processed food should be limited, Piepenburg said, along with red meats, such as beef, pork and lamb.

“Pork is not the ‘other white meat.’ How many of you grew up seeing and hearing that in the 1970s and 1980s? It’s a red meat, and it’s metabolized like a red meat.”

Advise patients to limit bacon, sausage, and lunch meat, she said, “even if they say, ‘I bought the nitrate-free and it’s really healthy for me.’”

It’s okay to eat some red meat, she said, “but there’s a tipping point. Tell them they can have some red meat but have it as a treat and please focus more on plant-based proteins—nuts, beans, legumes. But it’s tough for a lot of our veterans who grew up on meat and potatoes, and the only vegetable they eat is corn.”

It’s tough to limit grilling in a place like Minnesota, Piepenburg said, where the prime grilling season is short, and locals go a bit nuts when it’s nice enough outside. “I tell them to at least marinate the meat and put it on indirect heat.”

Finally, she encouraged oncology care providers to not fall for vitamin hype. Don’t rely on supplements for cancer prevention, she said. With some exceptions, she said, research has suggested they don’t work, and a 1990s study of beta-carotene and retinyl palmitate (vitamin A) in lung cancer was halted because patients actually fared worse on the regimen, although the effects didn’t seem to persist.²

MINNEAPOLIS -- Ignore the big health claims about vitamin supplements, pork, and nitrate-free food products. Meet patients “where they are,” even if that means you focus first on helping a morbidly obese patient maintain her weight instead of losing pounds. And use nutrition to empower patients and reduce the risk of cancer recurrence.

Dianne Piepenburg, MS, RDN, CSO, a certified oncology nutritionist at the Malcolm Randall VA Medical Center in Gainesville, Florida, offered these tips and more in a presentation about nutrition for cancer survivors. She spoke at the annual meeting of the Association of VA Hematology/Oncology (AVAHO).

According to the National Institutes of Health, an estimated 17 million cancer survivors live in the US, accounting for 5% of the population. Nearly two-thirds are aged ≥ 65 years.¹

Piepenburg highlighted the existence of certified specialists in oncology nutrition (CSOs). To be certified, registered dietitian nutritionists must have worked in that job for at least 2 years, have at least 2,000 hours of practice experience within the past 5 years and pass a board exam every 5 years.

Oncology nutritionists seek to empower cancer survivors to regain equilibrium in their lives, she said. “When a patient is told what scan to have next, what blood work they have to have, what treatment they need to be on, they feel they’re losing control,” she said. “Nutrition gives the power back to them, and they feel like there’s something they can do that’s in their control.”

Piepenburg urged colleagues to “meet patients where they are.” She gave the example of a patient with breast cancer whose body mass index is in the 50s, making her morbidly obese. “Our discussion wasn’t, ‘Let’s start [losing weight] today.’ Instead, I said, ‘Can we at least prevent you from gaining any more weight?’ She thought she could at least do that, try to recuperate a bit, and then start looking at a healthy weight loss. We’ll start there and circle back in a few months and see where we’re at.”

Piepenburg urged colleagues to bring exercise into the discussion. “We need people to be physically active no matter what phase of their survivorship journey they are in,” she said.

What about people who say, “I’ve never exercised a day in my life”? Her response: “I tell folks that we need them to move more. Maybe they’re walking to the mailbox or 3 laps around the house that day.”

Oncology patients should also watch sugar, meat, and processed foods. Refined sugar, fast food and processed food should be limited, Piepenburg said, along with red meats, such as beef, pork and lamb.

“Pork is not the ‘other white meat.’ How many of you grew up seeing and hearing that in the 1970s and 1980s? It’s a red meat, and it’s metabolized like a red meat.”

Advise patients to limit bacon, sausage, and lunch meat, she said, “even if they say, ‘I bought the nitrate-free and it’s really healthy for me.’”

It’s okay to eat some red meat, she said, “but there’s a tipping point. Tell them they can have some red meat but have it as a treat and please focus more on plant-based proteins—nuts, beans, legumes. But it’s tough for a lot of our veterans who grew up on meat and potatoes, and the only vegetable they eat is corn.”

It’s tough to limit grilling in a place like Minnesota, Piepenburg said, where the prime grilling season is short, and locals go a bit nuts when it’s nice enough outside. “I tell them to at least marinate the meat and put it on indirect heat.”

Finally, she encouraged oncology care providers to not fall for vitamin hype. Don’t rely on supplements for cancer prevention, she said. With some exceptions, she said, research has suggested they don’t work, and a 1990s study of beta-carotene and retinyl palmitate (vitamin A) in lung cancer was halted because patients actually fared worse on the regimen, although the effects didn’t seem to persist.²

MINNEAPOLIS -- Ignore the big health claims about vitamin supplements, pork, and nitrate-free food products. Meet patients “where they are,” even if that means you focus first on helping a morbidly obese patient maintain her weight instead of losing pounds. And use nutrition to empower patients and reduce the risk of cancer recurrence.

Dianne Piepenburg, MS, RDN, CSO, a certified oncology nutritionist at the Malcolm Randall VA Medical Center in Gainesville, Florida, offered these tips and more in a presentation about nutrition for cancer survivors. She spoke at the annual meeting of the Association of VA Hematology/Oncology (AVAHO).

According to the National Institutes of Health, an estimated 17 million cancer survivors live in the US, accounting for 5% of the population. Nearly two-thirds are aged ≥ 65 years.¹

Piepenburg highlighted the existence of certified specialists in oncology nutrition (CSOs). To be certified, registered dietitian nutritionists must have worked in that job for at least 2 years, have at least 2,000 hours of practice experience within the past 5 years and pass a board exam every 5 years.

Oncology nutritionists seek to empower cancer survivors to regain equilibrium in their lives, she said. “When a patient is told what scan to have next, what blood work they have to have, what treatment they need to be on, they feel they’re losing control,” she said. “Nutrition gives the power back to them, and they feel like there’s something they can do that’s in their control.”

Piepenburg urged colleagues to “meet patients where they are.” She gave the example of a patient with breast cancer whose body mass index is in the 50s, making her morbidly obese. “Our discussion wasn’t, ‘Let’s start [losing weight] today.’ Instead, I said, ‘Can we at least prevent you from gaining any more weight?’ She thought she could at least do that, try to recuperate a bit, and then start looking at a healthy weight loss. We’ll start there and circle back in a few months and see where we’re at.”

Piepenburg urged colleagues to bring exercise into the discussion. “We need people to be physically active no matter what phase of their survivorship journey they are in,” she said.

What about people who say, “I’ve never exercised a day in my life”? Her response: “I tell folks that we need them to move more. Maybe they’re walking to the mailbox or 3 laps around the house that day.”

Oncology patients should also watch sugar, meat, and processed foods. Refined sugar, fast food and processed food should be limited, Piepenburg said, along with red meats, such as beef, pork and lamb.

“Pork is not the ‘other white meat.’ How many of you grew up seeing and hearing that in the 1970s and 1980s? It’s a red meat, and it’s metabolized like a red meat.”

Advise patients to limit bacon, sausage, and lunch meat, she said, “even if they say, ‘I bought the nitrate-free and it’s really healthy for me.’”

It’s okay to eat some red meat, she said, “but there’s a tipping point. Tell them they can have some red meat but have it as a treat and please focus more on plant-based proteins—nuts, beans, legumes. But it’s tough for a lot of our veterans who grew up on meat and potatoes, and the only vegetable they eat is corn.”

It’s tough to limit grilling in a place like Minnesota, Piepenburg said, where the prime grilling season is short, and locals go a bit nuts when it’s nice enough outside. “I tell them to at least marinate the meat and put it on indirect heat.”

Finally, she encouraged oncology care providers to not fall for vitamin hype. Don’t rely on supplements for cancer prevention, she said. With some exceptions, she said, research has suggested they don’t work, and a 1990s study of beta-carotene and retinyl palmitate (vitamin A) in lung cancer was halted because patients actually fared worse on the regimen, although the effects didn’t seem to persist.²

SAN FRANCISCO – Should targeted agents replace chemoimmunotherapy (CIT) as first-line treatment for chronic lymphocytic leukemia (CLL)? A recent debate suggests there’s no consensus.

Jennifer Smith/MDedge News

William G. Wierda, MD, PhD, of The University of Texas MD Anderson Cancer Center in Houston, and Jennifer R. Brown, MD, PhD, of Dana-Farber Cancer Institute in Boston, debated the topic at the National Comprehensive Cancer Network Hematologic Malignancies Annual Congress.

Dr. Wierda argued that CLL patients should receive a BTK inhibitor or BCL2 inhibitor, with or without obinutuzumab, as first-line therapy because these targeted agents have been shown to provide better progression-free survival (PFS) than CIT, and the targeted therapies may prolong overall survival (OS) as well.

Dr. Brown countered that targeted agents don’t improve PFS for all CLL patients, improved PFS doesn’t always translate to improved OS, and targeted agents cost more than CIT.
 

No role for CIT as first-line treatment

“We have two approaches right now, with nonchemoimmunotherapy-based treatment,” Dr. Wierda said. “One approach, with small-molecule inhibitors, is to have a sustained and durable period of disease control, particularly with BTK inhibitors. The other strategy that has emerged is deep remissions with fixed-duration treatment with BCL2 small-molecule inhibitor-based therapy, which, I would argue, is better than being exposed to genotoxic chemoimmunotherapy.”

Dr. Wierda went on to explain that the BTK inhibitor ibrutinib has been shown to improve PFS, compared with CIT, in phase 3 trials.

In the iLLUMINATE trial, researchers compared ibrutinib plus obinutuzumab to chlorambucil plus obinutuzumab as first-line treatment in CLL. At a median follow-up of 31.3 months, the median PFS was not reached in the ibrutinib arm and was 19 months in the chlorambucil arm (P less than .0001; Lancet Oncol. 2019 Jan;20[1]:43-56).

In the A041202 study, researchers compared ibrutinib alone (Ib) or in combination with rituximab (Ib-R) to bendamustine plus rituximab (BR) in untreated, older patients with CLL. The 2-year PFS estimates were 74% in the BR arm, 87% in the Ib arm, and 88% in the Ib-R arm (P less than .001 for BR vs. Ib or Ib-R; N Engl J Med. 2018; 379:2517-28).

In the E1912 trial, researchers compared Ib-R to fludarabine, cyclophosphamide, and rituximab (FCR) in younger, untreated CLL patients. The 3-year PFS was 89.4% with Ib-R and 72.9% with FCR (P less than .001; N Engl J Med. 2019 Aug 1;381:432-43).

Dr. Wierda noted that the E1912 trial also showed superior OS with Ib-R. The 3-year OS rate was 98.8% with Ib-R and 91.5% with FCR (P less than .001). However, there was no significant difference in OS between the treatment arms in the A041202 trial or the iLLUMINATE trial.

“But I would argue that is, in part, because of short follow-up,” Dr. Wierda said. “The trials were all designed to look at progression-free survival, not overall survival. With longer follow-up, we may see differences in overall survival emerging.”

Dr. Wierda went on to say that fixed‐duration treatment with the BCL2 inhibitor venetoclax can improve PFS over CIT.

In the phase 3 CLL14 trial, researchers compared fixed-duration treatment with venetoclax plus obinutuzumab to chlorambucil plus obinutuzumab in previously untreated CLL patients with comorbidities. The estimated PFS at 2 years was 88.2% in the venetoclax group and 64.1% in the chlorambucil group (P less than .001; N Engl J Med. 2019; 380:2225-36).

“[There was] no difference in overall survival,” Dr. Wierda noted. “But, again, I would argue ... that follow-up is relatively limited. We may ultimately see a difference in overall survival.”

Based on these findings, Dr. Wierda made the following treatment recommendations:

• Any CLL patient with del(17p) or TP53 mutation, and older, unfit patients with unmutated IGHV should receive a BTK inhibitor, with or without obinutuzumab.
• All young, fit patients, and older, unfit patients with mutated IGHV should receive a BCL2 inhibitor plus obinutuzumab.

Dr. Wierda also noted that ibrutinib and venetoclax in combination have shown early promise for patients with previously untreated CLL (N Engl J Med. 2019; 380:2095-2103).
 

CIT still has a role as first-line treatment

Dr. Brown suggested that a PFS benefit may not be enough to recommend targeted agents over CIT. For one thing, the PFS benefit doesn’t apply to all patients, as the IGHV-mutated subgroup does equally well with CIT and targeted agents.

Jennifer Smith/MDedge News

In the IGHV-mutated group from the E1912 trial, the 3-year PFS was 88% for patients who received Ib-R and those who received FCR (N Engl J Med. 2019 Aug 1;381:432-43). In the A041202 study, the 2-year PFS among IGHV-mutated patients was 87% in the BR arm, 86% in the Ib arm, and 88% in the Ib-R arm (N Engl J Med. 2018; 379:2517-28).

In the CLL14 trial, PFS rates were similar among IGHV-mutated patients who received chlorambucil plus obinutuzumab and IGHV-mutated or unmutated patients who received venetoclax and obinutuzumab (N Engl J Med. 2019; 380:2225-36).

Dr. Brown also noted that the overall improvement in PFS observed with ibrutinib and venetoclax doesn’t always translate to improved OS.

In the A041202 study, there was no significant difference in OS between the Ib, Ib-R, and BR arms (N Engl J Med. 2018; 379:2517-28). There was no significant difference in OS between the ibrutinib and chlorambucil arms in the iLLUMINATE trial (Lancet Oncol. 2019 Jan;20[1]:43-56). And there was no significant difference in OS between the venetoclax and chlorambucil arms in the CLL14 trial (N Engl J Med. 2019; 380:2225-36).

However, in the RESONATE-2 trial, ibrutinib provided an OS benefit over chlorambucil. The 2-year OS was 95% and 84%, respectively (P = .0145; Haematologica. Sept 2018;103:1502-10). Dr. Brown said the OS advantage in this study was due to the “very poor comparator of chlorambucil and very limited crossover.”

As Dr. Wierda mentioned, the OS rate was higher with Ib-R than with FCR in the E1912 trial. The 3-year OS rate was 98.8% and 91.5%, respectively (P less than .001; N Engl J Med. 2019 Aug 1;381:432-43). Dr. Brown noted, however, that there were few deaths in this study, and many of them “were not clearly related to the disease or its treatment.”

Dr. Brown also pointed out that FCR has been shown to have curative potential in IGHV-mutated CLL in both the FCR300 trial (Blood. 2016 127:303-9) and the CLL8 trial (Blood. 2016 127:208-15).

Another factor to consider is the greater cost of targeted agents. One analysis suggested the per-patient lifetime cost of CLL treatment in the United States will increase from $147,000 to $604,000 as targeted therapies overtake CIT as first-line treatment (J Clin Oncol. 2017 Jan 10;35[2]:166-174).

“Given all of the above, chemoimmunotherapy is going to remain part of the treatment repertoire for CLL,” Dr. Brown said. “It’s our only known potential cure for the fit, mutated patients ... and can also result in prolonged treatment-free intervals for patients who are older. As we manage CLL as a chronic disease over a lifetime, we need to continue to have this in our armamentarium.”

Specifically, Dr. Brown said CIT is appropriate for patients who don’t have del(17p) or mutated TP53. FCR should be given to young, fit patients with IGHV-mutated CLL, and FCR or BR should be given to older patients and young, fit patients with IGHV-unmutated CLL.

Dr. Brown and Dr. Wierda reported financial ties to multiple pharmaceutical companies, including makers of CLL treatments.

[email protected]

SAN FRANCISCO – Should targeted agents replace chemoimmunotherapy (CIT) as first-line treatment for chronic lymphocytic leukemia (CLL)? A recent debate suggests there’s no consensus.

Jennifer Smith/MDedge News

William G. Wierda, MD, PhD, of The University of Texas MD Anderson Cancer Center in Houston, and Jennifer R. Brown, MD, PhD, of Dana-Farber Cancer Institute in Boston, debated the topic at the National Comprehensive Cancer Network Hematologic Malignancies Annual Congress.

Dr. Wierda argued that CLL patients should receive a BTK inhibitor or BCL2 inhibitor, with or without obinutuzumab, as first-line therapy because these targeted agents have been shown to provide better progression-free survival (PFS) than CIT, and the targeted therapies may prolong overall survival (OS) as well.

Dr. Brown countered that targeted agents don’t improve PFS for all CLL patients, improved PFS doesn’t always translate to improved OS, and targeted agents cost more than CIT.
 

No role for CIT as first-line treatment

“We have two approaches right now, with nonchemoimmunotherapy-based treatment,” Dr. Wierda said. “One approach, with small-molecule inhibitors, is to have a sustained and durable period of disease control, particularly with BTK inhibitors. The other strategy that has emerged is deep remissions with fixed-duration treatment with BCL2 small-molecule inhibitor-based therapy, which, I would argue, is better than being exposed to genotoxic chemoimmunotherapy.”

Dr. Wierda went on to explain that the BTK inhibitor ibrutinib has been shown to improve PFS, compared with CIT, in phase 3 trials.

In the iLLUMINATE trial, researchers compared ibrutinib plus obinutuzumab to chlorambucil plus obinutuzumab as first-line treatment in CLL. At a median follow-up of 31.3 months, the median PFS was not reached in the ibrutinib arm and was 19 months in the chlorambucil arm (P less than .0001; Lancet Oncol. 2019 Jan;20[1]:43-56).

In the A041202 study, researchers compared ibrutinib alone (Ib) or in combination with rituximab (Ib-R) to bendamustine plus rituximab (BR) in untreated, older patients with CLL. The 2-year PFS estimates were 74% in the BR arm, 87% in the Ib arm, and 88% in the Ib-R arm (P less than .001 for BR vs. Ib or Ib-R; N Engl J Med. 2018; 379:2517-28).

In the E1912 trial, researchers compared Ib-R to fludarabine, cyclophosphamide, and rituximab (FCR) in younger, untreated CLL patients. The 3-year PFS was 89.4% with Ib-R and 72.9% with FCR (P less than .001; N Engl J Med. 2019 Aug 1;381:432-43).

Dr. Wierda noted that the E1912 trial also showed superior OS with Ib-R. The 3-year OS rate was 98.8% with Ib-R and 91.5% with FCR (P less than .001). However, there was no significant difference in OS between the treatment arms in the A041202 trial or the iLLUMINATE trial.

“But I would argue that is, in part, because of short follow-up,” Dr. Wierda said. “The trials were all designed to look at progression-free survival, not overall survival. With longer follow-up, we may see differences in overall survival emerging.”

Dr. Wierda went on to say that fixed‐duration treatment with the BCL2 inhibitor venetoclax can improve PFS over CIT.

In the phase 3 CLL14 trial, researchers compared fixed-duration treatment with venetoclax plus obinutuzumab to chlorambucil plus obinutuzumab in previously untreated CLL patients with comorbidities. The estimated PFS at 2 years was 88.2% in the venetoclax group and 64.1% in the chlorambucil group (P less than .001; N Engl J Med. 2019; 380:2225-36).

“[There was] no difference in overall survival,” Dr. Wierda noted. “But, again, I would argue ... that follow-up is relatively limited. We may ultimately see a difference in overall survival.”

Based on these findings, Dr. Wierda made the following treatment recommendations:

• Any CLL patient with del(17p) or TP53 mutation, and older, unfit patients with unmutated IGHV should receive a BTK inhibitor, with or without obinutuzumab.
• All young, fit patients, and older, unfit patients with mutated IGHV should receive a BCL2 inhibitor plus obinutuzumab.

Dr. Wierda also noted that ibrutinib and venetoclax in combination have shown early promise for patients with previously untreated CLL (N Engl J Med. 2019; 380:2095-2103).
 

CIT still has a role as first-line treatment

Dr. Brown suggested that a PFS benefit may not be enough to recommend targeted agents over CIT. For one thing, the PFS benefit doesn’t apply to all patients, as the IGHV-mutated subgroup does equally well with CIT and targeted agents.

Jennifer Smith/MDedge News

In the IGHV-mutated group from the E1912 trial, the 3-year PFS was 88% for patients who received Ib-R and those who received FCR (N Engl J Med. 2019 Aug 1;381:432-43). In the A041202 study, the 2-year PFS among IGHV-mutated patients was 87% in the BR arm, 86% in the Ib arm, and 88% in the Ib-R arm (N Engl J Med. 2018; 379:2517-28).

In the CLL14 trial, PFS rates were similar among IGHV-mutated patients who received chlorambucil plus obinutuzumab and IGHV-mutated or unmutated patients who received venetoclax and obinutuzumab (N Engl J Med. 2019; 380:2225-36).

Dr. Brown also noted that the overall improvement in PFS observed with ibrutinib and venetoclax doesn’t always translate to improved OS.

In the A041202 study, there was no significant difference in OS between the Ib, Ib-R, and BR arms (N Engl J Med. 2018; 379:2517-28). There was no significant difference in OS between the ibrutinib and chlorambucil arms in the iLLUMINATE trial (Lancet Oncol. 2019 Jan;20[1]:43-56). And there was no significant difference in OS between the venetoclax and chlorambucil arms in the CLL14 trial (N Engl J Med. 2019; 380:2225-36).

However, in the RESONATE-2 trial, ibrutinib provided an OS benefit over chlorambucil. The 2-year OS was 95% and 84%, respectively (P = .0145; Haematologica. Sept 2018;103:1502-10). Dr. Brown said the OS advantage in this study was due to the “very poor comparator of chlorambucil and very limited crossover.”

As Dr. Wierda mentioned, the OS rate was higher with Ib-R than with FCR in the E1912 trial. The 3-year OS rate was 98.8% and 91.5%, respectively (P less than .001; N Engl J Med. 2019 Aug 1;381:432-43). Dr. Brown noted, however, that there were few deaths in this study, and many of them “were not clearly related to the disease or its treatment.”

Dr. Brown also pointed out that FCR has been shown to have curative potential in IGHV-mutated CLL in both the FCR300 trial (Blood. 2016 127:303-9) and the CLL8 trial (Blood. 2016 127:208-15).

Another factor to consider is the greater cost of targeted agents. One analysis suggested the per-patient lifetime cost of CLL treatment in the United States will increase from $147,000 to $604,000 as targeted therapies overtake CIT as first-line treatment (J Clin Oncol. 2017 Jan 10;35[2]:166-174).

“Given all of the above, chemoimmunotherapy is going to remain part of the treatment repertoire for CLL,” Dr. Brown said. “It’s our only known potential cure for the fit, mutated patients ... and can also result in prolonged treatment-free intervals for patients who are older. As we manage CLL as a chronic disease over a lifetime, we need to continue to have this in our armamentarium.”

Specifically, Dr. Brown said CIT is appropriate for patients who don’t have del(17p) or mutated TP53. FCR should be given to young, fit patients with IGHV-mutated CLL, and FCR or BR should be given to older patients and young, fit patients with IGHV-unmutated CLL.

Dr. Brown and Dr. Wierda reported financial ties to multiple pharmaceutical companies, including makers of CLL treatments.

[email protected]

SAN FRANCISCO – Should targeted agents replace chemoimmunotherapy (CIT) as first-line treatment for chronic lymphocytic leukemia (CLL)? A recent debate suggests there’s no consensus.

Jennifer Smith/MDedge News

William G. Wierda, MD, PhD, of The University of Texas MD Anderson Cancer Center in Houston, and Jennifer R. Brown, MD, PhD, of Dana-Farber Cancer Institute in Boston, debated the topic at the National Comprehensive Cancer Network Hematologic Malignancies Annual Congress.

Dr. Wierda argued that CLL patients should receive a BTK inhibitor or BCL2 inhibitor, with or without obinutuzumab, as first-line therapy because these targeted agents have been shown to provide better progression-free survival (PFS) than CIT, and the targeted therapies may prolong overall survival (OS) as well.

Dr. Brown countered that targeted agents don’t improve PFS for all CLL patients, improved PFS doesn’t always translate to improved OS, and targeted agents cost more than CIT.
 

No role for CIT as first-line treatment

“We have two approaches right now, with nonchemoimmunotherapy-based treatment,” Dr. Wierda said. “One approach, with small-molecule inhibitors, is to have a sustained and durable period of disease control, particularly with BTK inhibitors. The other strategy that has emerged is deep remissions with fixed-duration treatment with BCL2 small-molecule inhibitor-based therapy, which, I would argue, is better than being exposed to genotoxic chemoimmunotherapy.”

Dr. Wierda went on to explain that the BTK inhibitor ibrutinib has been shown to improve PFS, compared with CIT, in phase 3 trials.

In the iLLUMINATE trial, researchers compared ibrutinib plus obinutuzumab to chlorambucil plus obinutuzumab as first-line treatment in CLL. At a median follow-up of 31.3 months, the median PFS was not reached in the ibrutinib arm and was 19 months in the chlorambucil arm (P less than .0001; Lancet Oncol. 2019 Jan;20[1]:43-56).

In the A041202 study, researchers compared ibrutinib alone (Ib) or in combination with rituximab (Ib-R) to bendamustine plus rituximab (BR) in untreated, older patients with CLL. The 2-year PFS estimates were 74% in the BR arm, 87% in the Ib arm, and 88% in the Ib-R arm (P less than .001 for BR vs. Ib or Ib-R; N Engl J Med. 2018; 379:2517-28).

In the E1912 trial, researchers compared Ib-R to fludarabine, cyclophosphamide, and rituximab (FCR) in younger, untreated CLL patients. The 3-year PFS was 89.4% with Ib-R and 72.9% with FCR (P less than .001; N Engl J Med. 2019 Aug 1;381:432-43).

Dr. Wierda noted that the E1912 trial also showed superior OS with Ib-R. The 3-year OS rate was 98.8% with Ib-R and 91.5% with FCR (P less than .001). However, there was no significant difference in OS between the treatment arms in the A041202 trial or the iLLUMINATE trial.

“But I would argue that is, in part, because of short follow-up,” Dr. Wierda said. “The trials were all designed to look at progression-free survival, not overall survival. With longer follow-up, we may see differences in overall survival emerging.”

Dr. Wierda went on to say that fixed‐duration treatment with the BCL2 inhibitor venetoclax can improve PFS over CIT.

In the phase 3 CLL14 trial, researchers compared fixed-duration treatment with venetoclax plus obinutuzumab to chlorambucil plus obinutuzumab in previously untreated CLL patients with comorbidities. The estimated PFS at 2 years was 88.2% in the venetoclax group and 64.1% in the chlorambucil group (P less than .001; N Engl J Med. 2019; 380:2225-36).

“[There was] no difference in overall survival,” Dr. Wierda noted. “But, again, I would argue ... that follow-up is relatively limited. We may ultimately see a difference in overall survival.”

Based on these findings, Dr. Wierda made the following treatment recommendations:

• Any CLL patient with del(17p) or TP53 mutation, and older, unfit patients with unmutated IGHV should receive a BTK inhibitor, with or without obinutuzumab.
• All young, fit patients, and older, unfit patients with mutated IGHV should receive a BCL2 inhibitor plus obinutuzumab.

Dr. Wierda also noted that ibrutinib and venetoclax in combination have shown early promise for patients with previously untreated CLL (N Engl J Med. 2019; 380:2095-2103).
 

CIT still has a role as first-line treatment

Dr. Brown suggested that a PFS benefit may not be enough to recommend targeted agents over CIT. For one thing, the PFS benefit doesn’t apply to all patients, as the IGHV-mutated subgroup does equally well with CIT and targeted agents.

Jennifer Smith/MDedge News

In the IGHV-mutated group from the E1912 trial, the 3-year PFS was 88% for patients who received Ib-R and those who received FCR (N Engl J Med. 2019 Aug 1;381:432-43). In the A041202 study, the 2-year PFS among IGHV-mutated patients was 87% in the BR arm, 86% in the Ib arm, and 88% in the Ib-R arm (N Engl J Med. 2018; 379:2517-28).

In the CLL14 trial, PFS rates were similar among IGHV-mutated patients who received chlorambucil plus obinutuzumab and IGHV-mutated or unmutated patients who received venetoclax and obinutuzumab (N Engl J Med. 2019; 380:2225-36).

Dr. Brown also noted that the overall improvement in PFS observed with ibrutinib and venetoclax doesn’t always translate to improved OS.

In the A041202 study, there was no significant difference in OS between the Ib, Ib-R, and BR arms (N Engl J Med. 2018; 379:2517-28). There was no significant difference in OS between the ibrutinib and chlorambucil arms in the iLLUMINATE trial (Lancet Oncol. 2019 Jan;20[1]:43-56). And there was no significant difference in OS between the venetoclax and chlorambucil arms in the CLL14 trial (N Engl J Med. 2019; 380:2225-36).

However, in the RESONATE-2 trial, ibrutinib provided an OS benefit over chlorambucil. The 2-year OS was 95% and 84%, respectively (P = .0145; Haematologica. Sept 2018;103:1502-10). Dr. Brown said the OS advantage in this study was due to the “very poor comparator of chlorambucil and very limited crossover.”

As Dr. Wierda mentioned, the OS rate was higher with Ib-R than with FCR in the E1912 trial. The 3-year OS rate was 98.8% and 91.5%, respectively (P less than .001; N Engl J Med. 2019 Aug 1;381:432-43). Dr. Brown noted, however, that there were few deaths in this study, and many of them “were not clearly related to the disease or its treatment.”

Dr. Brown also pointed out that FCR has been shown to have curative potential in IGHV-mutated CLL in both the FCR300 trial (Blood. 2016 127:303-9) and the CLL8 trial (Blood. 2016 127:208-15).

Another factor to consider is the greater cost of targeted agents. One analysis suggested the per-patient lifetime cost of CLL treatment in the United States will increase from $147,000 to $604,000 as targeted therapies overtake CIT as first-line treatment (J Clin Oncol. 2017 Jan 10;35[2]:166-174).

“Given all of the above, chemoimmunotherapy is going to remain part of the treatment repertoire for CLL,” Dr. Brown said. “It’s our only known potential cure for the fit, mutated patients ... and can also result in prolonged treatment-free intervals for patients who are older. As we manage CLL as a chronic disease over a lifetime, we need to continue to have this in our armamentarium.”

Specifically, Dr. Brown said CIT is appropriate for patients who don’t have del(17p) or mutated TP53. FCR should be given to young, fit patients with IGHV-mutated CLL, and FCR or BR should be given to older patients and young, fit patients with IGHV-unmutated CLL.

Dr. Brown and Dr. Wierda reported financial ties to multiple pharmaceutical companies, including makers of CLL treatments.

[email protected]

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Women with early breast cancer and a high genetic risk for recurrence at a distant site achieved greater freedom from recurrence when treated with chemoendocrine therapy than with endocrine therapy alone in a study of 1,389 women with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer.

“The 21-gene recurrence score (RS) assay provides prognostic information for distant recurrence in hormone-receptor–positive, ERBB2-negative early breast cancer that is independent of clinicopathologic features and is also predictive of chemotherapy benefit when the RS is high,” wrote Joseph A. Sparano, MD, of Montefiore Medical Center, New York, and his colleagues. However, little is known about how this risk score applies to women with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer, they said.

In the study published in JAMA Oncology, they identified 1,389 women with a recurrence score of 26-100 (the definition of a high recurrence risk). The average age of the patients was 56 years, and 71% were postmenopausal.

In addition to receiving endocrine therapy, the women were randomized to no chemotherapy (89 patients) or one of several chemotherapy regimens including docetaxel/cyclophosphamide (589 patients), anthracycline without a taxane (334 patients), an anthracycline and taxane (244 patients), cyclophosphamide/methotrexate/5-fluorouracil (52 patients), and other regimens (81 patients). Among those treated with chemotherapy, overall survival (OS) at 5 years was 96% and estimated rates of freedom from recurrence of breast cancer at a distant site, and from a distant and/or local regional site, at 5 years were 93% and 91%, respectively. At 5 years, the estimated rate of invasive disease–free survival (IDFS) was 88%.

When broken down by chemotherapy regimen, 5-year rates of freedom from recurrence of breast cancer at a distant site ranged from 92.3% to 95.5%, except for 88.5% for the cyclophosphamide/methotrexate/5-fluorouracil (CMF) group; the rate was 92.6% for patients in the no-chemotherapy group.

The 5-year rates of IDFS ranged from 84% to 91.3% in the chemotherapy groups, compared with 79.7% in the no-chemotherapy group.

The expected rates of distant recurrence in the overall patient population if treated with endocrine therapy alone was 78.8% at 5 years and 65.4% at 9 years, the researchers said. Rates among the patients with an RS of 26-30 if treated with endocrine therapy alone were 89.6% at 5 years and 80.6% at 9 years; rates for those with an RS of 31-100 were 70.7% and 54% for 5 and 9 years, respectively.

The study findings were limited by several factors including a lack of randomization to endocrine therapy alone and the relatively short follow-up period, the researchers noted. However, strengths include the large sample size and high rate of compliance with chemotherapy.

The results support data from previous studies and “add to the evidence base supporting the use of the 21-gene RS assay to guide the use of adjuvant chemotherapy in patients with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer,” they concluded.

The study was supported in part by the National Cancer Institute, the Canadian Cancer Society Research Institute, the Breast Cancer Research Foundation, the Komen Foundation, and the Breast Cancer Research Stamp issued by the United States Postal Service. Dr. Sparano disclosed grants from the National Cancer Institute. Of the remaining authors, several disclosed receiving personal or speaker fees from the assay manufacturer, Genomic Health; one author received funding from the company during the study.

SOURCE: Sparano J et al. JAMA Oncol. 2019 Sep 30. doi: 10.1001/jamaoncol.2019.4794.

Women with early breast cancer and a high genetic risk for recurrence at a distant site achieved greater freedom from recurrence when treated with chemoendocrine therapy than with endocrine therapy alone in a study of 1,389 women with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer.

“The 21-gene recurrence score (RS) assay provides prognostic information for distant recurrence in hormone-receptor–positive, ERBB2-negative early breast cancer that is independent of clinicopathologic features and is also predictive of chemotherapy benefit when the RS is high,” wrote Joseph A. Sparano, MD, of Montefiore Medical Center, New York, and his colleagues. However, little is known about how this risk score applies to women with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer, they said.

In the study published in JAMA Oncology, they identified 1,389 women with a recurrence score of 26-100 (the definition of a high recurrence risk). The average age of the patients was 56 years, and 71% were postmenopausal.

In addition to receiving endocrine therapy, the women were randomized to no chemotherapy (89 patients) or one of several chemotherapy regimens including docetaxel/cyclophosphamide (589 patients), anthracycline without a taxane (334 patients), an anthracycline and taxane (244 patients), cyclophosphamide/methotrexate/5-fluorouracil (52 patients), and other regimens (81 patients). Among those treated with chemotherapy, overall survival (OS) at 5 years was 96% and estimated rates of freedom from recurrence of breast cancer at a distant site, and from a distant and/or local regional site, at 5 years were 93% and 91%, respectively. At 5 years, the estimated rate of invasive disease–free survival (IDFS) was 88%.

When broken down by chemotherapy regimen, 5-year rates of freedom from recurrence of breast cancer at a distant site ranged from 92.3% to 95.5%, except for 88.5% for the cyclophosphamide/methotrexate/5-fluorouracil (CMF) group; the rate was 92.6% for patients in the no-chemotherapy group.

The 5-year rates of IDFS ranged from 84% to 91.3% in the chemotherapy groups, compared with 79.7% in the no-chemotherapy group.

The expected rates of distant recurrence in the overall patient population if treated with endocrine therapy alone was 78.8% at 5 years and 65.4% at 9 years, the researchers said. Rates among the patients with an RS of 26-30 if treated with endocrine therapy alone were 89.6% at 5 years and 80.6% at 9 years; rates for those with an RS of 31-100 were 70.7% and 54% for 5 and 9 years, respectively.

The study findings were limited by several factors including a lack of randomization to endocrine therapy alone and the relatively short follow-up period, the researchers noted. However, strengths include the large sample size and high rate of compliance with chemotherapy.

The results support data from previous studies and “add to the evidence base supporting the use of the 21-gene RS assay to guide the use of adjuvant chemotherapy in patients with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer,” they concluded.

The study was supported in part by the National Cancer Institute, the Canadian Cancer Society Research Institute, the Breast Cancer Research Foundation, the Komen Foundation, and the Breast Cancer Research Stamp issued by the United States Postal Service. Dr. Sparano disclosed grants from the National Cancer Institute. Of the remaining authors, several disclosed receiving personal or speaker fees from the assay manufacturer, Genomic Health; one author received funding from the company during the study.

SOURCE: Sparano J et al. JAMA Oncol. 2019 Sep 30. doi: 10.1001/jamaoncol.2019.4794.

Women with early breast cancer and a high genetic risk for recurrence at a distant site achieved greater freedom from recurrence when treated with chemoendocrine therapy than with endocrine therapy alone in a study of 1,389 women with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer.

“The 21-gene recurrence score (RS) assay provides prognostic information for distant recurrence in hormone-receptor–positive, ERBB2-negative early breast cancer that is independent of clinicopathologic features and is also predictive of chemotherapy benefit when the RS is high,” wrote Joseph A. Sparano, MD, of Montefiore Medical Center, New York, and his colleagues. However, little is known about how this risk score applies to women with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer, they said.

In the study published in JAMA Oncology, they identified 1,389 women with a recurrence score of 26-100 (the definition of a high recurrence risk). The average age of the patients was 56 years, and 71% were postmenopausal.

In addition to receiving endocrine therapy, the women were randomized to no chemotherapy (89 patients) or one of several chemotherapy regimens including docetaxel/cyclophosphamide (589 patients), anthracycline without a taxane (334 patients), an anthracycline and taxane (244 patients), cyclophosphamide/methotrexate/5-fluorouracil (52 patients), and other regimens (81 patients). Among those treated with chemotherapy, overall survival (OS) at 5 years was 96% and estimated rates of freedom from recurrence of breast cancer at a distant site, and from a distant and/or local regional site, at 5 years were 93% and 91%, respectively. At 5 years, the estimated rate of invasive disease–free survival (IDFS) was 88%.

When broken down by chemotherapy regimen, 5-year rates of freedom from recurrence of breast cancer at a distant site ranged from 92.3% to 95.5%, except for 88.5% for the cyclophosphamide/methotrexate/5-fluorouracil (CMF) group; the rate was 92.6% for patients in the no-chemotherapy group.

The 5-year rates of IDFS ranged from 84% to 91.3% in the chemotherapy groups, compared with 79.7% in the no-chemotherapy group.

The expected rates of distant recurrence in the overall patient population if treated with endocrine therapy alone was 78.8% at 5 years and 65.4% at 9 years, the researchers said. Rates among the patients with an RS of 26-30 if treated with endocrine therapy alone were 89.6% at 5 years and 80.6% at 9 years; rates for those with an RS of 31-100 were 70.7% and 54% for 5 and 9 years, respectively.

The study findings were limited by several factors including a lack of randomization to endocrine therapy alone and the relatively short follow-up period, the researchers noted. However, strengths include the large sample size and high rate of compliance with chemotherapy.

The results support data from previous studies and “add to the evidence base supporting the use of the 21-gene RS assay to guide the use of adjuvant chemotherapy in patients with hormone receptor–positive, ERBB2-negative, axillary node–negative breast cancer,” they concluded.

The study was supported in part by the National Cancer Institute, the Canadian Cancer Society Research Institute, the Breast Cancer Research Foundation, the Komen Foundation, and the Breast Cancer Research Stamp issued by the United States Postal Service. Dr. Sparano disclosed grants from the National Cancer Institute. Of the remaining authors, several disclosed receiving personal or speaker fees from the assay manufacturer, Genomic Health; one author received funding from the company during the study.

SOURCE: Sparano J et al. JAMA Oncol. 2019 Sep 30. doi: 10.1001/jamaoncol.2019.4794.

BARCELONA – In patients with advanced, treatment-naive non–small cell lung cancer (NSCLC), therapy with osimertinib (Tagrisso) is associated with a significant and clinically meaningful improvement in overall survival, compared with other agents targeted against NSCLC with epidermal growth factor–receptor (EGFR) mutations, investigators for the FLAURA trial reported.

Neil Osterweil/MDedge News

After median follow-up ranging from 27 to 35.8 months, the median overall survival was 38.6 months for patients randomized to osimertinib, compared with 31.8 months for patients assigned to either of two comparator tyrosine kinase inhibitors (TKIs), gefitinib (Iressa) or erlotinib (Tarceva).

The hazard ratio for death with osimertinib was 0.799 (P = .0462), reported Suresh Ramalingam, MD, director of the lung cancer program at Winship Cancer Institute of Emory University, Atlanta.

“I’m excited that the new milestone accomplished with osimertinib in this trial will serve as the platform to build on in our efforts to improve the lives of patients with lung cancer,” he said at the European Society for Medical Oncology Congress.

Osimertinib is the first TKI to show improvement in overall survival over another TKI in the treatment of advanced stage cancers, he noted.

Overall survival was a secondary endpoint of the FLAURA trial. As previously reported, FLAURA met its primary endpoint of improvement in progression-free survival (PFS) in an interim analysis presented at ESMO 2017. In that analysis, osimertinib cut the risk of disease progression by 54%, compared with gefitinib or erlotinib.

Among 279 patients with EGFR-mutated locally advanced or metastatic NSCLC treated with osimertinib, the median PFS was 18.9 months, compared with 10.2 months for 277 patients treated with the standard of care, which translated into a HR of 0.46 (P less than .0001).

The FLAURA results supported Food and Drug Administration approval of osimertinib in April 2018 for first-line treatment of patients with metastatic NSCLC with EGFR mutations as detected by an FDA-approved test.

The current overall survival analysis, although not powered to show differences among patient subgroups, showed trends favoring osimertinib over a comparator TKI among both men and women, older and younger patients, patients with central nervous system metastases at trial entry, and patients with the EGFR exon 19 deletion at randomization.

The 31.8 month median overall survival for the control (comparator-TKI) arm is among the highest reported for patients with EGFR-mutated NSCLC, Dr. Ramalingam noted.

“That is because a lot of patients crossed over from the control group to receive osimertinib on progression,” he said, adding that the magnitude of benefit from osimertinib was greater among non-Asian patients, compared with Asians.
 

FLAURA details

In the phase 3 FLAURA trial, investigators stratified patients with previously untreated NSCLC positive for EGFR resistance mutations according to mutation status (exon 19 deletion or the L858R amino acid substitution in exon 21) and race (Asian or non-Asian).

Patients were randomly assigned to treatment with either oral osimertinib 80 mg daily or an EGFR TKI, either oral gefitinib 250 mg or erlotinib 150 mg daily.

The patients were assessed by Response Evaluation Criteria in Solid Tumors (RECIST) every 6 weeks until objective disease progression.

Patients assigned to the standard-of-care arm who had central confirmation of progression and T790M positivity were allowed to cross over to open-label osimertinib.

PFS, the primary endpoint, was also significantly better with osimertinib than with either of the comparator TKIs in patients with and without central nervous system metastases at study entry (HR, 0.47; P = .0009 for patients with CNS metastases; HR, 0.46; P less than .0001 for patients with no CNS metastases).

Neil Osterweil/MDedge News

“For clinicians, for patients, and also for our health authorities, the results in terms of overall survival are really relevant, and this is why this study is so important, knowing this secondary endpoint from a statistical point of view. The study is statistically significant and clinically relevant,” commented Pilar Garrido, MD, from the department of medical oncology, Hospital Universitario Ramón y Cajal in Madrid, the invited discussant at a briefing where Dr. Ramalingam outlined the study findings prior to his presentation of the data in a symposium.

“What’s the future of EGFR mutant lung cancer? Well, I think we should be done with single-agent EGFR-TKI comparisons: We have a clear agent that’s associated with an improvement in survival. I think our focus needs to shift to building on or adding to osimertinib,” commented Pasi A Jänne, MD, PhD, director of the Lowe Center for Thoracic Oncology at Dana-Farber Cancer Institute in Boston, the invited discussant at the symposium.

He said that the challenge for clinicians will be to identify high- and low-risk EGFR-mutant NSCLC, and to determine which patients could be treated with a single agent, and which may require a combination therapy approach.

FLAURA was sponsored by AstraZeneca. Dr. Ramalingam disclosed honoraria, an advisory or consulting role, and research funding from that company and others. Dr. Garrido disclosed a speaker and advisory role for AstraZeneca and others. Dr. Jänne disclosed prior consulting for AstraZeneca.

SOURCE: Ramalingam S et al. ESMO 2019. Abstract LBA5_PR.

BARCELONA – In patients with advanced, treatment-naive non–small cell lung cancer (NSCLC), therapy with osimertinib (Tagrisso) is associated with a significant and clinically meaningful improvement in overall survival, compared with other agents targeted against NSCLC with epidermal growth factor–receptor (EGFR) mutations, investigators for the FLAURA trial reported.

Neil Osterweil/MDedge News

After median follow-up ranging from 27 to 35.8 months, the median overall survival was 38.6 months for patients randomized to osimertinib, compared with 31.8 months for patients assigned to either of two comparator tyrosine kinase inhibitors (TKIs), gefitinib (Iressa) or erlotinib (Tarceva).

The hazard ratio for death with osimertinib was 0.799 (P = .0462), reported Suresh Ramalingam, MD, director of the lung cancer program at Winship Cancer Institute of Emory University, Atlanta.

“I’m excited that the new milestone accomplished with osimertinib in this trial will serve as the platform to build on in our efforts to improve the lives of patients with lung cancer,” he said at the European Society for Medical Oncology Congress.

Osimertinib is the first TKI to show improvement in overall survival over another TKI in the treatment of advanced stage cancers, he noted.

Overall survival was a secondary endpoint of the FLAURA trial. As previously reported, FLAURA met its primary endpoint of improvement in progression-free survival (PFS) in an interim analysis presented at ESMO 2017. In that analysis, osimertinib cut the risk of disease progression by 54%, compared with gefitinib or erlotinib.

Among 279 patients with EGFR-mutated locally advanced or metastatic NSCLC treated with osimertinib, the median PFS was 18.9 months, compared with 10.2 months for 277 patients treated with the standard of care, which translated into a HR of 0.46 (P less than .0001).

The FLAURA results supported Food and Drug Administration approval of osimertinib in April 2018 for first-line treatment of patients with metastatic NSCLC with EGFR mutations as detected by an FDA-approved test.

The current overall survival analysis, although not powered to show differences among patient subgroups, showed trends favoring osimertinib over a comparator TKI among both men and women, older and younger patients, patients with central nervous system metastases at trial entry, and patients with the EGFR exon 19 deletion at randomization.

The 31.8 month median overall survival for the control (comparator-TKI) arm is among the highest reported for patients with EGFR-mutated NSCLC, Dr. Ramalingam noted.

“That is because a lot of patients crossed over from the control group to receive osimertinib on progression,” he said, adding that the magnitude of benefit from osimertinib was greater among non-Asian patients, compared with Asians.
 

FLAURA details

In the phase 3 FLAURA trial, investigators stratified patients with previously untreated NSCLC positive for EGFR resistance mutations according to mutation status (exon 19 deletion or the L858R amino acid substitution in exon 21) and race (Asian or non-Asian).

Patients were randomly assigned to treatment with either oral osimertinib 80 mg daily or an EGFR TKI, either oral gefitinib 250 mg or erlotinib 150 mg daily.

The patients were assessed by Response Evaluation Criteria in Solid Tumors (RECIST) every 6 weeks until objective disease progression.

Patients assigned to the standard-of-care arm who had central confirmation of progression and T790M positivity were allowed to cross over to open-label osimertinib.

PFS, the primary endpoint, was also significantly better with osimertinib than with either of the comparator TKIs in patients with and without central nervous system metastases at study entry (HR, 0.47; P = .0009 for patients with CNS metastases; HR, 0.46; P less than .0001 for patients with no CNS metastases).

Neil Osterweil/MDedge News

“For clinicians, for patients, and also for our health authorities, the results in terms of overall survival are really relevant, and this is why this study is so important, knowing this secondary endpoint from a statistical point of view. The study is statistically significant and clinically relevant,” commented Pilar Garrido, MD, from the department of medical oncology, Hospital Universitario Ramón y Cajal in Madrid, the invited discussant at a briefing where Dr. Ramalingam outlined the study findings prior to his presentation of the data in a symposium.

“What’s the future of EGFR mutant lung cancer? Well, I think we should be done with single-agent EGFR-TKI comparisons: We have a clear agent that’s associated with an improvement in survival. I think our focus needs to shift to building on or adding to osimertinib,” commented Pasi A Jänne, MD, PhD, director of the Lowe Center for Thoracic Oncology at Dana-Farber Cancer Institute in Boston, the invited discussant at the symposium.

He said that the challenge for clinicians will be to identify high- and low-risk EGFR-mutant NSCLC, and to determine which patients could be treated with a single agent, and which may require a combination therapy approach.

FLAURA was sponsored by AstraZeneca. Dr. Ramalingam disclosed honoraria, an advisory or consulting role, and research funding from that company and others. Dr. Garrido disclosed a speaker and advisory role for AstraZeneca and others. Dr. Jänne disclosed prior consulting for AstraZeneca.

SOURCE: Ramalingam S et al. ESMO 2019. Abstract LBA5_PR.

BARCELONA – In patients with advanced, treatment-naive non–small cell lung cancer (NSCLC), therapy with osimertinib (Tagrisso) is associated with a significant and clinically meaningful improvement in overall survival, compared with other agents targeted against NSCLC with epidermal growth factor–receptor (EGFR) mutations, investigators for the FLAURA trial reported.

Neil Osterweil/MDedge News

After median follow-up ranging from 27 to 35.8 months, the median overall survival was 38.6 months for patients randomized to osimertinib, compared with 31.8 months for patients assigned to either of two comparator tyrosine kinase inhibitors (TKIs), gefitinib (Iressa) or erlotinib (Tarceva).

The hazard ratio for death with osimertinib was 0.799 (P = .0462), reported Suresh Ramalingam, MD, director of the lung cancer program at Winship Cancer Institute of Emory University, Atlanta.

“I’m excited that the new milestone accomplished with osimertinib in this trial will serve as the platform to build on in our efforts to improve the lives of patients with lung cancer,” he said at the European Society for Medical Oncology Congress.

Osimertinib is the first TKI to show improvement in overall survival over another TKI in the treatment of advanced stage cancers, he noted.

Overall survival was a secondary endpoint of the FLAURA trial. As previously reported, FLAURA met its primary endpoint of improvement in progression-free survival (PFS) in an interim analysis presented at ESMO 2017. In that analysis, osimertinib cut the risk of disease progression by 54%, compared with gefitinib or erlotinib.

Among 279 patients with EGFR-mutated locally advanced or metastatic NSCLC treated with osimertinib, the median PFS was 18.9 months, compared with 10.2 months for 277 patients treated with the standard of care, which translated into a HR of 0.46 (P less than .0001).

The FLAURA results supported Food and Drug Administration approval of osimertinib in April 2018 for first-line treatment of patients with metastatic NSCLC with EGFR mutations as detected by an FDA-approved test.

The current overall survival analysis, although not powered to show differences among patient subgroups, showed trends favoring osimertinib over a comparator TKI among both men and women, older and younger patients, patients with central nervous system metastases at trial entry, and patients with the EGFR exon 19 deletion at randomization.

The 31.8 month median overall survival for the control (comparator-TKI) arm is among the highest reported for patients with EGFR-mutated NSCLC, Dr. Ramalingam noted.

“That is because a lot of patients crossed over from the control group to receive osimertinib on progression,” he said, adding that the magnitude of benefit from osimertinib was greater among non-Asian patients, compared with Asians.
 

FLAURA details

In the phase 3 FLAURA trial, investigators stratified patients with previously untreated NSCLC positive for EGFR resistance mutations according to mutation status (exon 19 deletion or the L858R amino acid substitution in exon 21) and race (Asian or non-Asian).

Patients were randomly assigned to treatment with either oral osimertinib 80 mg daily or an EGFR TKI, either oral gefitinib 250 mg or erlotinib 150 mg daily.

The patients were assessed by Response Evaluation Criteria in Solid Tumors (RECIST) every 6 weeks until objective disease progression.

Patients assigned to the standard-of-care arm who had central confirmation of progression and T790M positivity were allowed to cross over to open-label osimertinib.

PFS, the primary endpoint, was also significantly better with osimertinib than with either of the comparator TKIs in patients with and without central nervous system metastases at study entry (HR, 0.47; P = .0009 for patients with CNS metastases; HR, 0.46; P less than .0001 for patients with no CNS metastases).

Neil Osterweil/MDedge News

“For clinicians, for patients, and also for our health authorities, the results in terms of overall survival are really relevant, and this is why this study is so important, knowing this secondary endpoint from a statistical point of view. The study is statistically significant and clinically relevant,” commented Pilar Garrido, MD, from the department of medical oncology, Hospital Universitario Ramón y Cajal in Madrid, the invited discussant at a briefing where Dr. Ramalingam outlined the study findings prior to his presentation of the data in a symposium.

“What’s the future of EGFR mutant lung cancer? Well, I think we should be done with single-agent EGFR-TKI comparisons: We have a clear agent that’s associated with an improvement in survival. I think our focus needs to shift to building on or adding to osimertinib,” commented Pasi A Jänne, MD, PhD, director of the Lowe Center for Thoracic Oncology at Dana-Farber Cancer Institute in Boston, the invited discussant at the symposium.

He said that the challenge for clinicians will be to identify high- and low-risk EGFR-mutant NSCLC, and to determine which patients could be treated with a single agent, and which may require a combination therapy approach.

FLAURA was sponsored by AstraZeneca. Dr. Ramalingam disclosed honoraria, an advisory or consulting role, and research funding from that company and others. Dr. Garrido disclosed a speaker and advisory role for AstraZeneca and others. Dr. Jänne disclosed prior consulting for AstraZeneca.

SOURCE: Ramalingam S et al. ESMO 2019. Abstract LBA5_PR.

MINNEAPOLIS -- Oncologist Mark Klein, MD, may have just stepped down as president of the Association of VA Hematology/Oncology (AVAHO), but his main legacy—a foundation that AVAHO can call its own—is set in stone.

Over the past year, Klein has guided AVAHO as it leveraged its remarkable growth in recent years into the landmark creation of a foundation devoted to research. “We want to provide funds to researchers and support access to clinical trials for patients within the VA,” said Klein in an interview after he stepped down as association president at the 2019 AVAHO annual meeting.

Dr. Klein, who works for the Minneapolis VA Healthcare System and University of Minnesota in Minneapolis said the foundation is being seeded with $250,000. One goal is to use the foundation to support unique research projects that may not otherwise draw funding, he said.

For example, he said, the foundation could fund a research project by dietitians into severe weight loss in cancer. Or it could support a study by speech pathologists into swallowing in cancer patients.

In addition, he said, the foundation will focus on providing grants to support junior faculty, including researchers who aren’t MDs. And its funds will be used to boost access to clinical trials in cancer.

Klein said he has also focused on strategic planning and developing partnerships with industry and the leadership of both the VA and the National Cancer Institute. “We’re working to come up with unique ways to get people thinking about the barriers to clinical trials and providing better access for veterans.”

He is especially proud of AVAHO’s partnership with National Association of Veterans’ Research and Education Foundations, which includes partial support of a program manager position.

On the corporate front, he said, “we’re going to start offering corporate memberships so that we can form more industry relationships. That’s another new change and a step in our growth as we work to help more veterans and make a bigger difference.”

MINNEAPOLIS -- Oncologist Mark Klein, MD, may have just stepped down as president of the Association of VA Hematology/Oncology (AVAHO), but his main legacy—a foundation that AVAHO can call its own—is set in stone.

Over the past year, Klein has guided AVAHO as it leveraged its remarkable growth in recent years into the landmark creation of a foundation devoted to research. “We want to provide funds to researchers and support access to clinical trials for patients within the VA,” said Klein in an interview after he stepped down as association president at the 2019 AVAHO annual meeting.

Dr. Klein, who works for the Minneapolis VA Healthcare System and University of Minnesota in Minneapolis said the foundation is being seeded with $250,000. One goal is to use the foundation to support unique research projects that may not otherwise draw funding, he said.

For example, he said, the foundation could fund a research project by dietitians into severe weight loss in cancer. Or it could support a study by speech pathologists into swallowing in cancer patients.

In addition, he said, the foundation will focus on providing grants to support junior faculty, including researchers who aren’t MDs. And its funds will be used to boost access to clinical trials in cancer.

Klein said he has also focused on strategic planning and developing partnerships with industry and the leadership of both the VA and the National Cancer Institute. “We’re working to come up with unique ways to get people thinking about the barriers to clinical trials and providing better access for veterans.”

He is especially proud of AVAHO’s partnership with National Association of Veterans’ Research and Education Foundations, which includes partial support of a program manager position.

On the corporate front, he said, “we’re going to start offering corporate memberships so that we can form more industry relationships. That’s another new change and a step in our growth as we work to help more veterans and make a bigger difference.”

MINNEAPOLIS -- Oncologist Mark Klein, MD, may have just stepped down as president of the Association of VA Hematology/Oncology (AVAHO), but his main legacy—a foundation that AVAHO can call its own—is set in stone.

Over the past year, Klein has guided AVAHO as it leveraged its remarkable growth in recent years into the landmark creation of a foundation devoted to research. “We want to provide funds to researchers and support access to clinical trials for patients within the VA,” said Klein in an interview after he stepped down as association president at the 2019 AVAHO annual meeting.

Dr. Klein, who works for the Minneapolis VA Healthcare System and University of Minnesota in Minneapolis said the foundation is being seeded with $250,000. One goal is to use the foundation to support unique research projects that may not otherwise draw funding, he said.

For example, he said, the foundation could fund a research project by dietitians into severe weight loss in cancer. Or it could support a study by speech pathologists into swallowing in cancer patients.

In addition, he said, the foundation will focus on providing grants to support junior faculty, including researchers who aren’t MDs. And its funds will be used to boost access to clinical trials in cancer.

Klein said he has also focused on strategic planning and developing partnerships with industry and the leadership of both the VA and the National Cancer Institute. “We’re working to come up with unique ways to get people thinking about the barriers to clinical trials and providing better access for veterans.”

He is especially proud of AVAHO’s partnership with National Association of Veterans’ Research and Education Foundations, which includes partial support of a program manager position.

On the corporate front, he said, “we’re going to start offering corporate memberships so that we can form more industry relationships. That’s another new change and a step in our growth as we work to help more veterans and make a bigger difference.”

Sex predicts mortality after a breast cancer diagnosis, with male patients about one-fifth more likely than female counterparts to have died by the 5-year mark, finds a cohort study of more than 1.8 million patients. Clinical characteristics and undertreatment explained much, but not all, of this excess mortality.

“Studies have indicated that male patients with breast cancer had worse overall survival than their female counterparts, including those with early-stage disease, although results have been inconsistent,” the investigators note. However, “few studies have systematically investigated the factors associated with mortality in male patients with breast cancer or assessed whether breast cancer prognosis for men is congruent with that for women, accounting for the differences in clinical characteristics and treatment.”

Senior investigator Xiao-Ou Shu, MD, PhD, of the Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tenn., and coinvestigators conducted a nationwide, registry-based cohort study using the National Cancer Database to identify patients receiving a breast cancer diagnosis during 2004-2014. Analyses were based on 16,025 male patients (mean age, 63.3 years) having a median follow-up of 54.0 months and 1,800,708 female patients (mean age, 59.9 years) having a median follow-up of 60.5 months.

Results reported in JAMA Oncology showed that men had higher mortality across all stages (P less than .001 for each). Male patients also had poorer relative overall survival (45.8% vs. 60.4%, P less than .001), 3-year survival (86.4% vs. 91.7%, P less than .001), and 5-year survival (77.6% vs. 86.4%, P less than .001).

Age, clinical factors (tumor size; nodal status; stage, ER, PR, and HER2 statuses; histologic type; grade; lymphovascular invasion; OncotypeDX Breast Recurrence Score; and Charlson/Deyo score), and treatment factors (surgical procedure, chemotherapy, endocrine therapy, radiation therapy, and immunotherapy) collectively explained 63.3% of the excess mortality rate for male patients. They explained fully 66.0% of the excess mortality in the first 3 years after diagnosis, including 30.5% and 13.6% of that among patients with stage I and stage II disease, respectively.

However, even after adjustment for these factors plus race/ethnicity and access to care, men still had significantly higher risks of overall mortality (adjusted hazard ratio, 1.19), 3-year mortality (adjusted hazard ratio, 1.15), and 5-year mortality (adjusted hazard ratio, 1.19).

The database used did not contain information on causes of death or on cancer recurrence or progression events, precluding analyses of disease-free survival.

“Future research should focus on why and how clinical characteristics, as well as biological features, may have different implications for the survival of male and female patients with breast cancer,” Dr. Shu and coinvestigators recommended. “Additional factors, particularly compliance to treatment, biological attributes, and lifestyle factors (e.g., smoking, drinking, and obesity), should be assessed to help in developing treatments tailored for men, which would mitigate this sex-based disparity.”

Dr. Shu disclosed no relevant conflicts of interest. One author was funded by the program of the China Scholarship Council.

SOURCE: Wang F et al. JAMA Oncol. 2019 Sep 19. doi: 10.1001/jamaoncol.2019.2803.

Sex predicts mortality after a breast cancer diagnosis, with male patients about one-fifth more likely than female counterparts to have died by the 5-year mark, finds a cohort study of more than 1.8 million patients. Clinical characteristics and undertreatment explained much, but not all, of this excess mortality.

“Studies have indicated that male patients with breast cancer had worse overall survival than their female counterparts, including those with early-stage disease, although results have been inconsistent,” the investigators note. However, “few studies have systematically investigated the factors associated with mortality in male patients with breast cancer or assessed whether breast cancer prognosis for men is congruent with that for women, accounting for the differences in clinical characteristics and treatment.”

Senior investigator Xiao-Ou Shu, MD, PhD, of the Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tenn., and coinvestigators conducted a nationwide, registry-based cohort study using the National Cancer Database to identify patients receiving a breast cancer diagnosis during 2004-2014. Analyses were based on 16,025 male patients (mean age, 63.3 years) having a median follow-up of 54.0 months and 1,800,708 female patients (mean age, 59.9 years) having a median follow-up of 60.5 months.

Results reported in JAMA Oncology showed that men had higher mortality across all stages (P less than .001 for each). Male patients also had poorer relative overall survival (45.8% vs. 60.4%, P less than .001), 3-year survival (86.4% vs. 91.7%, P less than .001), and 5-year survival (77.6% vs. 86.4%, P less than .001).

Age, clinical factors (tumor size; nodal status; stage, ER, PR, and HER2 statuses; histologic type; grade; lymphovascular invasion; OncotypeDX Breast Recurrence Score; and Charlson/Deyo score), and treatment factors (surgical procedure, chemotherapy, endocrine therapy, radiation therapy, and immunotherapy) collectively explained 63.3% of the excess mortality rate for male patients. They explained fully 66.0% of the excess mortality in the first 3 years after diagnosis, including 30.5% and 13.6% of that among patients with stage I and stage II disease, respectively.

However, even after adjustment for these factors plus race/ethnicity and access to care, men still had significantly higher risks of overall mortality (adjusted hazard ratio, 1.19), 3-year mortality (adjusted hazard ratio, 1.15), and 5-year mortality (adjusted hazard ratio, 1.19).

The database used did not contain information on causes of death or on cancer recurrence or progression events, precluding analyses of disease-free survival.

“Future research should focus on why and how clinical characteristics, as well as biological features, may have different implications for the survival of male and female patients with breast cancer,” Dr. Shu and coinvestigators recommended. “Additional factors, particularly compliance to treatment, biological attributes, and lifestyle factors (e.g., smoking, drinking, and obesity), should be assessed to help in developing treatments tailored for men, which would mitigate this sex-based disparity.”

Dr. Shu disclosed no relevant conflicts of interest. One author was funded by the program of the China Scholarship Council.

SOURCE: Wang F et al. JAMA Oncol. 2019 Sep 19. doi: 10.1001/jamaoncol.2019.2803.

Sex predicts mortality after a breast cancer diagnosis, with male patients about one-fifth more likely than female counterparts to have died by the 5-year mark, finds a cohort study of more than 1.8 million patients. Clinical characteristics and undertreatment explained much, but not all, of this excess mortality.

“Studies have indicated that male patients with breast cancer had worse overall survival than their female counterparts, including those with early-stage disease, although results have been inconsistent,” the investigators note. However, “few studies have systematically investigated the factors associated with mortality in male patients with breast cancer or assessed whether breast cancer prognosis for men is congruent with that for women, accounting for the differences in clinical characteristics and treatment.”

Senior investigator Xiao-Ou Shu, MD, PhD, of the Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tenn., and coinvestigators conducted a nationwide, registry-based cohort study using the National Cancer Database to identify patients receiving a breast cancer diagnosis during 2004-2014. Analyses were based on 16,025 male patients (mean age, 63.3 years) having a median follow-up of 54.0 months and 1,800,708 female patients (mean age, 59.9 years) having a median follow-up of 60.5 months.

Results reported in JAMA Oncology showed that men had higher mortality across all stages (P less than .001 for each). Male patients also had poorer relative overall survival (45.8% vs. 60.4%, P less than .001), 3-year survival (86.4% vs. 91.7%, P less than .001), and 5-year survival (77.6% vs. 86.4%, P less than .001).

Age, clinical factors (tumor size; nodal status; stage, ER, PR, and HER2 statuses; histologic type; grade; lymphovascular invasion; OncotypeDX Breast Recurrence Score; and Charlson/Deyo score), and treatment factors (surgical procedure, chemotherapy, endocrine therapy, radiation therapy, and immunotherapy) collectively explained 63.3% of the excess mortality rate for male patients. They explained fully 66.0% of the excess mortality in the first 3 years after diagnosis, including 30.5% and 13.6% of that among patients with stage I and stage II disease, respectively.

However, even after adjustment for these factors plus race/ethnicity and access to care, men still had significantly higher risks of overall mortality (adjusted hazard ratio, 1.19), 3-year mortality (adjusted hazard ratio, 1.15), and 5-year mortality (adjusted hazard ratio, 1.19).

The database used did not contain information on causes of death or on cancer recurrence or progression events, precluding analyses of disease-free survival.

“Future research should focus on why and how clinical characteristics, as well as biological features, may have different implications for the survival of male and female patients with breast cancer,” Dr. Shu and coinvestigators recommended. “Additional factors, particularly compliance to treatment, biological attributes, and lifestyle factors (e.g., smoking, drinking, and obesity), should be assessed to help in developing treatments tailored for men, which would mitigate this sex-based disparity.”

Dr. Shu disclosed no relevant conflicts of interest. One author was funded by the program of the China Scholarship Council.

SOURCE: Wang F et al. JAMA Oncol. 2019 Sep 19. doi: 10.1001/jamaoncol.2019.2803.

MINNEAPOLIS -- When William “Bill” Wachsman, MD, PhD, joined the executive board of the Association of VA Hematology/Oncology earlier this decade, the organization revolved around its annual meeting. Now, AVAHO is expanding its horizons, and Dr. Wachsman plans to push for a wider focus and greater impact as its new president.

“We’re a group of like-minded individuals who came together about 15 years ago and said we want to take better care of our patients, coordinate our services, and better educate ourselves,” said Dr. Wachsman, a hematologist/oncologist with US Department of Veterans Affairs (VA) San Diego Health Care System, University of California San Diego School of Medicine, and Moores Cancer Center. “We’re still dedicated to this mission. Moving forward, I want to improve educational opportunities, encourage our interest groups to develop initiatives, and utilize our foundation to support medical professionals and improve patient care within the VA.”

Dr. Wachsman took over as AVAHO’s president on the last day of the organization’s annual meeting in Minneapolis. He replaces immediate past president Mark Klein, MD, and will serve for 1 year.

According to Dr. Wachsman, AVAHO is unique among cancer/hematology associations because it’s not limited to physicians. “Everyone who’s involved with the care of patients with hematologic or oncologic disease can be involved. You don’t need to be an employee of the VA.”

Indeed, AVAHO’s approximately 800 members include medical oncologists and hematologists, surgical oncologists, radiation oncologists, pharmacists, nurses, nurse practitioners, advanced practice registered nurses, physician assistants, social workers, cancer registrars, and other allied health professionals.

AVAHO is also unique because it’s not a VA organization. “It’s an association of people are interested in better care for patients at the VA,” Dr. Wachsman said.

Over the next year, Dr. Wachsman hopes to form a community advisory board “that can not only give us advice, but reach out to other associations in the VA and in oncology to spread the word about what we’re doing.” Other forms of outreach can help AVAHO gain influence among policymakers, he said.

As for AVAHO’s foundation, he hopes to bring in funding through grants to support fellowship awards and to help VA sites around the nation develop infrastructure to support clinical trials.

On another national level, he said, AVAHO can improve its relationship with the VA with a goal of promoting honest and productive communication that goes both ways. “You have to get to know each other,” he said, “before you jump into the same pool and begin to swim.”

MINNEAPOLIS -- When William “Bill” Wachsman, MD, PhD, joined the executive board of the Association of VA Hematology/Oncology earlier this decade, the organization revolved around its annual meeting. Now, AVAHO is expanding its horizons, and Dr. Wachsman plans to push for a wider focus and greater impact as its new president.

“We’re a group of like-minded individuals who came together about 15 years ago and said we want to take better care of our patients, coordinate our services, and better educate ourselves,” said Dr. Wachsman, a hematologist/oncologist with US Department of Veterans Affairs (VA) San Diego Health Care System, University of California San Diego School of Medicine, and Moores Cancer Center. “We’re still dedicated to this mission. Moving forward, I want to improve educational opportunities, encourage our interest groups to develop initiatives, and utilize our foundation to support medical professionals and improve patient care within the VA.”

Dr. Wachsman took over as AVAHO’s president on the last day of the organization’s annual meeting in Minneapolis. He replaces immediate past president Mark Klein, MD, and will serve for 1 year.

According to Dr. Wachsman, AVAHO is unique among cancer/hematology associations because it’s not limited to physicians. “Everyone who’s involved with the care of patients with hematologic or oncologic disease can be involved. You don’t need to be an employee of the VA.”

Indeed, AVAHO’s approximately 800 members include medical oncologists and hematologists, surgical oncologists, radiation oncologists, pharmacists, nurses, nurse practitioners, advanced practice registered nurses, physician assistants, social workers, cancer registrars, and other allied health professionals.

AVAHO is also unique because it’s not a VA organization. “It’s an association of people are interested in better care for patients at the VA,” Dr. Wachsman said.

Over the next year, Dr. Wachsman hopes to form a community advisory board “that can not only give us advice, but reach out to other associations in the VA and in oncology to spread the word about what we’re doing.” Other forms of outreach can help AVAHO gain influence among policymakers, he said.

As for AVAHO’s foundation, he hopes to bring in funding through grants to support fellowship awards and to help VA sites around the nation develop infrastructure to support clinical trials.

On another national level, he said, AVAHO can improve its relationship with the VA with a goal of promoting honest and productive communication that goes both ways. “You have to get to know each other,” he said, “before you jump into the same pool and begin to swim.”

MINNEAPOLIS -- When William “Bill” Wachsman, MD, PhD, joined the executive board of the Association of VA Hematology/Oncology earlier this decade, the organization revolved around its annual meeting. Now, AVAHO is expanding its horizons, and Dr. Wachsman plans to push for a wider focus and greater impact as its new president.

“We’re a group of like-minded individuals who came together about 15 years ago and said we want to take better care of our patients, coordinate our services, and better educate ourselves,” said Dr. Wachsman, a hematologist/oncologist with US Department of Veterans Affairs (VA) San Diego Health Care System, University of California San Diego School of Medicine, and Moores Cancer Center. “We’re still dedicated to this mission. Moving forward, I want to improve educational opportunities, encourage our interest groups to develop initiatives, and utilize our foundation to support medical professionals and improve patient care within the VA.”

Dr. Wachsman took over as AVAHO’s president on the last day of the organization’s annual meeting in Minneapolis. He replaces immediate past president Mark Klein, MD, and will serve for 1 year.

According to Dr. Wachsman, AVAHO is unique among cancer/hematology associations because it’s not limited to physicians. “Everyone who’s involved with the care of patients with hematologic or oncologic disease can be involved. You don’t need to be an employee of the VA.”

Indeed, AVAHO’s approximately 800 members include medical oncologists and hematologists, surgical oncologists, radiation oncologists, pharmacists, nurses, nurse practitioners, advanced practice registered nurses, physician assistants, social workers, cancer registrars, and other allied health professionals.

AVAHO is also unique because it’s not a VA organization. “It’s an association of people are interested in better care for patients at the VA,” Dr. Wachsman said.

Over the next year, Dr. Wachsman hopes to form a community advisory board “that can not only give us advice, but reach out to other associations in the VA and in oncology to spread the word about what we’re doing.” Other forms of outreach can help AVAHO gain influence among policymakers, he said.

As for AVAHO’s foundation, he hopes to bring in funding through grants to support fellowship awards and to help VA sites around the nation develop infrastructure to support clinical trials.

On another national level, he said, AVAHO can improve its relationship with the VA with a goal of promoting honest and productive communication that goes both ways. “You have to get to know each other,” he said, “before you jump into the same pool and begin to swim.”

MINNEAPOLIS -- Picture this: A patient with cancer wants to get better and needs your help. But he or she refuses to hear the prognosis or understand the treatment options. The patient, in essence, has embraced a personal don’t-ask, don’t-tell policy.

What should you do as a medical professional? Get help from a psychologist and consider the ethics of the situation, advised a VA psychologist in a presentation at the September 2019 annual meeting of the Association of VA Hematology/Oncology.

Alyssa Ford, PhD, psychosocial oncology coordinator at VA Pittsburgh Healthcare System in Pennsylvania, said she has faced this situation. “We didn’t know the staging yet, but the veteran did not want to know about their prognosis or the treatment options,” she recalled. “They just wanted to fight this cancer.”

At issue in this case, she said, is this question: “What do we do when a patient opts out about receiving sufficient information to make an informed choice?”

As she explained, the key is to understand the person’s capacity—the ability to make an informed decision. “It can be assessed by any licensed health care provider who understands the components of capacity and is able to assess them.”

Ford evaluates a patient’s capacity by analyzing whether he or she can perform 4 tasks: Make decisions, live independently, manage finances, and grant power of attorney.  “Often,” she said, “they have 1 but not all.” 

Other components of capacity include the ability to understand one’s medical situation, an appreciation of the pros and cons of treatment options, the consistency of choices over time, and the ability to reason. “Can the patient consider the risks and benefits of each option and consider quality of life vs quantity of life in light of their own cultural identity and personal values?”

Keep in mind that levels of capacity can change over time, Ford said, and remember that these judgements are not arbitrary or punitive.

When someone doesn’t have capacity, she said, “it doesn’t necessarily tell us why or whether it will come back. But it does say they can’t provide informed consent.”

What happened to the determinedly reluctant patient who simply wanted to “fight” and not make decisions?

“The oncology provider chose to have the psychology provider in the room while staging information and prognosis was shared,” Ford said in an interview following her presentation. “And the psychology provider assisted with ensuring that the veteran received education in simple terms and in promoting active coping.”

In addition, she said, “the psychologist provider also spent several minutes after the visit giving the veteran an opportunity to discuss their feelings. And the provider physically escorted the veteran to the laboratory to ensure that the impact of receiving difficult news did not impair mood or cognition to the point that the veteran left the medical center instead of engaging in the next step of needed medical care.”

Ford reports no relevant disclosures.

MINNEAPOLIS -- Picture this: A patient with cancer wants to get better and needs your help. But he or she refuses to hear the prognosis or understand the treatment options. The patient, in essence, has embraced a personal don’t-ask, don’t-tell policy.

What should you do as a medical professional? Get help from a psychologist and consider the ethics of the situation, advised a VA psychologist in a presentation at the September 2019 annual meeting of the Association of VA Hematology/Oncology.

Alyssa Ford, PhD, psychosocial oncology coordinator at VA Pittsburgh Healthcare System in Pennsylvania, said she has faced this situation. “We didn’t know the staging yet, but the veteran did not want to know about their prognosis or the treatment options,” she recalled. “They just wanted to fight this cancer.”

At issue in this case, she said, is this question: “What do we do when a patient opts out about receiving sufficient information to make an informed choice?”

As she explained, the key is to understand the person’s capacity—the ability to make an informed decision. “It can be assessed by any licensed health care provider who understands the components of capacity and is able to assess them.”

Ford evaluates a patient’s capacity by analyzing whether he or she can perform 4 tasks: Make decisions, live independently, manage finances, and grant power of attorney.  “Often,” she said, “they have 1 but not all.” 

Other components of capacity include the ability to understand one’s medical situation, an appreciation of the pros and cons of treatment options, the consistency of choices over time, and the ability to reason. “Can the patient consider the risks and benefits of each option and consider quality of life vs quantity of life in light of their own cultural identity and personal values?”

Keep in mind that levels of capacity can change over time, Ford said, and remember that these judgements are not arbitrary or punitive.

When someone doesn’t have capacity, she said, “it doesn’t necessarily tell us why or whether it will come back. But it does say they can’t provide informed consent.”

What happened to the determinedly reluctant patient who simply wanted to “fight” and not make decisions?

“The oncology provider chose to have the psychology provider in the room while staging information and prognosis was shared,” Ford said in an interview following her presentation. “And the psychology provider assisted with ensuring that the veteran received education in simple terms and in promoting active coping.”

In addition, she said, “the psychologist provider also spent several minutes after the visit giving the veteran an opportunity to discuss their feelings. And the provider physically escorted the veteran to the laboratory to ensure that the impact of receiving difficult news did not impair mood or cognition to the point that the veteran left the medical center instead of engaging in the next step of needed medical care.”

Ford reports no relevant disclosures.

MINNEAPOLIS -- Picture this: A patient with cancer wants to get better and needs your help. But he or she refuses to hear the prognosis or understand the treatment options. The patient, in essence, has embraced a personal don’t-ask, don’t-tell policy.

What should you do as a medical professional? Get help from a psychologist and consider the ethics of the situation, advised a VA psychologist in a presentation at the September 2019 annual meeting of the Association of VA Hematology/Oncology.

Alyssa Ford, PhD, psychosocial oncology coordinator at VA Pittsburgh Healthcare System in Pennsylvania, said she has faced this situation. “We didn’t know the staging yet, but the veteran did not want to know about their prognosis or the treatment options,” she recalled. “They just wanted to fight this cancer.”

At issue in this case, she said, is this question: “What do we do when a patient opts out about receiving sufficient information to make an informed choice?”

As she explained, the key is to understand the person’s capacity—the ability to make an informed decision. “It can be assessed by any licensed health care provider who understands the components of capacity and is able to assess them.”

Ford evaluates a patient’s capacity by analyzing whether he or she can perform 4 tasks: Make decisions, live independently, manage finances, and grant power of attorney.  “Often,” she said, “they have 1 but not all.” 

Other components of capacity include the ability to understand one’s medical situation, an appreciation of the pros and cons of treatment options, the consistency of choices over time, and the ability to reason. “Can the patient consider the risks and benefits of each option and consider quality of life vs quantity of life in light of their own cultural identity and personal values?”

Keep in mind that levels of capacity can change over time, Ford said, and remember that these judgements are not arbitrary or punitive.

When someone doesn’t have capacity, she said, “it doesn’t necessarily tell us why or whether it will come back. But it does say they can’t provide informed consent.”

What happened to the determinedly reluctant patient who simply wanted to “fight” and not make decisions?

“The oncology provider chose to have the psychology provider in the room while staging information and prognosis was shared,” Ford said in an interview following her presentation. “And the psychology provider assisted with ensuring that the veteran received education in simple terms and in promoting active coping.”

In addition, she said, “the psychologist provider also spent several minutes after the visit giving the veteran an opportunity to discuss their feelings. And the provider physically escorted the veteran to the laboratory to ensure that the impact of receiving difficult news did not impair mood or cognition to the point that the veteran left the medical center instead of engaging in the next step of needed medical care.”

Ford reports no relevant disclosures.