Oncology

PHOENIX — Patients with head and neck cancer face high rates of malnutrition during treatment, and oral supplements are often recommended. But they are not the entire answer, a dietician told colleagues at the Association of Veterans Affairs (VA) Hematology/Oncology annual meeting.

“Patients should consume the most liberal diet possible throughout treatment,” said advanced practice oncology dietician Brittany Leneweaver, RD, CSO, CES, at the VA Washington DC Healthcare System. “This means not solely relying on oral nutrition supplements like Ensure if possible.”

While Leneweaver said many patients will need supplements, she stressed these products “are meant to supplement the diet and not be the sole source of nutrition, ideally.” Encouraging the intake of whole foods “is really key to make the transition back to solid foods after they’re done with treatment. This makes it so much easier when they’re already swallowing those thicker textures, rather than just liquid the entire time.”

Malnutrition: Common and Damaging

As Leneweaver noted, malnutrition is common in patients with head and neck cancer, and can lead to “increased treatment toxicity, increased risk of infection, decreased survival, increased surgical complication, delayed healing, decreased physical function, and decreased quality of life.”

Malnutrition data in patients with head and neck cancer in the US is sparse. However, a 2024 study found malnutrition in 20% of patients undergoing head and neck cancer surgery and linked the condition to increased length of stay (β, 5.20 additional days), higher costs (β, $15,722) higher odds of potentially preventable complications (adjusted odds ratio [aOR], 2.04), and lower odds of discharge to home (aOR, 0.34).

Leneweaver said her role involves addressing “nutrition impact symptoms” that reduce veteran food intake such as difficulty swallowing, taste disorders, dry mouth, and inflammation of the mucus membranes.

“I can’t tell you how much time I spend just talking to the patient about their medication regimens, making sure they have antiemetics on board, letting the radiation oncologist know, ‘Hey, it’s probably time for medicine,’” she said. “We’re constantly looking at side effects and addressing to alert the team as quickly as possible so that we can prevent further weight loss.”

Better Diets Lead to Better Outcomes

Leneweaver noted that “many times, patients will continue to rely on oral supplements as their primary source of nutrition over the long term. They may be missing out on several health benefits as a result.”

Research shows that high-quality diets matter in this patient group, she said. They’re associated with “decreased symptoms during treatment, reduced head and neck cancer risk, and reduced risk of those chronic nutrition impact symptoms,” Leneweaver said.

Diets before and after cancer diagnosis can make a difference. A 2019 study examined patient diets prior to diagnosis of head and neck cancer. It found that patients with better diet quality were less likely to experience overall nutrition impact symptoms (OR 0.45). However, “studies have found that the majority of our patients with head and neck cancer have an inadequate diet prior to diagnosis,” Leneweaver said. 

As for postdiagnosis nutrition, a 2022 study linked healthier diets in patients with head and neck cancer to 93% lower 3-year risk of all-cause mortality and 85% lower risk of cancer-specific mortality. 

What’s in a High-Quality Diet?

Regarding specific food recommendations, Leneweaver prefers the American Institute for Cancer Research (AICR) nutrition guidelines over the US Department of Agriculture’s Dietary Guidelines for Americans. The AICR “more clearly recommends plant-based diet with at least two-thirds of each meal coming from a variety of plant sources” and recommends avoiding alcohol entirely and limiting red meat, she said. 

Leneweaver said she recognizes that dietary change can be gradual.

“It’s not going to happen overnight,” she said. “We know that lifestyle change takes a lot of work.”

Basic interventions can be effective, she said: “This can be just as simple as recommending a plant-based diet to your patient or recommending they eat the rainbow. And I don’t mean Skittles, I mean actual plants. If you just mention these couple of things to the patients, this can really go a long way, especially if they’re hearing that consistent messaging.”

Team-Based Follow-Up Is Key

Leneweaver emphasized the importance of following up over time even if patients do not initially accept referrals to nutritional services. Dieticians ideally see patients before or during initial treatment and then weekly during radiation therapy. Posttreatment follow-up continues “until they’re nutritionally stable. This can be anywhere from weekly to monthly.”

Leneweaver emphasized collaborating with other team members. For example, she works with a speech pathologist at joint visits, either weekly or monthly, “so that they can get off of that feeding tube or get back to a solid consistency diet, typically before that 3-month PET scan.”

It is also important to understand barriers to healthy eating in the veteran population, including transportation challenges and poor access to healthy food, Leneweaver said.

“Make sure you’re utilizing your social worker, your psychologist, other resources, and food pantries, if you have them.” 

Even when the most ideal choices are not available, she said, “if they only have access to canned vegetables, I’d much rather them eat that than have nothing.”

No disclosures for Leneweaver were provided. 

PHOENIX — Patients with head and neck cancer face high rates of malnutrition during treatment, and oral supplements are often recommended. But they are not the entire answer, a dietician told colleagues at the Association of Veterans Affairs (VA) Hematology/Oncology annual meeting.

“Patients should consume the most liberal diet possible throughout treatment,” said advanced practice oncology dietician Brittany Leneweaver, RD, CSO, CES, at the VA Washington DC Healthcare System. “This means not solely relying on oral nutrition supplements like Ensure if possible.”

While Leneweaver said many patients will need supplements, she stressed these products “are meant to supplement the diet and not be the sole source of nutrition, ideally.” Encouraging the intake of whole foods “is really key to make the transition back to solid foods after they’re done with treatment. This makes it so much easier when they’re already swallowing those thicker textures, rather than just liquid the entire time.”

Malnutrition: Common and Damaging

As Leneweaver noted, malnutrition is common in patients with head and neck cancer, and can lead to “increased treatment toxicity, increased risk of infection, decreased survival, increased surgical complication, delayed healing, decreased physical function, and decreased quality of life.”

Malnutrition data in patients with head and neck cancer in the US is sparse. However, a 2024 study found malnutrition in 20% of patients undergoing head and neck cancer surgery and linked the condition to increased length of stay (β, 5.20 additional days), higher costs (β, $15,722) higher odds of potentially preventable complications (adjusted odds ratio [aOR], 2.04), and lower odds of discharge to home (aOR, 0.34).

Leneweaver said her role involves addressing “nutrition impact symptoms” that reduce veteran food intake such as difficulty swallowing, taste disorders, dry mouth, and inflammation of the mucus membranes.

“I can’t tell you how much time I spend just talking to the patient about their medication regimens, making sure they have antiemetics on board, letting the radiation oncologist know, ‘Hey, it’s probably time for medicine,’” she said. “We’re constantly looking at side effects and addressing to alert the team as quickly as possible so that we can prevent further weight loss.”

Better Diets Lead to Better Outcomes

Leneweaver noted that “many times, patients will continue to rely on oral supplements as their primary source of nutrition over the long term. They may be missing out on several health benefits as a result.”

Research shows that high-quality diets matter in this patient group, she said. They’re associated with “decreased symptoms during treatment, reduced head and neck cancer risk, and reduced risk of those chronic nutrition impact symptoms,” Leneweaver said.

Diets before and after cancer diagnosis can make a difference. A 2019 study examined patient diets prior to diagnosis of head and neck cancer. It found that patients with better diet quality were less likely to experience overall nutrition impact symptoms (OR 0.45). However, “studies have found that the majority of our patients with head and neck cancer have an inadequate diet prior to diagnosis,” Leneweaver said. 

As for postdiagnosis nutrition, a 2022 study linked healthier diets in patients with head and neck cancer to 93% lower 3-year risk of all-cause mortality and 85% lower risk of cancer-specific mortality. 

What’s in a High-Quality Diet?

Regarding specific food recommendations, Leneweaver prefers the American Institute for Cancer Research (AICR) nutrition guidelines over the US Department of Agriculture’s Dietary Guidelines for Americans. The AICR “more clearly recommends plant-based diet with at least two-thirds of each meal coming from a variety of plant sources” and recommends avoiding alcohol entirely and limiting red meat, she said. 

Leneweaver said she recognizes that dietary change can be gradual.

“It’s not going to happen overnight,” she said. “We know that lifestyle change takes a lot of work.”

Basic interventions can be effective, she said: “This can be just as simple as recommending a plant-based diet to your patient or recommending they eat the rainbow. And I don’t mean Skittles, I mean actual plants. If you just mention these couple of things to the patients, this can really go a long way, especially if they’re hearing that consistent messaging.”

Team-Based Follow-Up Is Key

Leneweaver emphasized the importance of following up over time even if patients do not initially accept referrals to nutritional services. Dieticians ideally see patients before or during initial treatment and then weekly during radiation therapy. Posttreatment follow-up continues “until they’re nutritionally stable. This can be anywhere from weekly to monthly.”

Leneweaver emphasized collaborating with other team members. For example, she works with a speech pathologist at joint visits, either weekly or monthly, “so that they can get off of that feeding tube or get back to a solid consistency diet, typically before that 3-month PET scan.”

It is also important to understand barriers to healthy eating in the veteran population, including transportation challenges and poor access to healthy food, Leneweaver said.

“Make sure you’re utilizing your social worker, your psychologist, other resources, and food pantries, if you have them.” 

Even when the most ideal choices are not available, she said, “if they only have access to canned vegetables, I’d much rather them eat that than have nothing.”

No disclosures for Leneweaver were provided. 

PHOENIX — Patients with head and neck cancer face high rates of malnutrition during treatment, and oral supplements are often recommended. But they are not the entire answer, a dietician told colleagues at the Association of Veterans Affairs (VA) Hematology/Oncology annual meeting.

“Patients should consume the most liberal diet possible throughout treatment,” said advanced practice oncology dietician Brittany Leneweaver, RD, CSO, CES, at the VA Washington DC Healthcare System. “This means not solely relying on oral nutrition supplements like Ensure if possible.”

While Leneweaver said many patients will need supplements, she stressed these products “are meant to supplement the diet and not be the sole source of nutrition, ideally.” Encouraging the intake of whole foods “is really key to make the transition back to solid foods after they’re done with treatment. This makes it so much easier when they’re already swallowing those thicker textures, rather than just liquid the entire time.”

Malnutrition: Common and Damaging

As Leneweaver noted, malnutrition is common in patients with head and neck cancer, and can lead to “increased treatment toxicity, increased risk of infection, decreased survival, increased surgical complication, delayed healing, decreased physical function, and decreased quality of life.”

Malnutrition data in patients with head and neck cancer in the US is sparse. However, a 2024 study found malnutrition in 20% of patients undergoing head and neck cancer surgery and linked the condition to increased length of stay (β, 5.20 additional days), higher costs (β, $15,722) higher odds of potentially preventable complications (adjusted odds ratio [aOR], 2.04), and lower odds of discharge to home (aOR, 0.34).

Leneweaver said her role involves addressing “nutrition impact symptoms” that reduce veteran food intake such as difficulty swallowing, taste disorders, dry mouth, and inflammation of the mucus membranes.

“I can’t tell you how much time I spend just talking to the patient about their medication regimens, making sure they have antiemetics on board, letting the radiation oncologist know, ‘Hey, it’s probably time for medicine,’” she said. “We’re constantly looking at side effects and addressing to alert the team as quickly as possible so that we can prevent further weight loss.”

Better Diets Lead to Better Outcomes

Leneweaver noted that “many times, patients will continue to rely on oral supplements as their primary source of nutrition over the long term. They may be missing out on several health benefits as a result.”

Research shows that high-quality diets matter in this patient group, she said. They’re associated with “decreased symptoms during treatment, reduced head and neck cancer risk, and reduced risk of those chronic nutrition impact symptoms,” Leneweaver said.

Diets before and after cancer diagnosis can make a difference. A 2019 study examined patient diets prior to diagnosis of head and neck cancer. It found that patients with better diet quality were less likely to experience overall nutrition impact symptoms (OR 0.45). However, “studies have found that the majority of our patients with head and neck cancer have an inadequate diet prior to diagnosis,” Leneweaver said. 

As for postdiagnosis nutrition, a 2022 study linked healthier diets in patients with head and neck cancer to 93% lower 3-year risk of all-cause mortality and 85% lower risk of cancer-specific mortality. 

What’s in a High-Quality Diet?

Regarding specific food recommendations, Leneweaver prefers the American Institute for Cancer Research (AICR) nutrition guidelines over the US Department of Agriculture’s Dietary Guidelines for Americans. The AICR “more clearly recommends plant-based diet with at least two-thirds of each meal coming from a variety of plant sources” and recommends avoiding alcohol entirely and limiting red meat, she said. 

Leneweaver said she recognizes that dietary change can be gradual.

“It’s not going to happen overnight,” she said. “We know that lifestyle change takes a lot of work.”

Basic interventions can be effective, she said: “This can be just as simple as recommending a plant-based diet to your patient or recommending they eat the rainbow. And I don’t mean Skittles, I mean actual plants. If you just mention these couple of things to the patients, this can really go a long way, especially if they’re hearing that consistent messaging.”

Team-Based Follow-Up Is Key

Leneweaver emphasized the importance of following up over time even if patients do not initially accept referrals to nutritional services. Dieticians ideally see patients before or during initial treatment and then weekly during radiation therapy. Posttreatment follow-up continues “until they’re nutritionally stable. This can be anywhere from weekly to monthly.”

Leneweaver emphasized collaborating with other team members. For example, she works with a speech pathologist at joint visits, either weekly or monthly, “so that they can get off of that feeding tube or get back to a solid consistency diet, typically before that 3-month PET scan.”

It is also important to understand barriers to healthy eating in the veteran population, including transportation challenges and poor access to healthy food, Leneweaver said.

“Make sure you’re utilizing your social worker, your psychologist, other resources, and food pantries, if you have them.” 

Even when the most ideal choices are not available, she said, “if they only have access to canned vegetables, I’d much rather them eat that than have nothing.”

No disclosures for Leneweaver were provided. 

TOPLINE:

A comprehensive review of 92 studies found that 15% to 20% of lung cancers occurred among nonsmokers and were associated with environmental and germline risk factors. These cancers frequently harbored actionable genomic drivers, and targeted EGFR and ALK therapies produced significant diseasefree survival (DFS) and overall survival benefits.

METHODOLOGY:

• Lung cancer continues to be the leading cause of cancer death worldwide, causing about 1.8 million deaths in 2022, with smoking remaining the predominant risk factor. However, the incidence of lung cancer among nonsmokers (those who have smoked less than 100 cigarettes in their lifetime) is rising, varies by sex and geography, and is linked to environmental exposures and family history. The misperception that lung cancer is almost invariably caused by smoking may delay assessment and diagnosis.
• Researchers conducted a review of 92 studies on lung cancer in nonsmokers: 6 meta-analyses or systematic reviews, 16 randomized clinical trials, eight prospective cohort studies, seven retrospective cohort studies, three cross-sectional studies, four observational or case-control studies, 13 genomic studies, and 35 other studies.
• Overall, lung cancer among nonsmokers accounted for 15% to 20% of all lung cancer cases. Most lung cancers in nonsmokers were adenocarcinomas (60% to 80%), with a median age at diagnosis of 67 years in this group compared with 70 years in people with a history of smoking.
• Data analysis from three US hospital networks showed that the proportion of lung cancer among nonsmokers increased from 8.0% to 14.9% between 1990 and 2013. A pooled analysis of seven Finnish cohorts reported an absolute increase in lung cancer among nonsmokers from 6.9 per 100,000 person-years in 1972 to 12.9 per 100,000 person-years in 2015.
• The age-adjusted incidence rate of lung cancer in the US between 2000 and 2013 was 17.5 per 100,000 individuals among Asian female nonsmokers compared with 10.1 per 100,000 among non-Hispanic White female nonsmokers.

TAKEAWAY:

• Environmental and occupational risk factors were secondhand smoke, residential radon, outdoor and household air pollution (PM_2.5), asbestos and silica exposure, and prior thoracic radiotherapy. Having a first-degree relative with lung cancer increased the risk of developing lung cancer, and genome-wide association studies identified susceptibility loci associated with lung cancer risk in nonsmokers.
• Family history and inherited susceptibility increased lung cancer risk in never smokers (odds ratio [OR] for lung cancer in those with a first–degree relative, 1.51), and clonal hematopoiesis was also associated with higher risk (OR, 1.43). Importantly, tumors in nonsmokers were frequently driven by actionable somatic alterations (EGFR mutations, 40% to 60% in nonsmokers compared with 10% in smokers) and enrichment of ALK/ROS1/RET/ERBB2/NTRK/NRG1 fusions; 78% to 92% of adenocarcinomas in nonsmokers harbored actionable drivers (compared with 49.5% in ever smokers), and nonsmokers had a substantially lower tumor mutational burden (10–fold lower).
• Similar to individuals with a history of smoking, nonsmokers with lung cancer presented with cough, pain, dyspnea, or weight loss or had disease detected incidentally. Surgical resection remained the preferred treatment for anatomically resectable lung cancer (stages I-III) in medically eligible patients, with follow-up CT screening recommended every 6 months for 2 to 3 years and then annually.
• Targeted adjuvant therapy substantially improved outcomes for resected EGFR–mutant or ALK–rearranged non-small cell lung cancer (NSCLC). Four-year DFS was increased to 70% with osimertinib compared with 29% with placebo (hazard ratio [HR], 0.23) and 5–year overall survival was increased to 85% compared with 73% (HR, 0.49). Two–year DFS was 93.8% with alectinib compared with 63% with placebo (HR, 0.24). In unresectable EGFR-mutated stage III NSCLC, median progression-free survival was 39.1 months with adjuvant osimertinib compared with 5.6 months with placebo. For resected ALKpositive disease, 2–year DFS was 93.8% with adjuvant alectinib compared with 63.0% with chemotherapy (HR, 0.24).
• However, singleagent single agent programmed cell death protein 1 inhibitors or programmed death-ligand 1 inhibitors demonstrated limited efficacy in EGFR or ALK–driven tumors, and benefit was attenuated in never smokers. Regarding screening and early detection, the US Preventive Services Task Force did not recommend lowdose CT screening for nonsmokers, whereas Taiwan implemented a biennial screening program for selected nonsmoking high–risk groups.

IN PRACTICE:

“Among patients with lung cancer, nonsmoking individuals are more likely to have genomic alterations, such as EGFR mutations or ALK gene rearrangements, and these patients have improved survival when treated with TKIs compared with chemotherapy,” the authors of the study wrote.

SOURCE:

The study, led by Cian Murphy, PhD, Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, England, was published online in JAMA.

LIMITATIONS:

Becausesmoking history was often not included in many databases, cancer registries, and trials, the incidence and prevalence of lung cancer in nonsmokers could not be accurately determined. Additionally, accurate quantification of environmental exposures, such as air pollution, presented significant challenges. The quality of the evidence was not formally evaluated, and some relevant articles may have been missed in the literature review.

DISCLOSURES:

The study received support from multiple organizations, including the Rosetrees Trust, Ruth Strauss Foundation, Cancer Research UK, and the National Health and Medical Research Council. Several authors reported receiving grants or personal fees from and having other ties with various sources. Full disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

A comprehensive review of 92 studies found that 15% to 20% of lung cancers occurred among nonsmokers and were associated with environmental and germline risk factors. These cancers frequently harbored actionable genomic drivers, and targeted EGFR and ALK therapies produced significant diseasefree survival (DFS) and overall survival benefits.

METHODOLOGY:

• Lung cancer continues to be the leading cause of cancer death worldwide, causing about 1.8 million deaths in 2022, with smoking remaining the predominant risk factor. However, the incidence of lung cancer among nonsmokers (those who have smoked less than 100 cigarettes in their lifetime) is rising, varies by sex and geography, and is linked to environmental exposures and family history. The misperception that lung cancer is almost invariably caused by smoking may delay assessment and diagnosis.
• Researchers conducted a review of 92 studies on lung cancer in nonsmokers: 6 meta-analyses or systematic reviews, 16 randomized clinical trials, eight prospective cohort studies, seven retrospective cohort studies, three cross-sectional studies, four observational or case-control studies, 13 genomic studies, and 35 other studies.
• Overall, lung cancer among nonsmokers accounted for 15% to 20% of all lung cancer cases. Most lung cancers in nonsmokers were adenocarcinomas (60% to 80%), with a median age at diagnosis of 67 years in this group compared with 70 years in people with a history of smoking.
• Data analysis from three US hospital networks showed that the proportion of lung cancer among nonsmokers increased from 8.0% to 14.9% between 1990 and 2013. A pooled analysis of seven Finnish cohorts reported an absolute increase in lung cancer among nonsmokers from 6.9 per 100,000 person-years in 1972 to 12.9 per 100,000 person-years in 2015.
• The age-adjusted incidence rate of lung cancer in the US between 2000 and 2013 was 17.5 per 100,000 individuals among Asian female nonsmokers compared with 10.1 per 100,000 among non-Hispanic White female nonsmokers.

TAKEAWAY:

• Environmental and occupational risk factors were secondhand smoke, residential radon, outdoor and household air pollution (PM_2.5), asbestos and silica exposure, and prior thoracic radiotherapy. Having a first-degree relative with lung cancer increased the risk of developing lung cancer, and genome-wide association studies identified susceptibility loci associated with lung cancer risk in nonsmokers.
• Family history and inherited susceptibility increased lung cancer risk in never smokers (odds ratio [OR] for lung cancer in those with a first–degree relative, 1.51), and clonal hematopoiesis was also associated with higher risk (OR, 1.43). Importantly, tumors in nonsmokers were frequently driven by actionable somatic alterations (EGFR mutations, 40% to 60% in nonsmokers compared with 10% in smokers) and enrichment of ALK/ROS1/RET/ERBB2/NTRK/NRG1 fusions; 78% to 92% of adenocarcinomas in nonsmokers harbored actionable drivers (compared with 49.5% in ever smokers), and nonsmokers had a substantially lower tumor mutational burden (10–fold lower).
• Similar to individuals with a history of smoking, nonsmokers with lung cancer presented with cough, pain, dyspnea, or weight loss or had disease detected incidentally. Surgical resection remained the preferred treatment for anatomically resectable lung cancer (stages I-III) in medically eligible patients, with follow-up CT screening recommended every 6 months for 2 to 3 years and then annually.
• Targeted adjuvant therapy substantially improved outcomes for resected EGFR–mutant or ALK–rearranged non-small cell lung cancer (NSCLC). Four-year DFS was increased to 70% with osimertinib compared with 29% with placebo (hazard ratio [HR], 0.23) and 5–year overall survival was increased to 85% compared with 73% (HR, 0.49). Two–year DFS was 93.8% with alectinib compared with 63% with placebo (HR, 0.24). In unresectable EGFR-mutated stage III NSCLC, median progression-free survival was 39.1 months with adjuvant osimertinib compared with 5.6 months with placebo. For resected ALKpositive disease, 2–year DFS was 93.8% with adjuvant alectinib compared with 63.0% with chemotherapy (HR, 0.24).
• However, singleagent single agent programmed cell death protein 1 inhibitors or programmed death-ligand 1 inhibitors demonstrated limited efficacy in EGFR or ALK–driven tumors, and benefit was attenuated in never smokers. Regarding screening and early detection, the US Preventive Services Task Force did not recommend lowdose CT screening for nonsmokers, whereas Taiwan implemented a biennial screening program for selected nonsmoking high–risk groups.

IN PRACTICE:

“Among patients with lung cancer, nonsmoking individuals are more likely to have genomic alterations, such as EGFR mutations or ALK gene rearrangements, and these patients have improved survival when treated with TKIs compared with chemotherapy,” the authors of the study wrote.

SOURCE:

The study, led by Cian Murphy, PhD, Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, England, was published online in JAMA.

LIMITATIONS:

Becausesmoking history was often not included in many databases, cancer registries, and trials, the incidence and prevalence of lung cancer in nonsmokers could not be accurately determined. Additionally, accurate quantification of environmental exposures, such as air pollution, presented significant challenges. The quality of the evidence was not formally evaluated, and some relevant articles may have been missed in the literature review.

DISCLOSURES:

The study received support from multiple organizations, including the Rosetrees Trust, Ruth Strauss Foundation, Cancer Research UK, and the National Health and Medical Research Council. Several authors reported receiving grants or personal fees from and having other ties with various sources. Full disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

A comprehensive review of 92 studies found that 15% to 20% of lung cancers occurred among nonsmokers and were associated with environmental and germline risk factors. These cancers frequently harbored actionable genomic drivers, and targeted EGFR and ALK therapies produced significant diseasefree survival (DFS) and overall survival benefits.

METHODOLOGY:

• Lung cancer continues to be the leading cause of cancer death worldwide, causing about 1.8 million deaths in 2022, with smoking remaining the predominant risk factor. However, the incidence of lung cancer among nonsmokers (those who have smoked less than 100 cigarettes in their lifetime) is rising, varies by sex and geography, and is linked to environmental exposures and family history. The misperception that lung cancer is almost invariably caused by smoking may delay assessment and diagnosis.
• Researchers conducted a review of 92 studies on lung cancer in nonsmokers: 6 meta-analyses or systematic reviews, 16 randomized clinical trials, eight prospective cohort studies, seven retrospective cohort studies, three cross-sectional studies, four observational or case-control studies, 13 genomic studies, and 35 other studies.
• Overall, lung cancer among nonsmokers accounted for 15% to 20% of all lung cancer cases. Most lung cancers in nonsmokers were adenocarcinomas (60% to 80%), with a median age at diagnosis of 67 years in this group compared with 70 years in people with a history of smoking.
• Data analysis from three US hospital networks showed that the proportion of lung cancer among nonsmokers increased from 8.0% to 14.9% between 1990 and 2013. A pooled analysis of seven Finnish cohorts reported an absolute increase in lung cancer among nonsmokers from 6.9 per 100,000 person-years in 1972 to 12.9 per 100,000 person-years in 2015.
• The age-adjusted incidence rate of lung cancer in the US between 2000 and 2013 was 17.5 per 100,000 individuals among Asian female nonsmokers compared with 10.1 per 100,000 among non-Hispanic White female nonsmokers.

TAKEAWAY:

• Environmental and occupational risk factors were secondhand smoke, residential radon, outdoor and household air pollution (PM_2.5), asbestos and silica exposure, and prior thoracic radiotherapy. Having a first-degree relative with lung cancer increased the risk of developing lung cancer, and genome-wide association studies identified susceptibility loci associated with lung cancer risk in nonsmokers.
• Family history and inherited susceptibility increased lung cancer risk in never smokers (odds ratio [OR] for lung cancer in those with a first–degree relative, 1.51), and clonal hematopoiesis was also associated with higher risk (OR, 1.43). Importantly, tumors in nonsmokers were frequently driven by actionable somatic alterations (EGFR mutations, 40% to 60% in nonsmokers compared with 10% in smokers) and enrichment of ALK/ROS1/RET/ERBB2/NTRK/NRG1 fusions; 78% to 92% of adenocarcinomas in nonsmokers harbored actionable drivers (compared with 49.5% in ever smokers), and nonsmokers had a substantially lower tumor mutational burden (10–fold lower).
• Similar to individuals with a history of smoking, nonsmokers with lung cancer presented with cough, pain, dyspnea, or weight loss or had disease detected incidentally. Surgical resection remained the preferred treatment for anatomically resectable lung cancer (stages I-III) in medically eligible patients, with follow-up CT screening recommended every 6 months for 2 to 3 years and then annually.
• Targeted adjuvant therapy substantially improved outcomes for resected EGFR–mutant or ALK–rearranged non-small cell lung cancer (NSCLC). Four-year DFS was increased to 70% with osimertinib compared with 29% with placebo (hazard ratio [HR], 0.23) and 5–year overall survival was increased to 85% compared with 73% (HR, 0.49). Two–year DFS was 93.8% with alectinib compared with 63% with placebo (HR, 0.24). In unresectable EGFR-mutated stage III NSCLC, median progression-free survival was 39.1 months with adjuvant osimertinib compared with 5.6 months with placebo. For resected ALKpositive disease, 2–year DFS was 93.8% with adjuvant alectinib compared with 63.0% with chemotherapy (HR, 0.24).
• However, singleagent single agent programmed cell death protein 1 inhibitors or programmed death-ligand 1 inhibitors demonstrated limited efficacy in EGFR or ALK–driven tumors, and benefit was attenuated in never smokers. Regarding screening and early detection, the US Preventive Services Task Force did not recommend lowdose CT screening for nonsmokers, whereas Taiwan implemented a biennial screening program for selected nonsmoking high–risk groups.

IN PRACTICE:

“Among patients with lung cancer, nonsmoking individuals are more likely to have genomic alterations, such as EGFR mutations or ALK gene rearrangements, and these patients have improved survival when treated with TKIs compared with chemotherapy,” the authors of the study wrote.

SOURCE:

The study, led by Cian Murphy, PhD, Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, England, was published online in JAMA.

LIMITATIONS:

Becausesmoking history was often not included in many databases, cancer registries, and trials, the incidence and prevalence of lung cancer in nonsmokers could not be accurately determined. Additionally, accurate quantification of environmental exposures, such as air pollution, presented significant challenges. The quality of the evidence was not formally evaluated, and some relevant articles may have been missed in the literature review.

DISCLOSURES:

The study received support from multiple organizations, including the Rosetrees Trust, Ruth Strauss Foundation, Cancer Research UK, and the National Health and Medical Research Council. Several authors reported receiving grants or personal fees from and having other ties with various sources. Full disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

A faster, oral alternative to docetaxel is set to reach NHS clinics after the National Institute for Health and Care Excellence (NICE) recommended darolutamide (Nubeqa, Bayer) in combination with androgen deprivation therapy (ADT) for men with metastatic hormone-sensitive prostate cancer who are unable to receive or tolerate chemotherapy.

Detailed in NICE’s final draft guidance, the decision will make darolutamide available through the NHS in England and Wales to approximately 6000 patients, offering a new oral therapy for those who with limited alternatives to docetaxel or other androgen-receptor inhibitors.

 

New Option for Chemo-Ineligible Patients

Darolutamide functions by blocking hormones that fuel cancer growth, specifically depriving prostate cancer cells of testosterone required for multiplication and spread. Patients take two tablets twice daily alongside standard ADT. 

Peter Johnson, national clinical director for cancer at NHS England, welcomed the decision and expects this approval to give clinicians and their patients “more flexibility to choose the approach best suited to individual circumstances and clinical needs.”

The guidance was finalised 5 weeks ahead of the standard review timeline, underscoring NICE’s commitment to accelerating access to effective prostate cancer treatments.

 

Clinical Trial Evidence

The NICE’s decision was supported by evidence from the phase 3 ARASENS trial (N = 1306). 

The results showed that adding darolutamide to ADT and docetaxel significantly improved overall survival in metastatic hormone-sensitive prostate cancer, reducing the risk for death by 32% compared with ADT and docetaxel alone. Progression-free outcomes, measured by time to castration-resistant disease or death, also favoured darolutamide. 

A NICE network meta-analysis of the TITAN, ARCHES, LATITUDE, and STAMPEDE trials suggested that combining ADT with androgen-receptor pathway inhibitors such as apalutamide, enzalutamide, and abiraterone provides comparable survival benefits in this disease setting.

 

Cost and Implementation

NICE determined that darolutamide plus ADT delivers similar or lower overall costs to the NHS compared with apalutamide plus ADT. The list price is £4040.00 for a 28-day supply (112 × 300-mg tablets), though Bayer has agreed to a confidential commercial discount.

The guidance requires healthcare providers to use the least expensive suitable treatment option, considering administration costs, dosages, price per dose, and commercial arrangements when choosing between darolutamide plus ADT and apalutamide plus ADT. 

NHS England and integrated care boards must provide funding within 30 days of final publication, with routine commissioning beginning after this interim period.

A version of this article first appeared on Medscape.com.

A faster, oral alternative to docetaxel is set to reach NHS clinics after the National Institute for Health and Care Excellence (NICE) recommended darolutamide (Nubeqa, Bayer) in combination with androgen deprivation therapy (ADT) for men with metastatic hormone-sensitive prostate cancer who are unable to receive or tolerate chemotherapy.

Detailed in NICE’s final draft guidance, the decision will make darolutamide available through the NHS in England and Wales to approximately 6000 patients, offering a new oral therapy for those who with limited alternatives to docetaxel or other androgen-receptor inhibitors.

 

New Option for Chemo-Ineligible Patients

Darolutamide functions by blocking hormones that fuel cancer growth, specifically depriving prostate cancer cells of testosterone required for multiplication and spread. Patients take two tablets twice daily alongside standard ADT. 

Peter Johnson, national clinical director for cancer at NHS England, welcomed the decision and expects this approval to give clinicians and their patients “more flexibility to choose the approach best suited to individual circumstances and clinical needs.”

The guidance was finalised 5 weeks ahead of the standard review timeline, underscoring NICE’s commitment to accelerating access to effective prostate cancer treatments.

 

Clinical Trial Evidence

The NICE’s decision was supported by evidence from the phase 3 ARASENS trial (N = 1306). 

The results showed that adding darolutamide to ADT and docetaxel significantly improved overall survival in metastatic hormone-sensitive prostate cancer, reducing the risk for death by 32% compared with ADT and docetaxel alone. Progression-free outcomes, measured by time to castration-resistant disease or death, also favoured darolutamide. 

A NICE network meta-analysis of the TITAN, ARCHES, LATITUDE, and STAMPEDE trials suggested that combining ADT with androgen-receptor pathway inhibitors such as apalutamide, enzalutamide, and abiraterone provides comparable survival benefits in this disease setting.

 

Cost and Implementation

NICE determined that darolutamide plus ADT delivers similar or lower overall costs to the NHS compared with apalutamide plus ADT. The list price is £4040.00 for a 28-day supply (112 × 300-mg tablets), though Bayer has agreed to a confidential commercial discount.

The guidance requires healthcare providers to use the least expensive suitable treatment option, considering administration costs, dosages, price per dose, and commercial arrangements when choosing between darolutamide plus ADT and apalutamide plus ADT. 

NHS England and integrated care boards must provide funding within 30 days of final publication, with routine commissioning beginning after this interim period.

A version of this article first appeared on Medscape.com.

A faster, oral alternative to docetaxel is set to reach NHS clinics after the National Institute for Health and Care Excellence (NICE) recommended darolutamide (Nubeqa, Bayer) in combination with androgen deprivation therapy (ADT) for men with metastatic hormone-sensitive prostate cancer who are unable to receive or tolerate chemotherapy.

Detailed in NICE’s final draft guidance, the decision will make darolutamide available through the NHS in England and Wales to approximately 6000 patients, offering a new oral therapy for those who with limited alternatives to docetaxel or other androgen-receptor inhibitors.

 

New Option for Chemo-Ineligible Patients

Darolutamide functions by blocking hormones that fuel cancer growth, specifically depriving prostate cancer cells of testosterone required for multiplication and spread. Patients take two tablets twice daily alongside standard ADT. 

Peter Johnson, national clinical director for cancer at NHS England, welcomed the decision and expects this approval to give clinicians and their patients “more flexibility to choose the approach best suited to individual circumstances and clinical needs.”

The guidance was finalised 5 weeks ahead of the standard review timeline, underscoring NICE’s commitment to accelerating access to effective prostate cancer treatments.

 

Clinical Trial Evidence

The NICE’s decision was supported by evidence from the phase 3 ARASENS trial (N = 1306). 

The results showed that adding darolutamide to ADT and docetaxel significantly improved overall survival in metastatic hormone-sensitive prostate cancer, reducing the risk for death by 32% compared with ADT and docetaxel alone. Progression-free outcomes, measured by time to castration-resistant disease or death, also favoured darolutamide. 

A NICE network meta-analysis of the TITAN, ARCHES, LATITUDE, and STAMPEDE trials suggested that combining ADT with androgen-receptor pathway inhibitors such as apalutamide, enzalutamide, and abiraterone provides comparable survival benefits in this disease setting.

 

Cost and Implementation

NICE determined that darolutamide plus ADT delivers similar or lower overall costs to the NHS compared with apalutamide plus ADT. The list price is £4040.00 for a 28-day supply (112 × 300-mg tablets), though Bayer has agreed to a confidential commercial discount.

The guidance requires healthcare providers to use the least expensive suitable treatment option, considering administration costs, dosages, price per dose, and commercial arrangements when choosing between darolutamide plus ADT and apalutamide plus ADT. 

NHS England and integrated care boards must provide funding within 30 days of final publication, with routine commissioning beginning after this interim period.

A version of this article first appeared on Medscape.com.

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at [email protected]. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

A version of this article first appeared on Medscape.com.

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at [email protected]. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

A version of this article first appeared on Medscape.com.

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at [email protected]. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

A version of this article first appeared on Medscape.com.

PHOENIX – Medications used in oncology clinical trials pose unique challenges in areas such as labeling, packaging, and administration, a US Department of Veterans Affairs (VA) pharmacist cautioned colleagues, and placebos have special needs too.

Even basic safety protections can be lacking when a drug is investigational, said Emily Hennes, PharmD, BCOP, clinical pharmacy specialist for research at William S. Middleton Memorial Veterans Hospital in Shorewood Hills, Wisconsin, in a presentation at the annual meeting of the Association of VA Hematology/Oncology.

“All of the safety features that we have come to know and love in dispensing commercial drugs are absent. There’s no Tall Man lettering, there's no color differentiation, and there's no barcoding, because these are not registered drugs," she said.

A 2017 report found that 81% of pharmacists surveyed indicated some level of concern regarding the safety risk in using  investigational drugs. At the same time, Hennes noted, the Joint Commission has mandated that pharmacists must control the storage, dispensing, labeling, and distribution of investigational medications.

Here are things to know about the use of investigational cancer drugs:

Drug Interactions Are Common

Hennes highlighted a 2023 study of medication reconciliation of 501 patients in 79 clinical trials that found alarming levels of drug interactions: 

• 360 clinically relevant drug-drug interactions were identified among 189 patients, including 158 therapies that were prohibited by protocols. Of these, 57.7% involved cytochrome P450 enzymes, which are involved in metabolism. 

• Reconciliation revealed that 35.2% of medications were not otherwise known or documented.

• A median of 2 previously unknown therapies per patient was discovered in 74% of patients.

• Alternative medicine products such as supplements and over-the-counter drugs were implicated in 60% of identified drug interactions.

• Only 41% of oncologists discussed alternative medicine use with patients, which Hennes attributed to “lack of familiarity with many alternative medicine products or insufficient training.”

To make things more complicated, “We sometimes don’t know the full pharmacokinetic and pharmacodynamic profile of an investigational agent,” she said. 

Naming and Labeling May Not Be Standard

Investigational products may not have genetic names and instead have an alphanumeric identifier such as INV54826 that can be quite similar to other products, she said. Investigational drugs may even go through name changes, forcing pharmacists to be alerted to protect patients. 

In addition, labeling may not be standardized. Drugs may arrive unlabeled, with the wrong volume and size, and lack of barcoding. In some cases, pharmacists choose to put new, patient-friendly labels on these products, Hennes said. 

Information Distribution is Key

“Something that comes up in our practice quite a bit is that there’s no standard drug reference regarding investigational drugs,” Hennes said. “Finding ways to get key information to staff at the point of care is really critical to make sure we’re able to safely treat our patients.”

Precautions May Be Needed to Maintain Blinding Protocols 

Hennes explained that pharmacists must use opaque brown bag covers to maintain blinding when parenteral products have distinctive colors. Lines may have to be covered too, which can create challenges during administration. 

“Pumps aren’t meant to run lines that are covered,” she said, which can lead to jams. “If you don’t do education with your point of care staff, it can cause a lot of confusion.” 

It’s also important for blinding purposes to keep an eye on how long it takes to prepare a treatment, she said. A study’s integrity, for example, could be violated if a complex investigational product takes an hour to equilibrate to room temperature and 20-30 minutes to prepare, while a placebo only requires “drawing a few mils of saline out of a bag and labeling it.”

Education for Patients Can Be Useful 

Hennes urged colleagues to remind patients to save investigational medication at the end of each cycle and return it to the clinic site for accountability.

She also suggested creating treatment calendars/reminders for patients and discussing adherence strategies with them. 

Hennes reported no disclosures. 

PHOENIX – Medications used in oncology clinical trials pose unique challenges in areas such as labeling, packaging, and administration, a US Department of Veterans Affairs (VA) pharmacist cautioned colleagues, and placebos have special needs too.

Even basic safety protections can be lacking when a drug is investigational, said Emily Hennes, PharmD, BCOP, clinical pharmacy specialist for research at William S. Middleton Memorial Veterans Hospital in Shorewood Hills, Wisconsin, in a presentation at the annual meeting of the Association of VA Hematology/Oncology.

“All of the safety features that we have come to know and love in dispensing commercial drugs are absent. There’s no Tall Man lettering, there's no color differentiation, and there's no barcoding, because these are not registered drugs," she said.

A 2017 report found that 81% of pharmacists surveyed indicated some level of concern regarding the safety risk in using  investigational drugs. At the same time, Hennes noted, the Joint Commission has mandated that pharmacists must control the storage, dispensing, labeling, and distribution of investigational medications.

Here are things to know about the use of investigational cancer drugs:

Drug Interactions Are Common

Hennes highlighted a 2023 study of medication reconciliation of 501 patients in 79 clinical trials that found alarming levels of drug interactions: 

• 360 clinically relevant drug-drug interactions were identified among 189 patients, including 158 therapies that were prohibited by protocols. Of these, 57.7% involved cytochrome P450 enzymes, which are involved in metabolism. 

• Reconciliation revealed that 35.2% of medications were not otherwise known or documented.

• A median of 2 previously unknown therapies per patient was discovered in 74% of patients.

• Alternative medicine products such as supplements and over-the-counter drugs were implicated in 60% of identified drug interactions.

• Only 41% of oncologists discussed alternative medicine use with patients, which Hennes attributed to “lack of familiarity with many alternative medicine products or insufficient training.”

To make things more complicated, “We sometimes don’t know the full pharmacokinetic and pharmacodynamic profile of an investigational agent,” she said. 

Naming and Labeling May Not Be Standard

Investigational products may not have genetic names and instead have an alphanumeric identifier such as INV54826 that can be quite similar to other products, she said. Investigational drugs may even go through name changes, forcing pharmacists to be alerted to protect patients. 

In addition, labeling may not be standardized. Drugs may arrive unlabeled, with the wrong volume and size, and lack of barcoding. In some cases, pharmacists choose to put new, patient-friendly labels on these products, Hennes said. 

Information Distribution is Key

“Something that comes up in our practice quite a bit is that there’s no standard drug reference regarding investigational drugs,” Hennes said. “Finding ways to get key information to staff at the point of care is really critical to make sure we’re able to safely treat our patients.”

Precautions May Be Needed to Maintain Blinding Protocols 

Hennes explained that pharmacists must use opaque brown bag covers to maintain blinding when parenteral products have distinctive colors. Lines may have to be covered too, which can create challenges during administration. 

“Pumps aren’t meant to run lines that are covered,” she said, which can lead to jams. “If you don’t do education with your point of care staff, it can cause a lot of confusion.” 

It’s also important for blinding purposes to keep an eye on how long it takes to prepare a treatment, she said. A study’s integrity, for example, could be violated if a complex investigational product takes an hour to equilibrate to room temperature and 20-30 minutes to prepare, while a placebo only requires “drawing a few mils of saline out of a bag and labeling it.”

Education for Patients Can Be Useful 

Hennes urged colleagues to remind patients to save investigational medication at the end of each cycle and return it to the clinic site for accountability.

She also suggested creating treatment calendars/reminders for patients and discussing adherence strategies with them. 

Hennes reported no disclosures. 

PHOENIX – Medications used in oncology clinical trials pose unique challenges in areas such as labeling, packaging, and administration, a US Department of Veterans Affairs (VA) pharmacist cautioned colleagues, and placebos have special needs too.

Even basic safety protections can be lacking when a drug is investigational, said Emily Hennes, PharmD, BCOP, clinical pharmacy specialist for research at William S. Middleton Memorial Veterans Hospital in Shorewood Hills, Wisconsin, in a presentation at the annual meeting of the Association of VA Hematology/Oncology.

“All of the safety features that we have come to know and love in dispensing commercial drugs are absent. There’s no Tall Man lettering, there's no color differentiation, and there's no barcoding, because these are not registered drugs," she said.

A 2017 report found that 81% of pharmacists surveyed indicated some level of concern regarding the safety risk in using  investigational drugs. At the same time, Hennes noted, the Joint Commission has mandated that pharmacists must control the storage, dispensing, labeling, and distribution of investigational medications.

Here are things to know about the use of investigational cancer drugs:

Drug Interactions Are Common

Hennes highlighted a 2023 study of medication reconciliation of 501 patients in 79 clinical trials that found alarming levels of drug interactions: 

• 360 clinically relevant drug-drug interactions were identified among 189 patients, including 158 therapies that were prohibited by protocols. Of these, 57.7% involved cytochrome P450 enzymes, which are involved in metabolism. 

• Reconciliation revealed that 35.2% of medications were not otherwise known or documented.

• A median of 2 previously unknown therapies per patient was discovered in 74% of patients.

• Alternative medicine products such as supplements and over-the-counter drugs were implicated in 60% of identified drug interactions.

• Only 41% of oncologists discussed alternative medicine use with patients, which Hennes attributed to “lack of familiarity with many alternative medicine products or insufficient training.”

To make things more complicated, “We sometimes don’t know the full pharmacokinetic and pharmacodynamic profile of an investigational agent,” she said. 

Naming and Labeling May Not Be Standard

Investigational products may not have genetic names and instead have an alphanumeric identifier such as INV54826 that can be quite similar to other products, she said. Investigational drugs may even go through name changes, forcing pharmacists to be alerted to protect patients. 

In addition, labeling may not be standardized. Drugs may arrive unlabeled, with the wrong volume and size, and lack of barcoding. In some cases, pharmacists choose to put new, patient-friendly labels on these products, Hennes said. 

Information Distribution is Key

“Something that comes up in our practice quite a bit is that there’s no standard drug reference regarding investigational drugs,” Hennes said. “Finding ways to get key information to staff at the point of care is really critical to make sure we’re able to safely treat our patients.”

Precautions May Be Needed to Maintain Blinding Protocols 

Hennes explained that pharmacists must use opaque brown bag covers to maintain blinding when parenteral products have distinctive colors. Lines may have to be covered too, which can create challenges during administration. 

“Pumps aren’t meant to run lines that are covered,” she said, which can lead to jams. “If you don’t do education with your point of care staff, it can cause a lot of confusion.” 

It’s also important for blinding purposes to keep an eye on how long it takes to prepare a treatment, she said. A study’s integrity, for example, could be violated if a complex investigational product takes an hour to equilibrate to room temperature and 20-30 minutes to prepare, while a placebo only requires “drawing a few mils of saline out of a bag and labeling it.”

Education for Patients Can Be Useful 

Hennes urged colleagues to remind patients to save investigational medication at the end of each cycle and return it to the clinic site for accountability.

She also suggested creating treatment calendars/reminders for patients and discussing adherence strategies with them. 

Hennes reported no disclosures. 

TOPLINE:

High-risk medications defined by the National Comprehensive Cancer Network (NCCN) and captured by the Geriatric Oncology Potentially Inappropriate Medication (GO-PIM) scale were prevalent in > one-third of veterans with solid and hematologic malignancies. Each additional GO-PIM was independently associated with higher risks for frailty at diagnosis, unplanned hospitalizations during follow-up, and death.

METHODOLOGY:

• Patients with cancer often use multiple chronic medications, raising risks for adverse events. Although several tools that identify PIMs have been developed that correlate with adverse cancer outcomes, their use is limited in busy oncology clinics. To improve implementation, researchers developed the GO-PIM scale using the NCCN’s list of high-risk medications.
• Researchers conducted a retrospective cohort study using data from the national Veterans Affairs Cancer Registry and electronic health records, which included 388,113 veterans newly diagnosed with solid or hematologic malignancies (median age, 69.3 years; 97.9% men; 76.1% non-Hispanic White and 17.3% Black individuals) between 2000 and 2022.
• They identified GO-PIMs using outpatient pharmacy records in the 90 days preceding cancer diagnosis. Each prescription for a specific GO-PIM was counted as one, including both individual drugs and drug classes listed in the GO-PIM scale.
• Study outcomes were frailty, hospitalizations, and overall survival. Baseline frailty at diagnosis was measured using the Veterans Affairs Frailty Index. The score ranged from 0 to 1, and higher scores indicated greater frailty. Patients were classified as nonfrail (score, ≤ 0.2), mildly frail (score, > 0.2 to 0.3), or moderate-to-severely frail (score, > 0.3).
• Lung (23.7%), prostate (21.5%), and gastrointestinal (20.5%) cancers were the most common, and the most frequent stages were IV (25.4%) and II (24.4%).

TAKEAWAY:

• Overall, 38.0% of veterans were prescribed ≥ 1 GO-PIMs at the time of cancer diagnosis, and the proportion increased to 56.1% among those with moderate-to-severe frailty.
• The most commonly prescribed classes of PIMs were selective serotonin reuptake inhibitors (SSRIs; 12.0%), opioids (10.4%), benzodiazepines (9.2%), and corticosteroids (9.2%). Among individual drugs, sertraline was the most common SSRI (4.3%), tramadol the most common opioid (5.3%), lorazepam the most common benzodiazepine (2.5%), and prednisone the most common corticosteroid (4.9%). Trends over time showed a steady increase in opioid prescriptions, peaking in 2014, followed by a subsequent decline, while prescriptions of benzodiazepines declined during the later years.
• After adjusting for age, cancer type and stage, and other covariates, each additional GO-PIM was associated with a 66% higher odds of mild or moderate-to-severe frailty at diagnosis (adjusted odds ratio, 1.66).
• After adjusting for frailty and covariates, each additional GO-PIM at diagnosis was associated with increased risks for unplanned hospitalizations and death (adjusted hazard ratios, 1.08 and 1.07, respectively). These associations remained stable in sensitivity analyses that restricted GO-PIMs to scheduled medications only, focused on patients who had initiated cancer treatment, and included only those aged ≥ 65 years.

IN PRACTICE:

“Whether prescribed for supportive oncology care or for coexisting medical conditions, high-risk medications identified as PIMs should be reviewed and optimized in patients with cancer,” the authors of the study wrote.

“GO-PIMs offers a streamlined, oncology-specific approach to identifying high-risk prescribing, and complements existing efforts to improve supportive care, especially for older, frail patients,” remarked Mostafa R. Mohamed, MBBCH, PhD, MSc, and Erika E. Ramsdale, MD, University of Rochester Medical Center, Rochester, New York in an invited commentary. “The next step lies in integrating tools such as GO-PIMs into everyday practice not only to flag high risk medications but also to support actionable changes in treatment planning and patient management, such as deprescribing,” they concluded.

SOURCE:

This study, led by Jennifer La, PhD, Harvard Medical School, Boston, was published online in Journal of the National Comprehensive Cancer Network.

LIMITATIONS:

Prescription chronicity before or after follow-up was not measured and actual medication adherence could not be confirmed. Residual confounding by comorbidity could have existed, and the cross-sectional nature of linking GO-PIMs with frailty might have limited causal inference. Additionally, prescriptions were measured within Veterans Affairs pharmacy data, potentially underestimating GO-PIM prevalence, and the predominantly male population limited generalizability to gynecologic cancers.

DISCLOSURES:

This study was supported by grants and rewards from the Veterans Affairs Office of Research and Development, Cooperative Studies Program, National Institutes of Health, and American Heart Association. Some authors declared serving as consultants or receiving grants and having other ties with various sources. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

High-risk medications defined by the National Comprehensive Cancer Network (NCCN) and captured by the Geriatric Oncology Potentially Inappropriate Medication (GO-PIM) scale were prevalent in > one-third of veterans with solid and hematologic malignancies. Each additional GO-PIM was independently associated with higher risks for frailty at diagnosis, unplanned hospitalizations during follow-up, and death.

METHODOLOGY:

• Patients with cancer often use multiple chronic medications, raising risks for adverse events. Although several tools that identify PIMs have been developed that correlate with adverse cancer outcomes, their use is limited in busy oncology clinics. To improve implementation, researchers developed the GO-PIM scale using the NCCN’s list of high-risk medications.
• Researchers conducted a retrospective cohort study using data from the national Veterans Affairs Cancer Registry and electronic health records, which included 388,113 veterans newly diagnosed with solid or hematologic malignancies (median age, 69.3 years; 97.9% men; 76.1% non-Hispanic White and 17.3% Black individuals) between 2000 and 2022.
• They identified GO-PIMs using outpatient pharmacy records in the 90 days preceding cancer diagnosis. Each prescription for a specific GO-PIM was counted as one, including both individual drugs and drug classes listed in the GO-PIM scale.
• Study outcomes were frailty, hospitalizations, and overall survival. Baseline frailty at diagnosis was measured using the Veterans Affairs Frailty Index. The score ranged from 0 to 1, and higher scores indicated greater frailty. Patients were classified as nonfrail (score, ≤ 0.2), mildly frail (score, > 0.2 to 0.3), or moderate-to-severely frail (score, > 0.3).
• Lung (23.7%), prostate (21.5%), and gastrointestinal (20.5%) cancers were the most common, and the most frequent stages were IV (25.4%) and II (24.4%).

TAKEAWAY:

• Overall, 38.0% of veterans were prescribed ≥ 1 GO-PIMs at the time of cancer diagnosis, and the proportion increased to 56.1% among those with moderate-to-severe frailty.
• The most commonly prescribed classes of PIMs were selective serotonin reuptake inhibitors (SSRIs; 12.0%), opioids (10.4%), benzodiazepines (9.2%), and corticosteroids (9.2%). Among individual drugs, sertraline was the most common SSRI (4.3%), tramadol the most common opioid (5.3%), lorazepam the most common benzodiazepine (2.5%), and prednisone the most common corticosteroid (4.9%). Trends over time showed a steady increase in opioid prescriptions, peaking in 2014, followed by a subsequent decline, while prescriptions of benzodiazepines declined during the later years.
• After adjusting for age, cancer type and stage, and other covariates, each additional GO-PIM was associated with a 66% higher odds of mild or moderate-to-severe frailty at diagnosis (adjusted odds ratio, 1.66).
• After adjusting for frailty and covariates, each additional GO-PIM at diagnosis was associated with increased risks for unplanned hospitalizations and death (adjusted hazard ratios, 1.08 and 1.07, respectively). These associations remained stable in sensitivity analyses that restricted GO-PIMs to scheduled medications only, focused on patients who had initiated cancer treatment, and included only those aged ≥ 65 years.

IN PRACTICE:

“Whether prescribed for supportive oncology care or for coexisting medical conditions, high-risk medications identified as PIMs should be reviewed and optimized in patients with cancer,” the authors of the study wrote.

“GO-PIMs offers a streamlined, oncology-specific approach to identifying high-risk prescribing, and complements existing efforts to improve supportive care, especially for older, frail patients,” remarked Mostafa R. Mohamed, MBBCH, PhD, MSc, and Erika E. Ramsdale, MD, University of Rochester Medical Center, Rochester, New York in an invited commentary. “The next step lies in integrating tools such as GO-PIMs into everyday practice not only to flag high risk medications but also to support actionable changes in treatment planning and patient management, such as deprescribing,” they concluded.

SOURCE:

This study, led by Jennifer La, PhD, Harvard Medical School, Boston, was published online in Journal of the National Comprehensive Cancer Network.

LIMITATIONS:

Prescription chronicity before or after follow-up was not measured and actual medication adherence could not be confirmed. Residual confounding by comorbidity could have existed, and the cross-sectional nature of linking GO-PIMs with frailty might have limited causal inference. Additionally, prescriptions were measured within Veterans Affairs pharmacy data, potentially underestimating GO-PIM prevalence, and the predominantly male population limited generalizability to gynecologic cancers.

DISCLOSURES:

This study was supported by grants and rewards from the Veterans Affairs Office of Research and Development, Cooperative Studies Program, National Institutes of Health, and American Heart Association. Some authors declared serving as consultants or receiving grants and having other ties with various sources. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

High-risk medications defined by the National Comprehensive Cancer Network (NCCN) and captured by the Geriatric Oncology Potentially Inappropriate Medication (GO-PIM) scale were prevalent in > one-third of veterans with solid and hematologic malignancies. Each additional GO-PIM was independently associated with higher risks for frailty at diagnosis, unplanned hospitalizations during follow-up, and death.

METHODOLOGY:

• Patients with cancer often use multiple chronic medications, raising risks for adverse events. Although several tools that identify PIMs have been developed that correlate with adverse cancer outcomes, their use is limited in busy oncology clinics. To improve implementation, researchers developed the GO-PIM scale using the NCCN’s list of high-risk medications.
• Researchers conducted a retrospective cohort study using data from the national Veterans Affairs Cancer Registry and electronic health records, which included 388,113 veterans newly diagnosed with solid or hematologic malignancies (median age, 69.3 years; 97.9% men; 76.1% non-Hispanic White and 17.3% Black individuals) between 2000 and 2022.
• They identified GO-PIMs using outpatient pharmacy records in the 90 days preceding cancer diagnosis. Each prescription for a specific GO-PIM was counted as one, including both individual drugs and drug classes listed in the GO-PIM scale.
• Study outcomes were frailty, hospitalizations, and overall survival. Baseline frailty at diagnosis was measured using the Veterans Affairs Frailty Index. The score ranged from 0 to 1, and higher scores indicated greater frailty. Patients were classified as nonfrail (score, ≤ 0.2), mildly frail (score, > 0.2 to 0.3), or moderate-to-severely frail (score, > 0.3).
• Lung (23.7%), prostate (21.5%), and gastrointestinal (20.5%) cancers were the most common, and the most frequent stages were IV (25.4%) and II (24.4%).

TAKEAWAY:

• Overall, 38.0% of veterans were prescribed ≥ 1 GO-PIMs at the time of cancer diagnosis, and the proportion increased to 56.1% among those with moderate-to-severe frailty.
• The most commonly prescribed classes of PIMs were selective serotonin reuptake inhibitors (SSRIs; 12.0%), opioids (10.4%), benzodiazepines (9.2%), and corticosteroids (9.2%). Among individual drugs, sertraline was the most common SSRI (4.3%), tramadol the most common opioid (5.3%), lorazepam the most common benzodiazepine (2.5%), and prednisone the most common corticosteroid (4.9%). Trends over time showed a steady increase in opioid prescriptions, peaking in 2014, followed by a subsequent decline, while prescriptions of benzodiazepines declined during the later years.
• After adjusting for age, cancer type and stage, and other covariates, each additional GO-PIM was associated with a 66% higher odds of mild or moderate-to-severe frailty at diagnosis (adjusted odds ratio, 1.66).
• After adjusting for frailty and covariates, each additional GO-PIM at diagnosis was associated with increased risks for unplanned hospitalizations and death (adjusted hazard ratios, 1.08 and 1.07, respectively). These associations remained stable in sensitivity analyses that restricted GO-PIMs to scheduled medications only, focused on patients who had initiated cancer treatment, and included only those aged ≥ 65 years.

IN PRACTICE:

“Whether prescribed for supportive oncology care or for coexisting medical conditions, high-risk medications identified as PIMs should be reviewed and optimized in patients with cancer,” the authors of the study wrote.

“GO-PIMs offers a streamlined, oncology-specific approach to identifying high-risk prescribing, and complements existing efforts to improve supportive care, especially for older, frail patients,” remarked Mostafa R. Mohamed, MBBCH, PhD, MSc, and Erika E. Ramsdale, MD, University of Rochester Medical Center, Rochester, New York in an invited commentary. “The next step lies in integrating tools such as GO-PIMs into everyday practice not only to flag high risk medications but also to support actionable changes in treatment planning and patient management, such as deprescribing,” they concluded.

SOURCE:

This study, led by Jennifer La, PhD, Harvard Medical School, Boston, was published online in Journal of the National Comprehensive Cancer Network.

LIMITATIONS:

Prescription chronicity before or after follow-up was not measured and actual medication adherence could not be confirmed. Residual confounding by comorbidity could have existed, and the cross-sectional nature of linking GO-PIMs with frailty might have limited causal inference. Additionally, prescriptions were measured within Veterans Affairs pharmacy data, potentially underestimating GO-PIM prevalence, and the predominantly male population limited generalizability to gynecologic cancers.

DISCLOSURES:

This study was supported by grants and rewards from the Veterans Affairs Office of Research and Development, Cooperative Studies Program, National Institutes of Health, and American Heart Association. Some authors declared serving as consultants or receiving grants and having other ties with various sources. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

About 1 in 10 veterans with early-stage cancer developed new persistent opioid use after curative‐intent surgery, though < 1% were diagnosed with opioid use disorder.

METHODOLOGY:

Although effective pain control during cancer treatment is vital, prescribing opioids in this context may contribute to unsafe, long-term use and related adverse outcomes. Veterans, who have higher-than-average rates of mental health and substance use disorders, may be at particular risk for adverse events from opioid use related to cancer treatment.

Researchers conducted a national retrospective cohort study of 9213 US veterans (98% men) with stage 0-III cancer who were opioid-naive and underwent curative-intent surgery at Veterans Affairs medical centers between 2015 and 2016. Prostate (n = 2594; 28%), colorectal (n = 2393; 26%), bladder (n = 2302; 25%), and lung (n = 1252; 14%) cancers were the most common.

Primary outcomes were the number of days of co-prescription of benzodiazepines and opioids (an indicator of unsafe opioid prescribing) and new persistent opioid use, defined as receiving ≥ 1 opioid prescription at 90-180 days postsurgery. Opioid‐related adverse effects, including opioid use disorder and opioid overdose, were also reported.

Overall, 6970 (76%) of the participants were prescribed opioids at some point during the baseline treatment period (30 days before through 14 days after surgery). The mean morphine milligram equivalent (MME) was 172.5.

 

TAKEAWAY:

Overall, 4% of patients received co-prescriptions of benzodiazepines and opioids. The mean number of days of coprescription rose in tandem with opioid doses during the treatment period: from 0.48 days in the lowest MME quartile to 2.1 days in the highest quartile (P < .0001).

Over 1 in 10 patients (10.6%) developed new persistent opioid use. Those in the highest MME quartile had a 1.6-fold greater risk of developing new persistent opioid use than those with no opioid exposure during the treatment period (hazard ratio [HR], 1.6; P < .001). The percentage of patients with opioid prescriptions did decline over the 13-month follow-up, but among those who continued on opioids, the daily MME remained stable (median, 20 for month 1 and 30 for month 12).

Treatment with adjuvant chemotherapy increased the risk for new persistent opioid use (HR, 1.5; 95% CI, 1.2-1.8; P < .001). Additional risk factors included having bladder, colorectal, lung, or other types of cancer (vs prostate cancer); stage I-III disease (vs stage 0); age 45-64 years (vs older); lower socioeconomic status; preoperative use of nonopioid pain medication; and a baseline history of anxiety, depression, or posttraumatic stress disorder.

Over 13 months, 72 patients (0.78%) developed opioid use disorder, 3 (0.03%) experienced nonoverdose adverse events, and no opioid overdose occurred.

 

IN PRACTICE:

“Although a cancer diagnosis, treatment, and associated pain syndromes will require specific pain management strategies,” the authors wrote, “efforts should be taken to mitigate long‐term opioid use and its potential adverse effects in this population. They added that “both system‐level changes that involve preoperative evaluation planning as well as increased knowledge, awareness, and education among providers and patients about the risk of long‐term opioid use can guide strategies for effective and safe pain management.”

SOURCE:

The study, led by Marilyn M. Schapira, MD, MPH, Center for Healthcare Evaluation, Research, and Promotion, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, was published online in Cancer.

LIMITATIONS:

Opioid prescriptions outside the Veterans Affairs system were not captured. The study was based on filled opioid prescriptions, and actual patient consumption was unknown. Outpatient methadone prescriptions were not included. The study also excluded patients with breast cancer, limiting generalizability.

DISCLOSURES:

The study was funded by grant from the Department of Veterans Affairs. One author reported consulting for Moderna and TriNetX. Another author reported consulting for Genetic Chemistry, Thyme Care, Biofourmis, Onc.Al, Credit Suisse, Main Street Health, ConcertAI, Medscape, and G1 Therapeutics. The other authors declared having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

TOPLINE:

About 1 in 10 veterans with early-stage cancer developed new persistent opioid use after curative‐intent surgery, though < 1% were diagnosed with opioid use disorder.

METHODOLOGY:

Although effective pain control during cancer treatment is vital, prescribing opioids in this context may contribute to unsafe, long-term use and related adverse outcomes. Veterans, who have higher-than-average rates of mental health and substance use disorders, may be at particular risk for adverse events from opioid use related to cancer treatment.

Researchers conducted a national retrospective cohort study of 9213 US veterans (98% men) with stage 0-III cancer who were opioid-naive and underwent curative-intent surgery at Veterans Affairs medical centers between 2015 and 2016. Prostate (n = 2594; 28%), colorectal (n = 2393; 26%), bladder (n = 2302; 25%), and lung (n = 1252; 14%) cancers were the most common.

Primary outcomes were the number of days of co-prescription of benzodiazepines and opioids (an indicator of unsafe opioid prescribing) and new persistent opioid use, defined as receiving ≥ 1 opioid prescription at 90-180 days postsurgery. Opioid‐related adverse effects, including opioid use disorder and opioid overdose, were also reported.

Overall, 6970 (76%) of the participants were prescribed opioids at some point during the baseline treatment period (30 days before through 14 days after surgery). The mean morphine milligram equivalent (MME) was 172.5.

 

TAKEAWAY:

Overall, 4% of patients received co-prescriptions of benzodiazepines and opioids. The mean number of days of coprescription rose in tandem with opioid doses during the treatment period: from 0.48 days in the lowest MME quartile to 2.1 days in the highest quartile (P < .0001).

Over 1 in 10 patients (10.6%) developed new persistent opioid use. Those in the highest MME quartile had a 1.6-fold greater risk of developing new persistent opioid use than those with no opioid exposure during the treatment period (hazard ratio [HR], 1.6; P < .001). The percentage of patients with opioid prescriptions did decline over the 13-month follow-up, but among those who continued on opioids, the daily MME remained stable (median, 20 for month 1 and 30 for month 12).

Treatment with adjuvant chemotherapy increased the risk for new persistent opioid use (HR, 1.5; 95% CI, 1.2-1.8; P < .001). Additional risk factors included having bladder, colorectal, lung, or other types of cancer (vs prostate cancer); stage I-III disease (vs stage 0); age 45-64 years (vs older); lower socioeconomic status; preoperative use of nonopioid pain medication; and a baseline history of anxiety, depression, or posttraumatic stress disorder.

Over 13 months, 72 patients (0.78%) developed opioid use disorder, 3 (0.03%) experienced nonoverdose adverse events, and no opioid overdose occurred.

 

IN PRACTICE:

“Although a cancer diagnosis, treatment, and associated pain syndromes will require specific pain management strategies,” the authors wrote, “efforts should be taken to mitigate long‐term opioid use and its potential adverse effects in this population. They added that “both system‐level changes that involve preoperative evaluation planning as well as increased knowledge, awareness, and education among providers and patients about the risk of long‐term opioid use can guide strategies for effective and safe pain management.”

SOURCE:

The study, led by Marilyn M. Schapira, MD, MPH, Center for Healthcare Evaluation, Research, and Promotion, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, was published online in Cancer.

LIMITATIONS:

Opioid prescriptions outside the Veterans Affairs system were not captured. The study was based on filled opioid prescriptions, and actual patient consumption was unknown. Outpatient methadone prescriptions were not included. The study also excluded patients with breast cancer, limiting generalizability.

DISCLOSURES:

The study was funded by grant from the Department of Veterans Affairs. One author reported consulting for Moderna and TriNetX. Another author reported consulting for Genetic Chemistry, Thyme Care, Biofourmis, Onc.Al, Credit Suisse, Main Street Health, ConcertAI, Medscape, and G1 Therapeutics. The other authors declared having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

TOPLINE:

About 1 in 10 veterans with early-stage cancer developed new persistent opioid use after curative‐intent surgery, though < 1% were diagnosed with opioid use disorder.

METHODOLOGY:

Although effective pain control during cancer treatment is vital, prescribing opioids in this context may contribute to unsafe, long-term use and related adverse outcomes. Veterans, who have higher-than-average rates of mental health and substance use disorders, may be at particular risk for adverse events from opioid use related to cancer treatment.

Researchers conducted a national retrospective cohort study of 9213 US veterans (98% men) with stage 0-III cancer who were opioid-naive and underwent curative-intent surgery at Veterans Affairs medical centers between 2015 and 2016. Prostate (n = 2594; 28%), colorectal (n = 2393; 26%), bladder (n = 2302; 25%), and lung (n = 1252; 14%) cancers were the most common.

Primary outcomes were the number of days of co-prescription of benzodiazepines and opioids (an indicator of unsafe opioid prescribing) and new persistent opioid use, defined as receiving ≥ 1 opioid prescription at 90-180 days postsurgery. Opioid‐related adverse effects, including opioid use disorder and opioid overdose, were also reported.

Overall, 6970 (76%) of the participants were prescribed opioids at some point during the baseline treatment period (30 days before through 14 days after surgery). The mean morphine milligram equivalent (MME) was 172.5.

 

TAKEAWAY:

Overall, 4% of patients received co-prescriptions of benzodiazepines and opioids. The mean number of days of coprescription rose in tandem with opioid doses during the treatment period: from 0.48 days in the lowest MME quartile to 2.1 days in the highest quartile (P < .0001).

Over 1 in 10 patients (10.6%) developed new persistent opioid use. Those in the highest MME quartile had a 1.6-fold greater risk of developing new persistent opioid use than those with no opioid exposure during the treatment period (hazard ratio [HR], 1.6; P < .001). The percentage of patients with opioid prescriptions did decline over the 13-month follow-up, but among those who continued on opioids, the daily MME remained stable (median, 20 for month 1 and 30 for month 12).

Treatment with adjuvant chemotherapy increased the risk for new persistent opioid use (HR, 1.5; 95% CI, 1.2-1.8; P < .001). Additional risk factors included having bladder, colorectal, lung, or other types of cancer (vs prostate cancer); stage I-III disease (vs stage 0); age 45-64 years (vs older); lower socioeconomic status; preoperative use of nonopioid pain medication; and a baseline history of anxiety, depression, or posttraumatic stress disorder.

Over 13 months, 72 patients (0.78%) developed opioid use disorder, 3 (0.03%) experienced nonoverdose adverse events, and no opioid overdose occurred.

 

IN PRACTICE:

“Although a cancer diagnosis, treatment, and associated pain syndromes will require specific pain management strategies,” the authors wrote, “efforts should be taken to mitigate long‐term opioid use and its potential adverse effects in this population. They added that “both system‐level changes that involve preoperative evaluation planning as well as increased knowledge, awareness, and education among providers and patients about the risk of long‐term opioid use can guide strategies for effective and safe pain management.”

SOURCE:

The study, led by Marilyn M. Schapira, MD, MPH, Center for Healthcare Evaluation, Research, and Promotion, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, was published online in Cancer.

LIMITATIONS:

Opioid prescriptions outside the Veterans Affairs system were not captured. The study was based on filled opioid prescriptions, and actual patient consumption was unknown. Outpatient methadone prescriptions were not included. The study also excluded patients with breast cancer, limiting generalizability.

DISCLOSURES:

The study was funded by grant from the Department of Veterans Affairs. One author reported consulting for Moderna and TriNetX. Another author reported consulting for Genetic Chemistry, Thyme Care, Biofourmis, Onc.Al, Credit Suisse, Main Street Health, ConcertAI, Medscape, and G1 Therapeutics. The other authors declared having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.