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Artificial Intelligence Shows Promise in Detecting Missed Interval Breast Cancer on Screening Mammograms
TOPLINE:
An artificial intelligence (AI) system flagged high-risk areas on mammograms for potentially missed interval breast cancers (IBCs), which radiologists had also retrospectively identified as abnormal. Moreover, the AI detected a substantial number of IBCs that manual review had overlooked.
METHODOLOGY:
- Researchers conducted a retrospective analysis of 119 IBC screening mammograms of women (mean age, 57.3 years) with a high breast density (Breast Imaging Reporting and Data System [BI-RADS] c/d, 63.0%) using data retrieved from Cancer Registries of Eastern Switzerland and Grisons-Glarus databases.
- A recorded tumour was classified as IBC when an invasive or in situ BC was diagnosed within 24 months after a normal screening mammogram.
- Three radiologists retrospectively assessed the mammograms for visible signs of BC, which were then classified as either potentially missed IBCs or IBCs without retrospective abnormalities on the basis of consensus conference recommendations of radiologists.
- An AI system generated two scores (a scale of 0 to 100): a case score reflecting the likelihood that the mammogram currently harbours cancer and a risk score estimating the probability of a BC diagnosis within 2 years.
TAKEAWAY:
- Radiologists classified 68.9% of IBCs as those having no retrospective abnormalities and assigned significantly higher BI-RADS scores to the remaining 31.1% of potentially missed IBCs (P < .05).
- Potentially missed IBCs received significantly higher AI case scores (mean, 54.1 vs 23.1; P < .05) and were assigned to a higher risk category (48.7% vs 14.6%; P < .05) than IBCs without retrospective abnormalities.
- Of all IBC cases, 46.2% received an AI case score > 25, 25.2% scored > 50, and 13.4% scored > 75.
- Potentially missed IBCs scored widely between low and high risk and case scores, whereas IBCs without retrospective abnormalities scored low case and risk scores. Specifically, 73.0% of potentially missed IBCs vs 34.1% of IBCs without retrospective abnormalities had case scores > 25, 51.4% vs 13.4% had case scores > 50, and 29.7% vs 6.1% had case scores > 75.
IN PRACTICE:
“Our research highlights that an AI system can identify BC signs in relevant portions of IBC screening mammograms and thus potentially reduce the number of IBCs in an MSP [mammography screening program] that currently does not utilize an AI system,” the authors of the study concluded, adding that “it can identify some IBCs that are not visible to humans (IBCs without retrospective abnormalities).”
SOURCE:
This study was led by Jonas Subelack, Chair of Health Economics, Policy and Management, School of Medicine, University of St. Gallen, St. Gallen, Switzerland. It was published online in European Radiology.
LIMITATIONS:
The retrospective study design inherently limited causal conclusions. Without access to diagnostic mammograms or the detailed position of BC, researchers could not evaluate whether AI-marked lesions corresponded to later detected BCs.
DISCLOSURES:
This research was funded by the Cancer League of Eastern Switzerland. One author reported receiving consulting and speaker fees from iCAD.
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:
An artificial intelligence (AI) system flagged high-risk areas on mammograms for potentially missed interval breast cancers (IBCs), which radiologists had also retrospectively identified as abnormal. Moreover, the AI detected a substantial number of IBCs that manual review had overlooked.
METHODOLOGY:
- Researchers conducted a retrospective analysis of 119 IBC screening mammograms of women (mean age, 57.3 years) with a high breast density (Breast Imaging Reporting and Data System [BI-RADS] c/d, 63.0%) using data retrieved from Cancer Registries of Eastern Switzerland and Grisons-Glarus databases.
- A recorded tumour was classified as IBC when an invasive or in situ BC was diagnosed within 24 months after a normal screening mammogram.
- Three radiologists retrospectively assessed the mammograms for visible signs of BC, which were then classified as either potentially missed IBCs or IBCs without retrospective abnormalities on the basis of consensus conference recommendations of radiologists.
- An AI system generated two scores (a scale of 0 to 100): a case score reflecting the likelihood that the mammogram currently harbours cancer and a risk score estimating the probability of a BC diagnosis within 2 years.
TAKEAWAY:
- Radiologists classified 68.9% of IBCs as those having no retrospective abnormalities and assigned significantly higher BI-RADS scores to the remaining 31.1% of potentially missed IBCs (P < .05).
- Potentially missed IBCs received significantly higher AI case scores (mean, 54.1 vs 23.1; P < .05) and were assigned to a higher risk category (48.7% vs 14.6%; P < .05) than IBCs without retrospective abnormalities.
- Of all IBC cases, 46.2% received an AI case score > 25, 25.2% scored > 50, and 13.4% scored > 75.
- Potentially missed IBCs scored widely between low and high risk and case scores, whereas IBCs without retrospective abnormalities scored low case and risk scores. Specifically, 73.0% of potentially missed IBCs vs 34.1% of IBCs without retrospective abnormalities had case scores > 25, 51.4% vs 13.4% had case scores > 50, and 29.7% vs 6.1% had case scores > 75.
IN PRACTICE:
“Our research highlights that an AI system can identify BC signs in relevant portions of IBC screening mammograms and thus potentially reduce the number of IBCs in an MSP [mammography screening program] that currently does not utilize an AI system,” the authors of the study concluded, adding that “it can identify some IBCs that are not visible to humans (IBCs without retrospective abnormalities).”
SOURCE:
This study was led by Jonas Subelack, Chair of Health Economics, Policy and Management, School of Medicine, University of St. Gallen, St. Gallen, Switzerland. It was published online in European Radiology.
LIMITATIONS:
The retrospective study design inherently limited causal conclusions. Without access to diagnostic mammograms or the detailed position of BC, researchers could not evaluate whether AI-marked lesions corresponded to later detected BCs.
DISCLOSURES:
This research was funded by the Cancer League of Eastern Switzerland. One author reported receiving consulting and speaker fees from iCAD.
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:
An artificial intelligence (AI) system flagged high-risk areas on mammograms for potentially missed interval breast cancers (IBCs), which radiologists had also retrospectively identified as abnormal. Moreover, the AI detected a substantial number of IBCs that manual review had overlooked.
METHODOLOGY:
- Researchers conducted a retrospective analysis of 119 IBC screening mammograms of women (mean age, 57.3 years) with a high breast density (Breast Imaging Reporting and Data System [BI-RADS] c/d, 63.0%) using data retrieved from Cancer Registries of Eastern Switzerland and Grisons-Glarus databases.
- A recorded tumour was classified as IBC when an invasive or in situ BC was diagnosed within 24 months after a normal screening mammogram.
- Three radiologists retrospectively assessed the mammograms for visible signs of BC, which were then classified as either potentially missed IBCs or IBCs without retrospective abnormalities on the basis of consensus conference recommendations of radiologists.
- An AI system generated two scores (a scale of 0 to 100): a case score reflecting the likelihood that the mammogram currently harbours cancer and a risk score estimating the probability of a BC diagnosis within 2 years.
TAKEAWAY:
- Radiologists classified 68.9% of IBCs as those having no retrospective abnormalities and assigned significantly higher BI-RADS scores to the remaining 31.1% of potentially missed IBCs (P < .05).
- Potentially missed IBCs received significantly higher AI case scores (mean, 54.1 vs 23.1; P < .05) and were assigned to a higher risk category (48.7% vs 14.6%; P < .05) than IBCs without retrospective abnormalities.
- Of all IBC cases, 46.2% received an AI case score > 25, 25.2% scored > 50, and 13.4% scored > 75.
- Potentially missed IBCs scored widely between low and high risk and case scores, whereas IBCs without retrospective abnormalities scored low case and risk scores. Specifically, 73.0% of potentially missed IBCs vs 34.1% of IBCs without retrospective abnormalities had case scores > 25, 51.4% vs 13.4% had case scores > 50, and 29.7% vs 6.1% had case scores > 75.
IN PRACTICE:
“Our research highlights that an AI system can identify BC signs in relevant portions of IBC screening mammograms and thus potentially reduce the number of IBCs in an MSP [mammography screening program] that currently does not utilize an AI system,” the authors of the study concluded, adding that “it can identify some IBCs that are not visible to humans (IBCs without retrospective abnormalities).”
SOURCE:
This study was led by Jonas Subelack, Chair of Health Economics, Policy and Management, School of Medicine, University of St. Gallen, St. Gallen, Switzerland. It was published online in European Radiology.
LIMITATIONS:
The retrospective study design inherently limited causal conclusions. Without access to diagnostic mammograms or the detailed position of BC, researchers could not evaluate whether AI-marked lesions corresponded to later detected BCs.
DISCLOSURES:
This research was funded by the Cancer League of Eastern Switzerland. One author reported receiving consulting and speaker fees from iCAD.
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.
Older Patients With Breast Cancer Face Inconsistent Bone Health Management Across Centres
TOPLINE:
Bone health management for older women with breast cancer receiving aromatase inhibitors (AIs) varied substantially across 5 UK hospitals. Despite the higher risk for fractures, women aged > 80 years were less likely to receive DEXA scans or bisphosphonates, highlighting the urgent need for standardised bone monitoring and treatment in frail older patients.
METHODOLOGY:
- This secondary analysis of the multicentre Age Gap study included 529 women (age ≥ 70 years) with oestrogen receptor-positive early breast cancer who received AIs, either as primary or adjuvant treatment, at five hospitals in the UK.
- Researchers collected comprehensive data including the type of endocrine therapy, DEXA scan results, bisphosphonate usage, calcium and vitamin D supplementation, and the incidence of fractures during or after AI therapy.
- Frailty was assessed using a modified Rockwood Frailty Index, with scores being calculated across 75 variables to categorise patients as robust (< 0.08), prefrail (0.08-0.25), or frail (> 0.25).
TAKEAWAY:
- Overall, 67% of patients had baseline DEXA scans. Of these, 42% were osteopenic and 18% osteoporotic. Scans were more common in 70- to 79-year-olds than in those aged ≥ 80 years and in women undergoing surgery than in those undergoing primary endocrine therapy, with marked variation across centres (P < .001 for all).
- Among patients receiving AI therapy, 43% were prescribed bisphosphonates, especially those who had surgery (hazard ratio [HR], 1.36; P = .04) and those aged 70-79 years (HR, 1.31; P = .02); 33% had vitamin D plus calcium along with bisphosphonates.
- During follow-up, 23% of patients had fractures, with significant variation across centres (P = .02), and 38% of these patients had received prior bisphosphonates.
- Although 94% of patients were frail or prefrail, frailty did not correlate with baseline hip (P = .10) or spine (P = .89) T scores. Bisphosphonates plus AIs were prescribed in 70% of nonfrail participants vs 43% of prefrail and 47% of frail participants (P = .02).
IN PRACTICE:
“Patient’s age and general health influence bone health decision making, with older and frailer patients often receiving non-standard care. Despite national and international recommendations, there is still wide variation in bone health management, highlighting the need for further education and standardised bone health care in older women with breast cancer,” the authors wrote.
SOURCE:
This study was led by Elisavet Theodoulou, University of Sheffield, Sheffield, England. It was published online, in the Journal of Geriatric Oncology.
LIMITATIONS:
The study’s inclusion of only 5 hospital sites limited the ability to draw broader conclusions about bone health management practices across a wider range of centres. Additionally, the interpretation of the results was complicated by the introduction of adjuvant bisphosphonates during the study period, making the cohort unstable in terms of bisphosphonate usage indications.
DISCLOSURES:
The Age Gap study was supported by the National Institute for Health and Care Research Programme Grants for Applied Research. The 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:
Bone health management for older women with breast cancer receiving aromatase inhibitors (AIs) varied substantially across 5 UK hospitals. Despite the higher risk for fractures, women aged > 80 years were less likely to receive DEXA scans or bisphosphonates, highlighting the urgent need for standardised bone monitoring and treatment in frail older patients.
METHODOLOGY:
- This secondary analysis of the multicentre Age Gap study included 529 women (age ≥ 70 years) with oestrogen receptor-positive early breast cancer who received AIs, either as primary or adjuvant treatment, at five hospitals in the UK.
- Researchers collected comprehensive data including the type of endocrine therapy, DEXA scan results, bisphosphonate usage, calcium and vitamin D supplementation, and the incidence of fractures during or after AI therapy.
- Frailty was assessed using a modified Rockwood Frailty Index, with scores being calculated across 75 variables to categorise patients as robust (< 0.08), prefrail (0.08-0.25), or frail (> 0.25).
TAKEAWAY:
- Overall, 67% of patients had baseline DEXA scans. Of these, 42% were osteopenic and 18% osteoporotic. Scans were more common in 70- to 79-year-olds than in those aged ≥ 80 years and in women undergoing surgery than in those undergoing primary endocrine therapy, with marked variation across centres (P < .001 for all).
- Among patients receiving AI therapy, 43% were prescribed bisphosphonates, especially those who had surgery (hazard ratio [HR], 1.36; P = .04) and those aged 70-79 years (HR, 1.31; P = .02); 33% had vitamin D plus calcium along with bisphosphonates.
- During follow-up, 23% of patients had fractures, with significant variation across centres (P = .02), and 38% of these patients had received prior bisphosphonates.
- Although 94% of patients were frail or prefrail, frailty did not correlate with baseline hip (P = .10) or spine (P = .89) T scores. Bisphosphonates plus AIs were prescribed in 70% of nonfrail participants vs 43% of prefrail and 47% of frail participants (P = .02).
IN PRACTICE:
“Patient’s age and general health influence bone health decision making, with older and frailer patients often receiving non-standard care. Despite national and international recommendations, there is still wide variation in bone health management, highlighting the need for further education and standardised bone health care in older women with breast cancer,” the authors wrote.
SOURCE:
This study was led by Elisavet Theodoulou, University of Sheffield, Sheffield, England. It was published online, in the Journal of Geriatric Oncology.
LIMITATIONS:
The study’s inclusion of only 5 hospital sites limited the ability to draw broader conclusions about bone health management practices across a wider range of centres. Additionally, the interpretation of the results was complicated by the introduction of adjuvant bisphosphonates during the study period, making the cohort unstable in terms of bisphosphonate usage indications.
DISCLOSURES:
The Age Gap study was supported by the National Institute for Health and Care Research Programme Grants for Applied Research. The 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:
Bone health management for older women with breast cancer receiving aromatase inhibitors (AIs) varied substantially across 5 UK hospitals. Despite the higher risk for fractures, women aged > 80 years were less likely to receive DEXA scans or bisphosphonates, highlighting the urgent need for standardised bone monitoring and treatment in frail older patients.
METHODOLOGY:
- This secondary analysis of the multicentre Age Gap study included 529 women (age ≥ 70 years) with oestrogen receptor-positive early breast cancer who received AIs, either as primary or adjuvant treatment, at five hospitals in the UK.
- Researchers collected comprehensive data including the type of endocrine therapy, DEXA scan results, bisphosphonate usage, calcium and vitamin D supplementation, and the incidence of fractures during or after AI therapy.
- Frailty was assessed using a modified Rockwood Frailty Index, with scores being calculated across 75 variables to categorise patients as robust (< 0.08), prefrail (0.08-0.25), or frail (> 0.25).
TAKEAWAY:
- Overall, 67% of patients had baseline DEXA scans. Of these, 42% were osteopenic and 18% osteoporotic. Scans were more common in 70- to 79-year-olds than in those aged ≥ 80 years and in women undergoing surgery than in those undergoing primary endocrine therapy, with marked variation across centres (P < .001 for all).
- Among patients receiving AI therapy, 43% were prescribed bisphosphonates, especially those who had surgery (hazard ratio [HR], 1.36; P = .04) and those aged 70-79 years (HR, 1.31; P = .02); 33% had vitamin D plus calcium along with bisphosphonates.
- During follow-up, 23% of patients had fractures, with significant variation across centres (P = .02), and 38% of these patients had received prior bisphosphonates.
- Although 94% of patients were frail or prefrail, frailty did not correlate with baseline hip (P = .10) or spine (P = .89) T scores. Bisphosphonates plus AIs were prescribed in 70% of nonfrail participants vs 43% of prefrail and 47% of frail participants (P = .02).
IN PRACTICE:
“Patient’s age and general health influence bone health decision making, with older and frailer patients often receiving non-standard care. Despite national and international recommendations, there is still wide variation in bone health management, highlighting the need for further education and standardised bone health care in older women with breast cancer,” the authors wrote.
SOURCE:
This study was led by Elisavet Theodoulou, University of Sheffield, Sheffield, England. It was published online, in the Journal of Geriatric Oncology.
LIMITATIONS:
The study’s inclusion of only 5 hospital sites limited the ability to draw broader conclusions about bone health management practices across a wider range of centres. Additionally, the interpretation of the results was complicated by the introduction of adjuvant bisphosphonates during the study period, making the cohort unstable in terms of bisphosphonate usage indications.
DISCLOSURES:
The Age Gap study was supported by the National Institute for Health and Care Research Programme Grants for Applied Research. The 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.