Breast cancer survivors who are overweight have a higher risk of developing second primary cancers, according to results from a study conducted by Kaiser Permanente researchers that were published April 5 in the Journal of the National Cancer Institute.
Obesity is associated with an increased risk of several types of cancer, with an estimated 55% of all cancers in women occurring in those who are overweight or obese.
In a study of 6,481 women led by Heather Feigelson, PhD, from the Kaiser Permanente Colorado Institute for Health Research, 822 (12.7%) developed a second cancer. The majority of women were overweight (33.4%) or obese (33.8%) at the time of their initial diagnosis.
The mean age at initial breast cancer diagnosis was 61 years, and most (82.2%) of the cohort was white. Black women comprised a small percentage of the cases but were more likely to be obese. Of the Black women in the cohort, 50.9% were obese, compared with 33.6% of the white women.
The outcomes evaluated included the following: all second cancers, obesity-related second cancers, any second breast cancer, and estrogen receptor-positive second breast cancers. Obesity-related cancer includes colorectal, uterine, ovarian, and pancreatic cancer.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
