Researchers at the Breast Cancer Surveillance Consortium (BCSC) have received a $17 million program project grant renewal from the U.S. National Cancer Institute (NCI) to study the efficacy of different breast cancer screening and surveillance strategies.
The evaluation, co-led by Diana Miglioretti, PhD, a researcher and professor of biostatistics at the University of California, Davis, will include digital mammography, digital breast tomosynthesis, and breast MRI. The grant renewal expands prior research by evaluating surveillance imaging of breast cancer survivors and the screening of women without a history of breast cancer.
The ultimate goal is to tailor each woman's screening regimen to her risk of screening outcomes based on family history, breast density, and other risk factors, and consider her personal preferences around balancing the potential benefits and harms of screening.











![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=112&q=70&w=112)






