Tuesday, November 30 | 8:00 a.m.-9:00 a.m. | SSBR05-6 | Room TBA
In this presentation, researchers will talk about their findings on distributing volumetric breast density across screening populations around the world for measuring risk.Ioannis Sechopoulos, PhD, from Radboud University Medical Center in the Netherlands will present the team's findings, which showed that the same criteria for categorizing breast density based on volumetric breast density can be used worldwide when breast thickness is considered.
Data from 780,648 screened women ages 50 and older were included. The screening populations were based in the U.S., the Netherlands, Norway, Brazil, Malaysia, and Greece.
The team found that breasts of comparable thickness have similar volumetric breast density distributions across populations, suggesting that a single population-based model may have use for applications such as radiation dose estimates or risk modeling.
Find out more by attending this morning session.














![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)





