A new mammography-based AI model predicts 10-year breast cancer risk more accurately than established clinical and AI models.
Key Details
- 1Developed by Karolinska Institute, the AI model uses digital mammogram images to assess long-term breast cancer risk.
- 2Tested in 8,696 women (KARMA and Olmsted cohorts) with 1,633 incident breast cancers.
- 3Model achieved AUC of 0.72 for invasive cancers in both KARMA and Olmsted cohorts, and 0.70 in the EMBED cohort.
- 4Outperformed the Mirai model (AUC 0.64 in Olmsted/KARMA; 0.63 in EMBED) and clinical risk models in all cohorts.
- 5In the KARMA cohort, the top 10% risk group identified by the AI model captured 33% of cancers, higher than other models.
- 6The model is being prepared for further validation in broader populations and potential integration into risk-stratified prevention programs.
Why It Matters
Improved long-term breast cancer risk prediction could enable more precise, personalized screening and prevention strategies. This model could shift mammography from a purely diagnostic tool to a critical predictor of future disease, enhancing patient care and outcomes.
Source
AuntMinnie
Related News
•AuntMinnie
AI Tool Enhances Quality and Efficiency of MSK Ultrasound for Novices
A China-developed MSK ultrasound AI tool significantly improves scanning quality and efficiency for novice sonologists.
•AuntMinnie
AI Systems Predict Breast Cancer Risk Years Before Diagnosis
AI-enabled mammography systems can flag individuals at increased risk for breast cancer up to six years ahead of diagnosis, outperforming traditional risk measures.
•AuntMinnie
AI Models Show High Sensitivity but Moderate Specificity for Lung Nodule Classification on CT
AI shows high sensitivity but only moderate specificity in classifying lung cancer nodules on CT, indicating a role as a rule-out adjunct.