Combining ensemble AI models with reads from breast radiologists of mixed experience levels can help health systems consistently diagnose malignant architectural distortion on mammography.

Researchers have developed a prediction model that takes into account both imaging (post-NAC breast MRI) and clinical-pathologic features when forecasting patients' overall survival.

Of the 51 plans, just 31% were consistent with the USPSTF recommendations pertaining to the starting age and frequency of screening women who are at average risk of developing breast cancer.

Experts have developed an artificial intelligence model that can estimate gestational age with accuracy that rivals that of formally trained sonographers completing fetal biometry scans. 

AB-MRI is a cost-effective means of screening women with dense breast tissue for breast cancer—as long as the per-exam costs don’t top 82% of what would have been spent to perform full-protocol breast MRI.

New research shows that persistent lymph node swelling observed on imaging can persist beyond 12 weeks post-vaccination for up to 50% of women.

In addition to O-RADS external validation, experts also sought to determine if incorporating acoustic shadowing as a benign finding would improve diagnostic performance. 

Researchers reported that the artificial intelligence system was able to interpret more than 114,000 screening mammograms using a reading protocol with high sensitivity and specificity.