Providers screening women with dense breasts and benign breast disease should consider individualized mammogram protocols for these patients, authors of the study suggested.

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.

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.

The findings could help clinicians manage patients’ treatment when the origin of axillary lymphadenopathy is of concern, experts suggested. 

The DBT-based combined radiomic nomogram achieved a specificity of nearly 95% when predicting lymphovascular invasion, which was higher than other clinical predictive models. 

The downward trend in annual mammography adherence should serve as a call to action for new processes to engage breast cancer survivors, physicians urged.