CEM descriptors (combination of those in the mammography and breast MRI BI-RADS atlas sections, respectively regarding morphology and contrast enhancement behavior) will be summarized. Combinations of these descriptors and associated clinical performance will be presented for masses and calcifications, as derived from a multi-case, multi-reader study.

The presenter will share and discuss initial results of a study investigating CEM capability to guide indication and extension of biopsy areas in cases displaying suspicious calcifications without associated mammographic mass.

As breast MRI, CEM may enhance some benign lesions. The presentation will share the results from research and associated clinical study on the possibility to use quantitative contrast enhancement level on CEM as an additional decision factor in whether a lesion requires immediate biopsy or not.

After a refresher on the The American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS), the addition of a dedicated CEM section (along with those for mammography, ultrasound and MRI) aimed to standardize CEM interpretation and reporting will be presented and discussed.

An exchange between a data scientist and a radiologist will refresh the audience on the principles, challenges and potential steps required for the deployment of AI in breast imaging.

The evaluation work performed on the Koios product and which will be reported at the Breast Academy.

Clinical experience with AI in breast MRI and impact on diagnostic confidence will be presented and illustrated with relevant cases.

The presenter will share and illustrate clinical and workflow experience from the routine use of a deep learning CAD in a large private practice.