Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer
Daqu Zhang, Looket Dihge, Pär‐Ola Bendahl, Ida Arvidsson, Magnus Dustler, Julia Ellbrant, Kim Gulis, Malin Hjärtström, Mattias Ohlsson, Cornelia Rejmer, David Schmidt, Sophia Zackrisson, Patrik Edén, Lisa Rydén
Abstract
With the shift toward de-escalating surgery in breast cancer, prediction models incorporating imaging can reassess the need for surgical axillary staging. This study employed advancements in deep learning to comprehensively evaluate routine mammograms for preoperative lymph node metastasis prediction. Mammograms and clinicopathological data from 1265 cN0 T1-T2 breast cancer patients (primary surgery, no neoadjuvant therapy) were retrospectively collected from three Swedish institutions. Compared to models using only clinical variables, incorporating full-breast mammograms with preoperative clinical variables improved the ROC AUC from 0.690 to 0.774 (improvement: 0.001-0.154) in the independent test set. The combined model showed good calibration and, at sensitivity ≥90%, achieved a significantly better net benefit, and a sentinel lymph node biopsy reduction rate of 41.7% (13.0-62.6%). Our findings suggest that routine mammograms, particularly full-breast images, can enhance preoperative nodal status prediction. They may substitute key predictors such as pathological tumor size and multifocality, aiding patient stratification before surgery.