Multi-institutional classification of lesions in breast MRI: a comparison of one-stage and two-stage methods

PurposeBreast magnetic resonance imaging (MRI) is widely used for diagnostic imaging in breast cancer, creating a need for automated image analysis to assist early detection and diagnosis. We approach the task of lesion suspiciousness classification in breast MRI.ApproachUsing a multi-centric data collection of 3792 patients, we compare a two-stage classification model based on nnU-Net and a three-dimensional (3D) Swin Transformer with the one-stage classifiers nnDetection and RadYOLO, our 3D extension of YOLO11.ResultsIn a fivefold leave-sites-out cross-validation scheme, the mean (standard deviation) across folds of the lesion classification area under the receiver operating curve (ROC-AUC) is 0.754 (0.086) for the two-stage model, 0.693 (0.108) for nnDetection, 0.737 (0.074) for RadYOLO, and 0.755 (0.064) for RadYOLO with a separate classification output.ConclusionsAlthough classification performance does not differ conclusively across methods, RadYOLO achieves 3 to 10 times shorter inference times.