Explainable WinDWS-SwinNet: Optimizing Ultrasound Deep Radiomics for Multipathology Breast Lesion Classification.

RATIONALE AND OBJECTIVES: Timely and accurate characterization of breast lesions into different pathological classes through noninvasive imaging techniques is crucial for early cancer diagnosis and effective patient management. This study aims to develop a novel, explainable attention-based deep network to analyze radiomic patterns for predicting heterogeneous breast cancer types using ultrasound imaging. MATERIALS AND METHODS: A multicentric retrospective dataset comprises brightness-mode ultrasound images of breast tissue collected across five different centers/databases. A total of 3256 images, having biopsy-proven 2068 benign and 1188 malignant lesions, were analyzed. A lightweight and computationally scalable network, WinDWS-SwinNet, is developed for binary and multipathology breast cancer classification. An optimized image preprocessing algorithm is proposed to enhance ultrasound radiomic features, thereby improving data generalizability and diagnostic accuracy. RESULTS: The WinDWS-SwinNet architecture demonstrated superior performance in breast cancer classification, achieving an area under the curve of 0.96, an overall test accuracy of 95.79%, and robust class-wise results with 95.11% precision and 98.51% sensitivity for benign tumors and 97.13% precision and 90.86% sensitivity for malignant tumors. For multipathology classification, the model achieved a test accuracy of 93%, with class-wise accuracies ranging from 94% to 99.5%. External validation on the independent unseen dataset resulted in an accuracy of 95.36%, highlighting the robustness and generalizability of the proposed model beyond the training data. CONCLUSION: The developed methodology shows promising results in characterizing breast lesions into pathological subtypes noninvasively, effectively mimicking real-world diagnostic workflows, thereby supporting radiologists in reducing diagnostic uncertainty and enhancing confidence in clinical decision-making.