An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population

Abstract Objectives Current deep learning models for early breast cancer lack interpretability and multimodal integration, limiting their clinical acceptance. This study aimed to develop and evaluate a deep learning system that automates breast ultrasound evaluation to support early breast cancer detection in clinical assessment. Materials and methods We developed BrcaDetect, which integrates ultrasound image-based deep learning predictions, Breast Imaging Reporting and Data System (BI-RADS) assessments, and demographic factors. A total of 24,762 ultrasound images from 3048 women across five hospitals were retrospectively collected. The model was trained and internally validated using 19,340 images from 2399 patients at three tertiary hospitals between January 2017 and December 2020, and externally validated using 5422 images from 649 women at two additional hospitals between January 2021 and August 2023. All lesions were confirmed by biopsy or 3-year follow-up. Model performance and its impact on the diagnostic accuracy of five radiologists were evaluated. Results BrcaDetect outperformed image-based deep learning and demographic model, achieving an area under the curve (AUC) of 0.989 (95% confidence interval (CI): 0.979–0.999), 0.851 (95% CI: 0.819–0.884), and 0.826 (95% CI: 0.804–0.848), with corresponding sensitivities of 98.8%, 93.0%, and 71.8%. In the reader study, radiologists assisted by BrcaDetect achieved significantly higher diagnostic accuracy than unassisted reading (0.977 95% CI: 0.967–0.986 vs. 0.919 95% CI: 0.900–0.935; p < 0.001). Conclusions As an image‑level decision-support model, BrcaDetect was associated with improved radiologists’ performance and interpretability under controlled reading conditions, reducing false positives and demonstrating proof-of-concept for decision support in clinical assessment workflows. Key Points Current deep learning models for early breast cancer lack interpretability and multimodal integration, severely limiting their clinical acceptance in practice. In a retrospective study, BrcaDetect outperformed single‑modality models across three cohorts and provided strong interpretability via Grad‑CAM and Shapley values. This article addresses the lack of interpretability and multimodal integration in deep learning models for early breast cancer, presenting BrcaDetect: explainable predictions via Grad-CAM and Shapley values may reduce diagnostic uncertainty and support clinical workflow integration as a proof-of-concept.