Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy

Objectives Accurate assessment of NAC efficacy is crucial for determining appropriate surgical strategies and guiding the extent of surgical resection in breast cancer. Therefore, this study aimed to design an integrated predictive model combining ultrasound imaging, deep learning features, and clinical characteristics to predict pCR in breast cancer patients undergoing NAC. Methods A retrospective study was conducted, including 643 pathologically confirmed breast cancer patients who underwent NAC between January 2022 to February 2024 from two institutions (Center 1: 372 cases; Center 2: 271 cases). Ultrasound images before and after NAC were collected for each patient. A total of 2,920 radiomics features and 4,096 deep learning features were extracted from the ultrasound images. Multiple machine learning algorithms were employed to model and validate the diagnostic performance of different types of features. Finally, clinical data, radiomics, and deep learning features were integrated to form a fusion model, which was evaluated using receiver operating characteristic (ROC) analysis. Results The combined model achieved the highest predictive performance for pathological complete response (pCR) across both cohorts. In the internal validation cohort, it reached an accuracy of 0.892 (95% CI: 0.862–0.912) and an AUC of 0.901 (95% CI: 0.854–0.948). In the external cohort, it maintained strong performance with an accuracy of 0.857 (95% CI: 0.822–0.928) and an AUC of 0.891 (95% CI: 0.848–0.934), significantly outperforming the individual models (DeLong test, p < 0.01).The deep learning model showed solid performance with accuracies of 0.875 and 0.833 in the internal and external cohorts, respectively, and AUCs of 0.870 and 0.874. The radiomics model displayed moderate accuracy and AUC in both cohorts, while the clinical model showed the lowest predictive capability among the models, with accuracy and AUC values around 0.67 in both cohorts. Conclusions The combined model, integrating clinical, radiomics, and deep learning features, demonstrated superior predictive accuracy for pCR following neoadjuvant chemotherapy (NAC) in breast cancer patients, outperforming individual models. This integrated approach highlights the value of combining diverse data types to improve prediction, offering a promising tool for guiding NAC response assessment and personalized treatment planning.