{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T21:22:53Z","timestamp":1790716973555,"version":"4.1.0"},"reference-count":22,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The pathological response to neoadjuvant chemotherapy (NAC) is an established predictor of long-term outcomes in breast cancer. However, conventional binary assessment based solely on pathological complete response (pCR) fails to capture prognostic heterogeneity across molecular subtypes. This study aimed to develop an interpretable deep learning model that integrates multiple clinical and pathological variables to predict both recurrence and metastasis development following NAC treatment. Methods: We conducted a retrospective analysis of 832 breast cancer patients who received NAC between 2013 and 2022. The analysis incorporated five key variables: tumor size changes, nodal status, Ki-67 index, Miller\u2013Payne grade, and molecular subtype. A Multi-Layer Perceptron (MLP) model was implemented on the PyTorch platform and systematically benchmarked against SVM, Random Forest, and XGBoost models using five-fold cross-validation. Model performance was assessed by calculating the area under the curve (AUC), accuracy, precision, recall, and F1-score, and by analyzing confusion matrices. Results: The MLP model achieved AUC values of 0.86 (95% CI: 0.82\u20130.93) for HER2-positive cases, 0.82 (95% CI: 0.70\u20130.92) for triple-negative cases, and 0.76 (95% CI: 0.66\u20130.82) for HR+\/HER2-negative cases. SHAP analysis identified post-NAC tumor size, Ki-67 index, and Miller\u2013Payne grade as the most influential predictors. Notably, patients who achieved pCR still had a 12% risk of developing recurrence, highlighting the necessity for ongoing risk assessment beyond binary response evaluation. Conclusions: The proposed deep learning system provides precise and interpretable risk assessment for NAC patients, facilitating individualized treatment approaches and post-treatment monitoring plans.<\/jats:p>","DOI":"10.3390\/diagnostics15212763","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:55:22Z","timestamp":1762178122000},"page":"2763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Correlation Study Between Neoadjuvant Chemotherapy Response and Long-Term Prognosis in Breast Cancer Based on Deep Learning Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8433-300X","authenticated-orcid":false,"given":"Ke","family":"Wang","sequence":"first","affiliation":[{"name":"Breast Surgery Department, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yikai","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2246-9080","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2269-3058","authenticated-orcid":false,"given":"Bing","family":"Yang","sequence":"additional","affiliation":[{"name":"Breast Surgery Department, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2283-6437","authenticated-orcid":false,"given":"Yubo","family":"Tao","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1097\/XCS.0000000000000872","article-title":"Long-term outcomes and predictors of response in breast cancer patients with advanced nodal involvement","volume":"238","author":"Sun","year":"2024","journal-title":"J. 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