{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T12:23:29Z","timestamp":1792671809260,"version":"4.5.0"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate prediction of axillary pathological complete response (apCR) after neoadjuvant chemotherapy (NAC) in clinically node-positive (cN+) breast cancer patients may guide surgical de-escalation from axillary lymph node dissection to sentinel lymph node biopsy. This study aimed to develop and internally validate a combined radiomics-clinical prediction model for apCR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center retrospective study enrolled 386 cN+ breast cancer patients (training, n = 270; validation, n = 116). Pre-NAC DCE-MRI radiomic features were extracted from primary tumors. LASSO regression selected eight features for Rad-score construction. Clinical predictors were identified via logistic regression. Model performance was evaluated using AUC, calibration metrics, and decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The overall apCR rate was 43.5% (168\/386). The combined model (tumor size, HER2 status, Ki-67, breast clinical complete response, and Rad-score) achieved a validation AUC of 0.703 (95% CI, 0.610\u20130.792). It significantly outperformed the radiomics-only model (\u0394AUC = 0.094, P\u00a0=\u00a00.004) but not the clinical-only model (\u0394AUC = 0.020, P\u00a0=\u00a00.713). The combined model showed a calibration slope of 0.811 and an intercept of 0.018, indicating moderate overfitting. Risk stratification showed monotonic gradients across tertiles (low 18.8%, intermediate 48.0%, high 58.8%). After bootstrap bias correction, the optimism-corrected training AUC of the combined model was 0.742.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combined model demonstrated moderate discriminatory ability but did not add significant value over clinical predictors alone. The current misclassification rate precludes direct clinical application for surgical de-escalation. External multicenter validation is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1876446","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:37:37Z","timestamp":1783661857000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and internal validation of a radiomics-clinical combined model for predicting axillary pathological complete response in clinically node-positive breast cancer patients after neoadjuvant chemotherapy"],"prefix":"10.3389","volume":"16","author":[{"given":"Weitao","family":"Yan","sequence":"first","affiliation":[{"name":"Breast Disease Diagnosis and Treatment Center, The First Hospital of Qinhuangdao","place":["Qinhuangdao, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Department 2 of Respiratory and Critical 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