{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:17:59Z","timestamp":1788182279261,"version":"build-2803163510"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"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>Purpose<\/jats:title>\n                    <jats:p>To develop and validate a combined model integrating intratumoral and peritumoral radiomics features, deep transfer learning features from multiparametric MRI (DCE-MRI, T2WI, and DWI), and clinical indicators, and to evaluate its diagnostic performance and clinical utility for preoperative prediction of HER2 expression status in breast cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We collected data from 411 breast cancer patients from three centers retrospectively (training set: 212; internal validation set: 91; external test sets: 50 and 58). Multiparametric MRI (DCE\/T2WI\/DWI) was acquired. This study extracted manually constructed radiomics features and deep transfer-learning features based on ResNet50 from the tumor interior and peritumoral regions using multiparametric MRI. Through multiple feature-selection steps, such as intraclass correlation coefficient calculation, Spearman correlation testing, and LASSO logistic regression, a fusion feature set of deep learning and radiomics (DLR) was constructed. Finally, the DLR feature set was combined with independent clinical predictors to establish a combined prediction model. The model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis, and visual interpretability analysis was conducted using Grad-CAM and SHAP methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The combined model had the best accuracy and prediction ability, with AUC values of 0.965 (95% CI: 0.939\u20130.990) and 0.904 (95% CI: 0.843\u20130.966) for the training and internal validation cohorts, respectively. In external test sets 1 and 2, it had AUCs of 0.844 (95% CI: 0.724\u20130.964) and 0.846 (95% CI: 0.743\u20130.949), respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>By integrating intratumoral\/peritumoral features from multiparametric MRI, radiomics, and deep transfer learning, and combining these with clinical indicators, the developed model enables precise prediction of HER2 status in breast cancer. This provides a reliable assessment tool for precision diagnosis and treatment decisions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1790816","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:52:02Z","timestamp":1787921522000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating intratumoral and peritumoral radiomics with deep transfer learning from multiparametric MRI for preoperative prediction of HER2 status in breast cancer: a multicenter study"],"prefix":"10.3389","volume":"16","author":[{"given":"Saisai","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]},{"name":"Department of Radiology, Taihe People\u2019s Hospital\/Taihe Hospital Affiliated to Wannan Medical University","place":["Fuyang, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Rong","sequence":"additional","affiliation":[{"name":"Department of Imaging, The First Affiliated Hospital of Xi\u2019an Medical University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiujuan","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqin","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Likang","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Imaging, The First Affiliated Hospital of Xi\u2019an Medical University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xijun","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Anhui 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