{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T16:42:14Z","timestamp":1793032934648,"version":"4.5.0"},"reference-count":54,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T00:00:00Z","timestamp":1780185600000},"content-version":"vor","delay-in-days":30,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Ductal carcinoma in situ (DCIS) diagnosed on core biopsy is frequently upgraded to invasive carcinoma at surgery, which may change indications for sentinel lymph node biopsy. Routine breast MRI has limited ability to detect occult invasion preoperatively. This study aimed to develop and externally validate an MRI\u2010based model combining clinical variables, conventional MRI findings, and dynamic contrast\u2010enhanced (DCE) MRI radiomics to predict invasive upgrade in biopsy\u2010proven DCIS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective multicenter study enrolled 478 patients from three hospitals (2014\u20132019). Center 1 contributed 314 patients, randomly split into a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0251) and an internal test set (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a063); Centers 2 (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a039) and 3 (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a062) formed two independent external test sets. Radiologists assessed conventional MRI features, including lesion size, enhancement descriptors, and diffusion\u2010derived apparent diffusion coefficient metrics. Tumors were segmented on DCE MRI. Radiomics features with intraclass correlation coefficient\u00a0&gt;\u00a00.85 were\n                      <jats:italic>z<\/jats:italic>\n                      \u2010score normalized, selected using least absolute shrinkage and selection operator regression, and used to train multiple machine learning classifiers; the best\u2010performing model generated a radiomics score. Model selection and hyperparameter tuning were performed by cross\u2010validation within the training set only. Clinico\u2010radiologic, radiomics, and combined models were evaluated using receiver operating characteristic (ROC) curve analysis, calibration, and decision curve analysis, the area under the curve (AUC) was calculated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six clinico\u2010radiologic factors and 13 radiomic features were retained. In the two external test sets, the clinico\u2010radiologic, radiomics, and combined models achieved AUCs of 0.61 (95% CI, 0.43\u20130.79) and 0.71 (0.58\u20130.83), 0.70 (0.54\u20130.86) and 0.71 (0.58\u20130.84), and 0.76 (0.60\u20130.91) and 0.77 (0.65\u20130.89), respectively. The combined model provided the highest net benefit on decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A combined clinico\u2010radiologic and DCE\u2010MRI radiomics model showed multicenter, externally validated performance for preoperative prediction of invasive upgrade in DCIS, supporting risk stratification for surgical planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70637","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T04:17:16Z","timestamp":1780287436000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multicenter development and external validation of clinical\u2013radiomics models to predict surgically confirmed upstaging in biopsy\u2010proven DCIS using DCE\u2010MRI"],"prefix":"10.1002","volume":"27","author":[{"given":"Xi","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lujie","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"He","sequence":"additional","affiliation":[{"name":"Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beili","family":"Shou","sequence":"additional","affiliation":[{"name":"Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Magnetic Resonance Imaging The First Affiliated Hospital of Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enhui","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Research and Development Shanghai United Imaging Healthcare Co. 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