{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:57:54Z","timestamp":1790657874552,"version":"4.1.0"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Breast cancer stratification increasingly extends beyond conventional TNM staging and binary human epidermal growth factor receptor 2 (HER2) classification. This shift is driven in part by the therapeutic relevance of HER2-low disease. We investigated whether multiparametric MRI-based radiomics could support pairwise discrimination among HER2-negative, HER2-low, and HER2-positive breast cancers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective single-center study included 157 patients who underwent breast MRI on two 3.0-T scanners. Radiomic features were extracted from T2-weighted, contrast-enhanced T1-weighted, and subtraction T1-weighted images. Seven classifiers were compared after a stratified 8:2 training-test split. Model development, five-fold cross-validation, and model selection were confined to the training set, and the selected logistic regression model was evaluated on the untouched test set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Logistic regression had the highest mean training AUC across the three pairwise tasks (0.778) and was selected as the common final classifier. Test-set AUCs were 0.922 (95% CI, 0.750\u20131.000) for HER2-negative versus HER2-positive, 0.756 (95% CI, 0.529\u20130.938) for HER2-low versus HER2-positive, and 0.705 (95% CI, 0.493\u20130.893) for HER2-negative versus HER2-low classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Training-set model selection followed by independent test-set evaluation identified the strongest discrimination between HER2-negative and HER2-positive tumors. Performance was less reliable when HER2-low tumors were involved, particularly for HER2-negative versus HER2-low classification. These findings are exploratory and require independent multicenter validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1941968","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:33:40Z","timestamp":1790656420000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiparametric MRI-based radiomics for differentiating HER2-negative, HER2-low, and HER2-positive breast cancer"],"prefix":"10.3389","volume":"13","author":[{"given":"Lulu","family":"Gai","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, Shengli Oilfield Central Hospital","place":["Dongying, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shubin","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Shengli Oilfield Central Hospital","place":["Dongying, 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