{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:11:29Z","timestamp":1788757889713,"version":"build-2803163510"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"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>Objective<\/jats:title>\n                    <jats:p>To explore the application value of habitat imaging based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for differentiating luminal and non-luminal subtypes of breast cancer (BC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Retrospective data from 396 BC patients across two centers were collected. The data from Center 1 were split into a training set of 220 patients and an internal validation set of 56 patients, while Center 2 provided an external test set of 120 patients. Multivariable analysis was performed to identify independent risk factors for developing the clinical model. K-means algorithm was used to perform clustering on DCE-MRI. After feature extraction and selection, eight machine learning algorithms were utilized to build traditional radiomics model, habitat model, and clinical model. A stacking fusion strategy was employed to integrate the traditional radiomics model, habitat model, and clinical model for identifying luminal and non-luminal subtypes patients. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curve and decision curve analysis (DCA). Shapley Additive Explanations (SHAP) was performed for model interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For the discrimination of breast cancer luminal and non-luminal subtypes, the stacking model yielded the largest area under the curve value (AUC\u00a0=\u00a00.840), followed by the habitat model and the conventional radiomics model (AUC\u00a0=\u00a00.830, AUC\u00a0=\u00a00.805, respectively), all of which were significantly better than the clinical model (p &amp;lt; 0.05, respectively). Calibration curves showed good calibration of the stacking model, and decision curves confirmed its favorable net clinical benefit. SHAP revealed habitat-LGBM in the stacking model with their contribution being particularly prominent.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Habitat imaging exhibits promising potential to differentiate luminal and non-luminal breast cancer subtypes. The stacking model integrating habitat-LGBM, traditional radiomics-LGBM and clinical-XGBoost may provide favorable predictive performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1899349","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T04:23:17Z","timestamp":1788754997000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Habitat imaging based on DCE-MRI for differentiating luminal and non-luminal subtypes of breast cancer: a two-center study"],"prefix":"10.3389","volume":"16","author":[{"given":"Zhang","family":"Qing","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Yangtze University","place":["Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Xiao-Tao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Yangtze University","place":["Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Radiology, Zhuzhou Central Hospital","place":["Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Xiu-Lan","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Yangtze University","place":["Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Jie","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Yangtze University","place":["Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yun","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Yangtze University","place":["Hubei, 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