{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:59:23Z","timestamp":1791381563172,"version":"4.3.3"},"reference-count":30,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"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":["Medical Physics"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate\u00a0assessment of\u00a0muscle invasion in bladder\u00a0cancer\u00a0is crucial\u00a0for\u00a0guiding treatment\u00a0and\u00a0prognosis. Habitat\u2010based radiomics, which\u00a0accounts\u00a0for\u00a0tumor heterogeneity, may\u00a0enhance\u00a0evaluation\u00a0of tumor\u00a0status and\u00a0outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This research primarily investigates the efficacy of a novel habitat\u2010based radiomic model in predicting muscle invasion in bladder cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 325 bladder cancer patients from two institutions (July 2018\u2013July 2023). Patients were divided into a training cohort (231 cases, Institution 1) and an external test cohort (94 cases, Institution 2). CT images were standardized, and areas of interest (AOIs) were delineated. Nineteen texture features were extracted from each AOI, and K\u2010means clustering identified intratumoral habitats. Radiomic features from each habitat were extracted using PyRadiomics and used to build a habitat model with the ExtraTree algorithm. For comparison, we also developed uniphase, multiphase, and clinical models. Model performance was evaluated by sensitivity, specificity, accuracy, and area under the ROC curve (AUC). The Delong test compared diagnostic performance between models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Three distinct habitats were identified within bladder tumors. The habitat model achieved an AUC of 0.947 (95% CI: 0.911\u20130.982) in the training cohort and 0.825 (95% CI: 0.704\u20130.946) in the external test cohort. In the training cohort, the habitat model outperformed the uniphase (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.003), multiphase (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.036), and clinical models (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.049). The combined habitat and clinical model showed superior diagnostic performance compared to uniphase (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.019) and multiphase clinical (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.069) fusion models. The radiomics signature integrating habitat and multiphase features reliably predicted muscle invasion across the entire cohort (AUC\u00a0=\u00a00.922, 95% CI: 0.883\u20130.960).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Habitat\u2010based radiomic features combined with machine learning enable accurate preoperative prediction of muscle invasion in bladder cancer using CT images.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18021","type":"journal-article","created":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T05:30:16Z","timestamp":1754717416000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Habitat\u2010based radiomic model for predicting muscle invasion in bladder cancer: A multi\u2010center study using enhanced\u2010CT and machine 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