{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:51:21Z","timestamp":1789041081699,"version":"build-2803163510"},"reference-count":56,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["BTIT (Grant No. 2022YFF1202803)"],"award-info":[{"award-number":["BTIT (Grant No. 2022YFF1202803)"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study aims to explore the value of habitat\u2010based magnetic resonance imaging (MRI) radiomics for predicting the origin of brain metastasis (BM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To investigate whether habitat\u2010based radiomics can identify the metastatic tumor type of BM and whether an imaging\u2010based model that integrates the volume of peritumoral edema (VPE) can enhance predictive performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A primary cohort was developed with 384 patients from two centers, which comprises 734\u00a0BM lesions. An independent cohort was developed with 28 patients from a third center, which comprises 70\u00a0BM lesions. All patients underwent T1\u2010weighted contrast\u2010enhanced (T1CE) and T2\u2010weighted (T2W) MRI scans before treatment. Radiomics features were extracted from tumor active area (TAA) and peritumoral edema area (PEA) selected using the least absolute shrinkage and selection operator (LASSO) to construct radiomics signatures (Rads). The Rads were further integrated with VPE to build combined models for predicting the metastatic type of BM. Performance of the models were assessed through receiver operating characteristic (ROC) curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Rads derived from TAA and PEA both showed predictive power for identifying the origin of BM. The developed combined models generated the best performance in the training (AUCs, lung cancer [LC]\/non\u2010lung cancer [NLC] vs. small cell lung cancer [SCLC]\/non\u2010small cell lung cancer [NSCLC] vs. breast cancer [BC]\/gastrointestinal cancer [GIC], 0.870\u00a0vs. 0.946\u00a0vs. 0.886), internal validation (area under the receiver operating characteristic curves [AUCs], LC\/NLC vs. SCLC\/NSCLC vs. BC\/GIC, 0.786\u00a0vs. 0.863\u00a0vs. 0.836) and external validation (AUCs, LC \/NLC vs. SCLC\/NSCLC vs. BC\/GIC, 0.805\u00a0vs. 0.877\u00a0vs. 0.774) cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The developed habitat\u2010based radiomics models can effectively identify the metastatic tumor type of BM and may be considered as a potential preoperative basis for timely treatment planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17610","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T23:24:47Z","timestamp":1736205887000},"page":"3075-3087","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Habitat\u2010based MRI radiomics to predict the origin of brain metastasis"],"prefix":"10.1002","volume":"52","author":[{"given":"Yiyao","family":"Sun","sequence":"first","affiliation":[{"name":"School of Intelligent Medicine China Medical University  Shenyang Liaoning P. 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