{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T14:55:05Z","timestamp":1789829705430,"version":"4.0.1"},"reference-count":39,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071871"],"award-info":[{"award-number":["82071871"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003785","name":"Guangdong Medical Research Foundation","doi-asserted-by":"publisher","award":["A2021405"],"award-info":[{"award-number":["A2021405"]}],"id":[{"id":"10.13039\/501100003785","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Studies have shown that deep\u2010learning radiomics (DLR) could help differentiate glioblastoma (GBM) from solitary brain metastasis (SBM), but whether integrating demographic\u2010MRI and DLR features can more accurately distinguish GBM from SBM remains uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To construct and validate a demographic\u2010MRI deep\u2010learning radiomics nomogram (DDLRN) integrating demographic\u2010MRI and DLR signatures to differentiate GBM from SBM preoperatively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>Two hundred and thirty\u2010five patients with GBM (N\u2009=\u2009115) or SBM (N\u2009=\u2009120), randomly divided into a training cohort (90 GBM and 98 SBM) and a validation cohort (25 GBM and 22 SBM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>Axial T2\u2010weighted fast spin\u2010echo sequence (T2WI), T2\u2010weighted fluid\u2010attenuated inversion recovery sequence (T2\u2010FLAIR), and contrast\u2010enhanced T1\u2010weighted spin\u2010echo sequence (CE\u2010T1WI) using 1.5\u2010T and 3.0\u2010T scanners.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>The demographic\u2010MRI signature was constructed with seven imaging features (\u201cpool sign,\u201d \u201cirregular ring sign,\u201d \u201cregular ring sign,\u201d \u201cintratumoral vessel sign,\u201d the ratio of the area of peritumoral edema to the enhanced tumor, the ratio of the lesion area on T2\u2010FLAIR to CE\u2010T1WI, and the tumor location) and demographic factors (age and sex). Based on multiparametric MRI, radiomics and deep\u2010learning (DL) models, DLR signature, and DDLRN were developed and validated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      The Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      test, Pearson test, least absolute shrinkage and selection operator, and support vector machine algorithm were applied for feature selection and construction of radiomics and DL models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DDLRN showed the best performance in differentiating GBM from SBM with area under the curves (AUCs) of 0.999 and 0.947 in the training and validation cohorts, respectively. Additionally, the DLR signature (AUC\u2009=\u20090.938) outperformed the radiomics and DL models, and the demographic\u2010MRI signature (AUC\u2009=\u20090.775) was comparable to the T2\u2010FLAIR radiomics and DL models in the validation cohort (AUC\u2009=\u20090.762 and 0.749, respectively).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>DDLRN integrating demographic\u2010MRI and DLR signatures showed excellent performance in differentiating GBM from SBM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>3<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 2<\/jats:p>\n                  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