{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:03:02Z","timestamp":1789534982726,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["H2025206451"],"award-info":[{"award-number":["H2025206451"]}],"id":[{"id":"10.13039\/501100003787","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":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Timely identification of early recurrence (\u2264\u20096\u2009months) may improve prognosis of glioblastoma (GBM), but conventional MRI has shown limited accuracy in this setting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Risk\u2010assessment models and a nomogram were constructed by integrating SyMRI metrics, clinical\u2013pathological variables, and cMRI features, including contrast\u2010enhanced T1\u2010weighted imaging and fluid\u2010attenuated inversion recovery (FLAIR) findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective observational study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>Seventy\u2010eight patients with GBM (median age, 59\u2009years; 44 [56.4%] males).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>3\u2009T; pre\u2010 and post\u2010contrast three\u2010dimensional T1\u2010weighted imaging, FLAIR, and SyMRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Histogram\u2010based quantitative metrics were extracted from SyMRI maps using subregions defined on fused FLAIR and contrast\u2010enhanced T1\u2010weighted images. All candidate clinical\u2013pathological variables, cMRI features, and SyMRI\u2010derived metrics were entered directly into least absolute shrinkage and selection operator (LASSO) regression for variable selection. Risk\u2010assessment models and a nomogram were constructed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Multivariable logistic regression, multicollinearity assessment using variance inflation factors, receiver operating characteristic curve analysis with DeLong test, stratified 10\u2010fold cross\u2010validation, leave\u2010one\u2010out cross\u2010validation, nested 5\u2010fold cross\u2010validation, calibration curves, and decision curve analysis. A two\u2010sided\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05 was considered statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multivariate analysis identified reduced T2 entropy (&lt;\u20092.113) in enhancement\u2010corresponding regions (odds ratio [OR]\u2009=\u20090.08), thick linear or nodular residual cavity wall enhancement (OR\u2009=\u20095.28), corpus callosum involvement (OR\u2009=\u20095.08), and O6\u2010methylguanine\u2010DNA methyltransferase (MGMT) promoter methylation (OR\u2009=\u20090.19) as variables associated with early recurrence. The integrated model achieved the highest performance (AUC\u2009=\u20090.864). Nested cross\u2010validation showed moderate internal validation performance, with an AUC of 0.724 (0.722\u20130.726).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>Histogram\u2010based pre\u2010radiotherapy SyMRI metrics, particularly T2 entropy, were associated with early GBM recurrence. The integrated model achieved the highest apparent performance; nested internal validation showed moderate performance. External validation in larger multicenter cohorts is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/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                  <\/jats:sec>","DOI":"10.1002\/jmri.70407","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:38:52Z","timestamp":1781638732000},"page":"1157-1170","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pre\u2010Radiotherapy Synthetic\n                    <scp>MRI<\/scp>\n                    \u2013Derived Quantitative Heterogeneity and Early Recurrence in Glioblastoma"],"prefix":"10.1002","volume":"64","author":[{"given":"Guanmin","family":"Quan","sequence":"first","affiliation":[{"name":"Department of Medical Imaging The Second Hospital of Hebei Medical University  Shijiazhuang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7633-0573","authenticated-orcid":false,"given":"Shijia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging The Second Hospital of Hebei Medical University  Shijiazhuang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2386-3370","authenticated-orcid":false,"given":"Yawu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Clinical Radiology Kuopio University Hospital  Kuopio Finland"},{"name":"Department of Neurology University of Eastern Finland  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