{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T12:01:56Z","timestamp":1789473716437,"version":"build-2803163510"},"reference-count":41,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microenvironment promotes tumor progression. A noninvasive screening tool capable of mapping tumor vascularity may assist in preoperative grading and subtyping of gliomas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate 7T susceptibility\u2010weighted imaging (SWI) for differentiating glioma isocitrate dehydrogenase (IDH) status and World Health Organization (WHO) grade based on vascular microenvironment features.<\/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>Among 218 patients with histologically confirmed gliomas, 152 (70%) were assigned to training and 66 (30%) to validation by stratified random sampling. The training\/validation sets included 61\/26 IDH\u2010mutant, 91\/40 IDH\u2010wildtype, 57\/25 Grade 1\u20132, and 95\/41 Grade 3\u20134 gliomas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>7T; T1\u2010weighted Magnetization prepared 2 rapid acquisition gradient echo (MP2RAGE) and SWI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Two independent neuroradiologists delineated tumors on 7T SWI. Vascular topology, density, and intensity were extracted following Frangi filtering and 3D skeletonization. Following feature selection, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and random forest (RF) were developed to differentiate IDH mutant from IDH wildtype, and WHO Grade 1\u20132 from WHO Grade 3\u20134.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Continuous and categorical variables were compared using Student's\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test\/Mann\u2013Whitney U test and chi\u2010square test, respectively. Cohen's kappa statistic and intraclass correlation coefficient (ICC) were used to assess the interobserver agreement. Features were selected by\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test (or Mann\u2013Whitney U test) and Spearman's rank correlation. Sensitivity, specificity, accuracy, F1\u2010score, and area under the curve (AUC) were used to evaluate model performance. A\n                      <jats:italic>p<\/jats:italic>\n                      value &lt;\u20090.05 was considered significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Nine robust vascular features were retained. Imaging models achieved AUCs of 0.816\u20130.843 for IDH status and 0.834\u20130.874 for WHO grade in the internal validation set. Integrating clinical factors improved performance, with optimal AUCs of 0.888 and 0.889, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>Integrating 7T SWI vascular features with machine learning may aid noninvasive differentiation of glioma molecular status and grade.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>4.<\/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.70523","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T02:43:12Z","timestamp":1787798592000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Noninvasive Profiling of the Glioma Vascular Microenvironment via\n                    <scp>7T MRI<\/scp>\n                    : Decoding Angiogenic Signatures for Isocitrate Dehydrogenase and World Health Organization Grade Differentiation"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1906-2371","authenticated-orcid":false,"given":"Mingxiao","family":"Wang","sequence":"first","affiliation":[{"name":"Medical School of Chinese PLA  Beijing China"},{"name":"Department of Radiology First Medical Center, Chinese PLA General Hospital  Beijing 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