{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T08:48:31Z","timestamp":1792054111898,"version":"4.4.0"},"reference-count":40,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["1003 grant 216S432"],"award-info":[{"award-number":["1003 grant 216S432"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Gliomas exhibit aberrant angiogenesis that drives tumor progression and therapeutic resistance. Susceptibility\u2010weighted imaging (SWI) enables noncontrast visualization of intratumoral vasculature, but current approaches rely on summary metrics that overlook vascular topology. We developed a graph\u2010based framework to model vascular networks from SWI and predict glioma survival. Between 2005 and 2020, 228 adults with histologically confirmed diffuse glioma (World Health Organization [WHO] Grades 2\u20134) who had undergone preoperative MRI were retrospectively screened. MRI was performed on 1.5\u2010 and 3\u2010T scanners (Siemens Healthineers, Erlangen, Germany) including T2\u2010weighted imaging (TR\/TE 4000\u20136000\/80\u2013120\u2009ms, FOV 220\u2013256\u2009mm, slice thickness 2\u20133\u2009mm) and SWI (TR\/TE 25\u201335\/15\u201325\u2009ms, FOV 200\u2013240\u2009mm, slice thickness 1.5\u20132\u2009mm). Tumors were manually delineated on T2\u2010weighted images and coregistered to SWI using advanced normalization tools (ANTs). Intratumoral vessels were extracted from SWI using the Frangi vesselness filtering and represented as graphs with nodes at branch points and endpoints and edges along vessel segments. Node\u2010level, edge\u2010level, and graph\u2010level vascular features were processed by a GraphSAGE encoder to generate patient\u2010specific embeddings, which were compared to a learnable population template within the connectional vessel template (CVT) framework to quantify individual vascular atypicality. A Cox proportional hazards head integrated graph embeddings, template deviation, and demographic features to output risk scores. Model performance was evaluated using 10\u2010fold stratified cross\u2010validation for progression\u2010free survival (PFS) and overall survival (OS), with multivariable Cox regression and subgroup analyses across isocitrate dehydrogenase (IDH) mutation status and histological subtypes. A total of 193 patients were included (mean age 43.4\u2009\u00b1\u200914.0\u2009years; 57.5% male). OS analysis included 182 patients (61 deaths), and PFS analysis included 170 patients (102 progression events). The CVT model achieved C\u2010indices of 0.812 for PFS and 0.839 for OS with significant risk stratification (PFS log\u2010rank\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20095.82\u2009\u00d7\u200910\n                    <jats:sup>\u22127<\/jats:sup>\n                    ; OS log\u2010rank\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20091.17\u2009\u00d7\u200910\n                    <jats:sup>\u22126<\/jats:sup>\n                    ). The CVT risk score independently predicted PFS (HR 1.77,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) and OS (HR 1.44,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) beyond age and sex. Subgroup analyses demonstrated consistent PFS discrimination in IDH\u2010wildtype tumors and glioblastoma (C\u2010index 0.677) and strong OS prediction in IDH\u2010mutant tumors (C\u2010index 0.850). Graph\u2010based modeling of SWI\u2010derived vascular topology provides robust, independent prognostic information for glioma survival using a single noncontrast MRI sequence.\n                  <\/jats:p>","DOI":"10.1002\/nbm.70410","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:38:16Z","timestamp":1790249896000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Glioma Survival From Intratumoral Vascular Topology: A Connectional Vessel Template Approach"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9841-9728","authenticated-orcid":false,"given":"Sena","family":"Azamat","sequence":"first","affiliation":[{"name":"Institute of Biomedical Engineering Bogazici University  Istanbul Turkey"},{"name":"Basaksehir Cam and Sakura City Hospital  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Bas","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering Bogazici University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8015-9916","authenticated-orcid":false,"given":"Ay\u00e7a Ersen","family":"Danyeli","sequence":"additional","affiliation":[{"name":"Department of Medical Pathology Acibadem University  Istanbul Turkey"},{"name":"Center for Neuroradiological Applications and Research Acibadem University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3037-2812","authenticated-orcid":false,"given":"Alpay","family":"Ozcan","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Department, Systems Science and Mathematics Laboratory, Medical Magnetic Devices Laboratory Bogazici University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Necmettin","family":"Pamir","sequence":"additional","affiliation":[{"name":"Center for Neuroradiological Applications and Research Acibadem University  Istanbul Turkey"},{"name":"Department of Neurosurgery Acibadem University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9158-8353","authenticated-orcid":false,"given":"Alp","family":"Din\u00e7er","sequence":"additional","affiliation":[{"name":"Center for Neuroradiological Applications and Research Acibadem University  Istanbul Turkey"},{"name":"Department of Radiology Acibadem University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koray","family":"\u00d6zduman","sequence":"additional","affiliation":[{"name":"Center for Neuroradiological Applications and Research Acibadem University  Istanbul Turkey"},{"name":"Department of Neurosurgery Acibadem University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8997-878X","authenticated-orcid":false,"given":"Esin","family":"Ozturk\u2010Isik","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering Bogazici University  Istanbul Turkey"},{"name":"Bogazici University Center for Targeted Therapy Technologies  Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0140\u20106736(23)01054\u20101"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1407279"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awad199"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms26062763"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/md.0000000000014695"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/cac2.12026"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41374\u2010018\u20100055\u20102"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26958"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017170845"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021203071"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010019\u201053629\u2010w"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010024\u201010703\u2010w"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2025.112595"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.2978386"},{"key":"e_1_2_10_16_1","first-page":"155","volume-title":"Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates","author":"Gurbuz M. 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