{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:12:43Z","timestamp":1791472363629,"version":"4.3.4"},"reference-count":68,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Government of Lower Austria","award":["SF_0045"],"award-info":[{"award-number":["SF_0045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Glioblastoma is an extremely aggressive brain tumor that diffusely infiltrates white matter and alters large-scale brain connectivity. Most prognostic models focus on localized tumor features and clinical variables, overlooking broader effects on the brain\u2019s structural connectome. This study addressed this limitation by integrating graph-theoretical analysis of preoperative diffusion tensor imaging (DTI)-derived structural connectomes with machine learning (ML) to improve prediction of overall survival (OS) in newly diagnosed glioblastoma. Methods: Preoperative DTI data from 871 glioblastoma patients from the UPenn-GBM and UCSF-PDGM cohorts were processed to construct whole-brain structural connectomes weighted by tract count and quantitative anisotropy (QA). Global and nodal graph-theoretical network metrics were extracted and combined with demographic and clinical information. Ten ML models were trained and validated on 784 patients (90% of the cohort). The three best-performing algorithms were tested on a held-out cohort of 87 patients (10%). Results: Random forest, adaptive boosting, and KStar showed the strongest validation performance. In held-out internal testing, random forest models using degree and QA-weighted strength achieved accuracies of 0.862 and 0.874, with AUROCs of 0.929 and 0.909, for predicting OS beyond one year. Strength and clustering coefficient were key predictors, with over two-thirds of significant nodes localized in the temporal lobe, particularly the parahippocampal, and superior, middle, and inferior temporal gyri. Conclusions: Graph-theoretical quantification of structural brain network disruption combined with ML allows accurate prediction of OS in glioblastoma. These results support a network-based conceptualization of the disease and indicate that connectome-derived metrics may complement established prognostic frameworks.<\/jats:p>","DOI":"10.3390\/cancers18071161","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:19:32Z","timestamp":1775434772000},"page":"1161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine-Learning-Based Survival Prediction in Glioblastoma Using Graph-Theoretical Analysis of Structural Network Alterations"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8348-2620","authenticated-orcid":false,"given":"Andreas","family":"Stadlbauer","sequence":"first","affiliation":[{"name":"Karl Landsteiner University of Health Sciences, 3500 Krems, Austria"},{"name":"Institute of Medical Radiology, University Clinic of St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"},{"name":"Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Oberndorfer","sequence":"additional","affiliation":[{"name":"Karl Landsteiner University of Health Sciences, 3500 Krems, Austria"},{"name":"Department of Neurology, University Clinic of St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gertraud","family":"Heinz","sequence":"additional","affiliation":[{"name":"Karl Landsteiner University of Health Sciences, 3500 Krems, Austria"},{"name":"Institute of Medical Radiology, University Clinic of St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8338-8060","authenticated-orcid":false,"given":"Franz","family":"Marhold","sequence":"additional","affiliation":[{"name":"Karl Landsteiner University of Health Sciences, 3500 Krems, Austria"},{"name":"Department of Neurosurgery, University Clinic of St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4888-543X","authenticated-orcid":false,"given":"Thomas M.","family":"Kinfe","sequence":"additional","affiliation":[{"name":"Mannheim Center for Neuromodulation and Neuroprosthetics (MCNN), Department of Neurosurgery, Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8037-4907","authenticated-orcid":false,"given":"Mario","family":"Dorostkar","sequence":"additional","affiliation":[{"name":"Karl Landsteiner University of Health Sciences, 3500 Krems, Austria"},{"name":"Institute of Clinical and Molecular Pathology, University Clinic of St. P\u00f6lten, Karl Landsteiner University of Health Sciences, 3100 St. P\u00f6lten, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1284-1427","authenticated-orcid":false,"given":"Oliver","family":"Schnell","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8130-9963","authenticated-orcid":false,"given":"Uwe","family":"Meyer-B\u00e4se","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anke","family":"Meyer-B\u00e4se","sequence":"additional","affiliation":[{"name":"Department of Scientific Computing, Dirac Science Library, Florida State University, Tallahassee, FL 32306, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","article-title":"The 2021 WHO Classification of Tumors of the Central Nervous System: A Summary","volume":"23","author":"Louis","year":"2021","journal-title":"Neuro. 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