Survival Rate Prediction in glioblastoma Patients Using Machine learning

Survival Rate Prediction in glioblastoma Patients Using Machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Survival Rate Prediction in glioblastoma Patients Using Machine learning Ichraq El Hachimy, Badr Ben ELallid, Lourdes Hontecillas-Prieto, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5765031/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Glioblastoma, an aggressive form of brain cancer, continues to pose significant challenges, with median survival rates ranging from 12 to 18 months despite ongoing advances in treatment. Traditional survival prediction models predominantly rely on non-omic data, such as MRI, PET, and CT scans, which often lack the granularity to uncover molecular biomarkers crucial for guiding personalized therapeutic strategies. In this study, we introduce a novel methodology focused solely on omic data for survival prediction in glioblastoma patients. Our approach integrates genomic features, including G-CIMP methylation, gene expression subtypes, and IDH1 mutation. Utilizing a robust dataset comprising 577 patient records and 22 features, we implement a deep learning model based on Transformer architecture. The model incorporates positional encoding to capture complex temporal relationships in survival data and leverages the Cox proportional hazards framework for survival analysis. Our results demonstrate a high concordance index (C-index) of 87% and an integrated Brier score (IBS) of 0.05 that further validates the model’s predictive accuracy. We highlight the influence of critical genomic features on survival predictions. This approach represents a significant advancement in leveraging omic data and modern machine learning techniques to enhance the accuracy and reliability of glioblastoma prognosis, offering promising implications for personalized treatment strategies and improved patient outcomes. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5765031","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":402090078,"identity":"b18602f4-1912-40c8-b77f-fd9de8eab306","order_by":0,"name":"Ichraq El Hachimy","email":"","orcid":"","institution":"Université Moulay Ismail de Meknes","correspondingAuthor":false,"prefix":"","firstName":"Ichraq","middleName":"El","lastName":"Hachimy","suffix":""},{"id":402090079,"identity":"cfb5d776-332e-4a22-b5ba-2fd3a833fbd9","order_by":1,"name":"Badr Ben ELallid","email":"","orcid":"","institution":"Université Moulay Ismail de Meknes","correspondingAuthor":false,"prefix":"","firstName":"Badr","middleName":"Ben","lastName":"ELallid","suffix":""},{"id":402090080,"identity":"94dec277-e67d-4746-a8cb-3d57beb0642b","order_by":2,"name":"Lourdes Hontecillas-Prieto","email":"","orcid":"","institution":"University of Seville","correspondingAuthor":false,"prefix":"","firstName":"Lourdes","middleName":"","lastName":"Hontecillas-Prieto","suffix":""},{"id":402090081,"identity":"2a72d56c-c893-4b6f-8196-eae943df7ef6","order_by":3,"name":"Nabil Benamar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYFACHijN3sDAkECaFp4DJGuRIFI9gzkD78HHBTXb5M1nvjHd8IDBTp5B7PADvFosG/iSjWccu20453aO2Y0EhmTDBuk0A7xaDA7wmEnzsN1mnCEN1sKcwCCdQFCL+W+ef7ftZ0ieAWmpB2pJ/0DQFmbettuJMyR4QFoOA7XkELDlMF+yNG/f7eQZPGllNxIMjhu2SecU4NdyvPfgZ55vt21nsB/edvNHRbU8v3T6BrxaGJhRTWBgYMOvfhSMglEwCkYBMQAAhLw/tU7jdQcAAAAASUVORK5CYII=","orcid":"","institution":"Université Moulay Ismail de Meknes","correspondingAuthor":true,"prefix":"","firstName":"Nabil","middleName":"","lastName":"Benamar","suffix":""},{"id":402090082,"identity":"a92ebde1-a136-4cc6-ab58-b4294897275d","order_by":4,"name":"Nabil Hajji","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Nabil","middleName":"","lastName":"Hajji","suffix":""}],"badges":[],"createdAt":"2025-01-04 18:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5765031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5765031/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74303516,"identity":"e5a8d05f-9538-46be-b002-1582e449976d","added_by":"auto","created_at":"2025-01-20 22:16:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":612236,"visible":true,"origin":"","legend":"","description":"","filename":"SurvivalRatePredictioninGlioblastomaPatientsUsingMachinelearning1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5765031/v1_covered_ed217d00-e85e-4380-8286-b5a254b70124.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Survival Rate Prediction in glioblastoma Patients Using Machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5765031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5765031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Glioblastoma, an aggressive form of brain cancer, continues to pose significant challenges, with median survival rates ranging from 12 to 18 months despite ongoing advances in treatment. Traditional survival prediction models predominantly rely on non-omic data, such as MRI, PET, and CT scans, which often lack the granularity to uncover molecular biomarkers crucial for guiding personalized therapeutic strategies. In this study, we introduce a novel methodology focused solely on omic data for survival prediction in glioblastoma patients. Our approach integrates genomic features, including G-CIMP methylation, gene expression subtypes, and IDH1 mutation. Utilizing a robust dataset comprising 577 patient records and 22 features, we implement a deep learning model based on Transformer architecture. The model incorporates positional encoding to capture complex temporal relationships in survival data and leverages the Cox proportional hazards framework for survival analysis.\nOur results demonstrate a high concordance index (C-index) of 87% and an integrated Brier score (IBS) of 0.05 that further\nvalidates the model’s predictive accuracy. We highlight the influence of critical genomic features on survival predictions. 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