Personalized Glioblastoma Multiforme Growth Modeling by Integrating Clinical Genomic and Phenotypic Data

Personalized Glioblastoma Multiforme Growth Modeling by Integrating Clinical Genomic and Phenotypic Data | 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 Personalized Glioblastoma Multiforme Growth Modeling by Integrating Clinical Genomic and Phenotypic Data Navid Moshtaghi Kashanian, Hanieh Niroomand-Oscuii, Shahriar Dabiri, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7102971/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 multiforme (GBM) is a highly aggressive brain tumor characterized by significant morbidity and mortality, alongside a complex and heterogeneous nature that complicates outcome prediction and treatment optimization. Here, we present a patient-specific simulation framework for GBM tumor growth, integrating genomic data from pathological biomarkers in biopsy specimens with phenotypic data derived from standard MRI sequences (T1Gd and T2). Model precision was enhanced by adding a patient-specific Pathological Coefficient, derived via a parameter sweeping analysis, to the model's proliferation term. Our findings demonstrate a substantial improvement in simulation accuracy with the incorporation of this genomic information, particularly the Ki-67 proliferation index. Specifically, tumors exhibiting higher Ki-67 expression correlated with increased Pathological Coefficients and accelerated growth dynamics. Mutant IDH-1 type GBM models further demonstrated greater sensitivity to these coefficients compared to wild-type tumors. The personalized nature of these models facilitates more accurate predictions of overall survival and supports informed clinical decision-making, enabling oncologists to simulate individual tumor behavior and tailor therapies for optimized patient outcomes. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Glioblastoma Multiforme Patient-Specific Modeling Mathematical Modeling Ki-67 IDH-1 Medical Imaging Prognostic Modeling Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx 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. 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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-7102971","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504965453,"identity":"1b837483-afe4-4a2e-b1a6-5103b13ba538","order_by":0,"name":"Navid Moshtaghi Kashanian","email":"","orcid":"","institution":"Sahand University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Navid","middleName":"Moshtaghi","lastName":"Kashanian","suffix":""},{"id":504965454,"identity":"ffefb548-341c-42fa-8774-d4dd2d94d64c","order_by":1,"name":"Hanieh 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