Latent Radiomic-Driven U-Net for Brain Tumor Segmentation, Survival Forecasting, and 3D Visualization

Latent Radiomic-Driven U-Net for Brain Tumor Segmentation, Survival Forecasting, and 3D Visualization | 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 Research Article Latent Radiomic-Driven U-Net for Brain Tumor Segmentation, Survival Forecasting, and 3D Visualization Sriramakrishnan Pathmanaban, Kalaiselvi Thiruvenkadam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6534126/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 Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is a challenging task due to the vast amount of data, physician shortage, high population, and time-consuming nature of the process. To address these challenges, we propose a method that utilizes a latent radiomic-driven U-Net for automatic and accurate brain tumor segmentation, 3D visualization, and survival prediction. Our approach consists of three phases: segmentation, feature extraction, and survival prediction. In the first phase, we preprocess the data, perform multi-model fusion, and employ a U-Net architecture for tumor segmentation. The second phase involves extracting latent features from the U-Net encoder and combining them with radiomic features (shape, area, volume, and location) of the segmented tumor subcomponents. The third phase includes feature selection, outlier elimination, bootstrapping, and finally, survival prediction using linear regression. Our results demonstrate a Dice coefficient of 84%, 86%, and 91% for enhancing tumor, tumor core, and whole tumor regions, respectively, on the BraTS2020 dataset. Under post processing, 3D tumor volume is rendered and visualized from segmented results. Average tumor volume size is measured in cm 3 and compared with ground truth volumes. Survival prediction yields an R² score of 0.73, classified into high-risk (0-586 days), moderate-risk (587-1168 days), and low-risk (1169-1767 days) categories, with a random forest accuracy of 85.3%. The proposed method outperforms competitive methods in both tumor substructure segmentation and survival forecast, showcasing its potential for clinical applications. Nuclear Medicine & Medical Imaging U-Net architecture survival forecast tumor segmentation latent features radiomic features BraTS2020 dataset deep learning Full Text Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6534126","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448332404,"identity":"d163e254-c7ad-4ada-8d63-c647454f15de","order_by":0,"name":"Sriramakrishnan Pathmanaban","email":"data:image/png;base64,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","orcid":"","institution":"amrita Vishwa vidhyapeetham","correspondingAuthor":true,"prefix":"","firstName":"Sriramakrishnan","middleName":"","lastName":"Pathmanaban","suffix":""},{"id":448332405,"identity":"03af6e01-2a07-4149-a05a-23deaf455f75","order_by":1,"name":"Kalaiselvi Thiruvenkadam","email":"","orcid":"","institution":"Gandhigram Rural University","correspondingAuthor":false,"prefix":"","firstName":"Kalaiselvi","middleName":"","lastName":"Thiruvenkadam","suffix":""}],"badges":[],"createdAt":"2025-04-26 09:38:50","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6534126/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6534126/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81601890,"identity":"c0dc9633-7570-4738-8ac6-29b52d0f880d","added_by":"auto","created_at":"2025-04-29 04:24:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589979,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6534126/v1_covered_1fa10aba-2b78-4e9a-96a2-fcdf000cb774.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLatent Radiomic-Driven U-Net for Brain Tumor Segmentation, Survival Forecasting, and 3D Visualization\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Amrita Vishwa Vidyapeetham University","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":"U-Net architecture, survival forecast, tumor segmentation, latent features, radiomic features, BraTS2020 dataset, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-6534126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6534126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain tumor segmentation from Magnetic Resonance Imaging (MRI) is a challenging task due to the vast amount of data, physician shortage, high population, and time-consuming nature of the process. To address these challenges, we propose a method that utilizes a latent radiomic-driven U-Net for automatic and accurate brain tumor segmentation, 3D visualization, and survival prediction. Our approach consists of three phases: segmentation, feature extraction, and survival prediction. In the first phase, we preprocess the data, perform multi-model fusion, and employ a U-Net architecture for tumor segmentation. The second phase involves extracting latent features from the U-Net encoder and combining them with radiomic features (shape, area, volume, and location) of the segmented tumor subcomponents. The third phase includes feature selection, outlier elimination, bootstrapping, and finally, survival prediction using linear regression. Our results demonstrate a Dice coefficient of 84%, 86%, and 91% for enhancing tumor, tumor core, and whole tumor regions, respectively, on the BraTS2020 dataset. Under post processing, 3D tumor volume is rendered and visualized from segmented results. Average tumor volume size is measured in cm\u003csup\u003e3\u003c/sup\u003e and compared with ground truth volumes. Survival prediction yields an R² score of 0.73, classified into high-risk (0-586 days), moderate-risk (587-1168 days), and low-risk (1169-1767 days) categories, with a random forest accuracy of 85.3%. The proposed method outperforms competitive methods in both tumor substructure segmentation and survival forecast, showcasing its potential for clinical applications.\u003c/p\u003e","manuscriptTitle":"Latent Radiomic-Driven U-Net for Brain Tumor Segmentation, Survival Forecasting, and 3D Visualization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 04:16:39","doi":"10.21203/rs.3.rs-6534126/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"3429ceaf-0cd7-4cbb-b981-38330ddadd5f","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47718471,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2025-04-29T04:16:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 04:16:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6534126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6534126","identity":"rs-6534126","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}