Analysis of CT Image Features of CcRCC on The Basis of Machine Learning: Differentiation of High-Grade from Low-Grade Fuhrman Nuclear Grades

Analysis of CT Image Features of CcRCC on The Basis of Machine Learning: Differentiation of High-Grade from Low-Grade Fuhrman Nuclear Grades | 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 Biological Sciences - Article Analysis of CT Image Features of CcRCC on The Basis of Machine Learning: Differentiation of High-Grade from Low-Grade Fuhrman Nuclear Grades Jenn- Yeu Wang, Chung-Ming Lo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5828567/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 Previous researches have clarified clinical applications of radiomics-based prediction of tumor phenotype. The purpose of our research is to utilize radiomic features in computer-aided diagnosis (CAD) system of prediction for high and low Fuhrman nuclear grades (FNG) in clear cell renal cell carcinoma (ccRCC). We selected 110 images from 109 cases of axial contrast- enhanced computed tomography with a pathological diagnosis of ccRCC from The Cancer Imaging Achieve portal. After preprocessing, extraction and selection of features, Weka Experiment Environment was run to compare performance of different classifiers to predict high and low grade FNG. The K- Nearest Neighbors classifiers input with 23 features (Sensitivity, specificity, accuracy, precision, recall and AUROC were 91%, 89%, 90.91%, 92%, 91% and 90%, respectively) showed significant better performance than other 11 classifiers. The developed CAD system generated by the machine-learning is a non-invasive and accurate method of predicting and monitoring FNG characteristics of ccRCC. Health sciences/Biomarkers/Prognostic markers Biological sciences/Computational biology and bioinformatics/Machine learning Fuhrman nuclear grade radiomic features classifiers Support Vector Machines K- Nearest Neighbors machine learning clear cell RCC Full Text Additional Declarations There is NO Competing Interest. Table 1 to 5 are available in the Supplementary Files section. 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. 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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-5828567","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":407615264,"identity":"3ac154d8-e29b-41d6-ba43-ca43d98f2222","order_by":0,"name":"Jenn- Yeu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACNiC2kGNgYGx8wNggQbQWCWMGBuZmA7AWEJ8hgbCWxAYG9jYJxgYGwlrM2XuPPa6okEhf238QqGWHRR6/fAPbg48/cGux7DmXbnjmjETuthuJzRaMZySKJdsY2A1n4LHF4EaOmWRjG0gLY+MNxjaJxA3HGNikefBpuf8GrCXd7PzBBgmQlv0gLX/w2sID1pJgdiCxCaxlAxtQCz7vG5zJS5NsOCNhCPKLQSJQy4xjiW2SPWl4tBw/e0yyocJG3uz88YcPPrbVJfY3Hz4m8cMGtxYGBh4kNsQ54NghVssoGAWjYBSMAmwAAMnAUFmug+GIAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7610-9374","institution":"Taipei City Hospital, Zhongxiao campus","correspondingAuthor":true,"prefix":"","firstName":"Jenn-","middleName":"Yeu","lastName":"Wang","suffix":""},{"id":407615265,"identity":"2acdef3e-939b-42f7-a6f4-88622a9e921c","order_by":1,"name":"Chung-Ming Lo","email":"","orcid":"","institution":"National Chenchi University","correspondingAuthor":false,"prefix":"","firstName":"Chung-Ming","middleName":"","lastName":"Lo","suffix":""}],"badges":[],"createdAt":"2025-01-14 16:06:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5828567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5828567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95657969,"identity":"a7aed3be-e847-4bee-8834-770bd5596c63","added_by":"auto","created_at":"2025-11-11 16:22:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":953079,"visible":true,"origin":"","legend":"","description":"","filename":"codecodeprotectnontracePatient261.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5828567/v1_covered_dd63e6cd-33f5-4f05-830a-a508256b0c2e.pdf"}],"financialInterests":"\u003cp\u003eThere is \u003cstrong\u003eNO\u003c/strong\u003e Competing Interest.\u003c/p\u003e\n\u003cp\u003eTable 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e","formattedTitle":"Analysis of CT Image Features of CcRCC on The Basis of Machine Learning: Differentiation of High-Grade from Low-Grade Fuhrman Nuclear Grades","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Fuhrman nuclear grade, radiomic features, classifiers, Support Vector Machines, K- Nearest Neighbors, machine learning, clear cell RCC","lastPublishedDoi":"10.21203/rs.3.rs-5828567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5828567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious researches have clarified clinical applications of radiomics-based prediction of tumor phenotype. 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