AI-based histopathological classification of central nervous system tumours

AI-based histopathological classification of central nervous system tumours | 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 AI-based histopathological classification of central nervous system tumours Moritz Gerstung, Darui Jin, Artem Shmatko, Areeba Patel, Ramin Rahmanzade, and 43 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5600492/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract DNA methylation analysis has become an essential diagnostic assay for classifying tumours of the central nervous system (CNS). However, this test requires additional resources and time compared to conventional histopathological diagnosis using haematoxylin and eosin (H&E) stained tissue sections, which are available globally. Here, we propose to reduce time and resource requirements using Hetairos, an artificial intelligence (AI) algorithm that predicts 102 methylation-based CNS tumour subtypes from digital images of H&E slides. Hetairos is built and assessed using over 11,000 slides from 10 centres across four continents. Hetairos produces well-calibrated probabilities for each subtype. Across cohorts, Hetairos identifies 50-70% of cases, which it classifies with high confidence and an accuracy of 0.87 for its highest-rated predictions. Hetairos achieved higher accuracy than four board-certified neuropathologists in a direct comparison using only histology images (0.68 vs 0.28). A prospective evaluation within a routine diagnostic setting confirmed Hetairos’ performance and highlighted its 2-day turnaround time compared to an average of 12 days for molecular testing. Hetairos can assist diagnostic decision-making by limiting the number of likely diagnoses, informing efficient further testing, and by highlighting tissue areas indicative of specific diagnoses. These capabilities suggest Hetairos is a valuable tool assisting in the diagnosis of the full spectrum of paediatric and adult CNS tumours. Biological sciences/Cancer/CNS cancer Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations Yes there is potential Competing Interest. D.C., M.S., D. Schrimpf, A.v.D., F.S., S.M.P. and D.T.W.J. are shareholders in and co-founders of Heidelberg Epignostix GmbH. AP and MS have become full-time employees of Heidelberg Epignostix GmbH since December and July 2024 respectively. D.Schrimpf has become a part-time employee of Heidelberg Epignostix GmbH since November 2024. SI is a consultant for Brainlab AG, Icotec AG and Carl Zeiss Meditec AG and has past honoraria from Nexstim AG. SMK is a consultant for Brainlab AG, Ulrich Medical and Need Inc., shareholder of Need Inc. and received honoraria from Nexstim Plc, Spineart Deutschland GmbH, Medtronic AG, and Carl Zeiss Meditec AG. None other declared. Supplementary Files SupplementaryTable1Hierarchyabbrcolorcode.xlsx Supplementary Table 1-Tumor_Hierarchy_abbreviation_color_code SupplementaryTable2EBRAINSclasscolorcode.xlsx Supplementary Table 2-EBRAINS_classes_color_code Cite Share Download PDF Status: Under Review 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-5600492","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388965095,"identity":"ea73c933-9ede-4b12-b5ff-e79163c243d7","order_by":0,"name":"Moritz 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Schrimpf, A.v.D., F.S., S.M.P. and D.T.W.J. are shareholders in and co-founders of Heidelberg Epignostix GmbH. AP and MS have become full-time employees of Heidelberg Epignostix GmbH since December and July 2024 respectively. D.Schrimpf has become a part-time employee of Heidelberg Epignostix GmbH since November 2024. SI is a consultant for Brainlab AG, Icotec AG and Carl Zeiss Meditec AG and has past honoraria from Nexstim AG. SMK is a consultant for Brainlab AG, Ulrich Medical and Need Inc., shareholder of Need Inc. and received honoraria from Nexstim Plc, Spineart Deutschland GmbH, Medtronic AG, and Carl Zeiss Meditec AG. None other declared.","formattedTitle":"AI-based histopathological classification of central nervous system tumours","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5600492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5600492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"DNA methylation analysis has become an essential diagnostic assay for classifying tumours of the central nervous system (CNS). However, this test requires additional resources and time compared to conventional histopathological diagnosis using haematoxylin and eosin (H\u0026E) stained tissue sections, which are available globally. \r\n\r\nHere, we propose to reduce time and resource requirements using Hetairos, an artificial intelligence (AI) algorithm that predicts 102 methylation-based CNS tumour subtypes from digital images of H\u0026E slides. Hetairos is built and assessed using over 11,000 slides from 10 centres across four continents. Hetairos produces well-calibrated probabilities for each subtype. Across cohorts, Hetairos identifies 50-70% of cases, which it classifies with high confidence and an accuracy of 0.87 for its highest-rated predictions. \r\n\r\nHetairos achieved higher accuracy than four board-certified neuropathologists in a direct comparison using only histology images (0.68 vs 0.28). A prospective evaluation within a routine diagnostic setting confirmed Hetairos’ performance and highlighted its 2-day turnaround time compared to an average of 12 days for molecular testing. Hetairos can assist diagnostic decision-making by limiting the number of likely diagnoses, informing efficient further testing, and by highlighting tissue areas indicative of specific diagnoses. These capabilities suggest Hetairos is a valuable tool assisting in the diagnosis of the full spectrum of paediatric and adult CNS tumours.","manuscriptTitle":"AI-based histopathological classification of central nervous system tumours","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 01:07:13","doi":"10.21203/rs.3.rs-5600492/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cancer","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcancer","sideBox":"Learn more about [Nature Cancer](http://www.nature.com/natcancer/)","snPcode":"","submissionUrl":"","title":"Nature Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8bb60c53-6567-487b-b589-67bf0369a11c","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":41453302,"name":"Biological sciences/Cancer/CNS cancer"},{"id":41453303,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2025-07-29T11:51:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 01:07:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5600492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5600492","identity":"rs-5600492","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}