Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels

Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels | 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 Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels Gregor Duwe, Dominque Mercier, Verena Kauth, Lisa Maria Jost, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8747701/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 Multidisciplinary cancer conferences (MCC) provide an expert platform for evaluating and selecting the best possible oncological treatment options. Artificial intelligence (AI) can be a highly promising technology to complement additional treatment recommendations (TR). In this context, we developed an AI system supporting prostate cancer (PC) TRs. Data from PC patients receiving MCC recommendations (2015-2022) were converted into a machine-readable format to train classifiers replicating TRs using machine learning and deep learning techniques. A two-step process identified superordinate recommendation categories (high-level) before specifying detailed TR (low-level). AI training was performed on 5478 MCC cases (76 patient input and 23 output parameters). The AI system generated fully automated TR with excellent F1-scores for high-level (e.g. surgery (0.89), radiation therapy (0.81)) and low-level (e.g. prostatectomy (0.99), PSMA-Ligand therapy (0.98)). Explainability is provided by clinical features and their importance score. To our knowledge, this study presents the first AI-generated explainable TR in metastatic and non-metastatic PC with multi-target and feature space, achieving excellent performance results. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Health sciences/Urology artificial intelligence prostate cancer clinical oncology deep learning machine learning multidisciplinary cancer conferences treatment recommendation Full Text Additional Declarations No competing interests reported. Supplementary Files 20260115PCSupplementwithfiguresfinal.pdf 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-8747701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587741680,"identity":"511c4309-4aa6-4d4b-bf49-4342c61a7368","order_by":0,"name":"Gregor Duwe","email":"data:image/png;base64,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","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":true,"prefix":"","firstName":"Gregor","middleName":"","lastName":"Duwe","suffix":""},{"id":587741681,"identity":"8f568015-7fdd-4009-bf16-b3c2f5eade2b","order_by":1,"name":"Dominque Mercier","email":"","orcid":"","institution":"German Research Centre for Artificial Intelligence","correspondingAuthor":false,"prefix":"","firstName":"Dominque","middleName":"","lastName":"Mercier","suffix":""},{"id":587741682,"identity":"5375e86c-a4d6-4ef9-99de-87267ec51dca","order_by":2,"name":"Verena Kauth","email":"","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Verena","middleName":"","lastName":"Kauth","suffix":""},{"id":587741683,"identity":"14e288a2-6048-4a03-9dd8-97ff4416345f","order_by":3,"name":"Lisa Maria Jost","email":"","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"Maria","lastName":"Jost","suffix":""},{"id":587741684,"identity":"6983adda-9e2d-41d2-9f75-b450a3bb121f","order_by":4,"name":"Kerstin Moench","email":"","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Kerstin","middleName":"","lastName":"Moench","suffix":""},{"id":587741685,"identity":"a8c3cda1-17ae-4535-add9-eee91f9e154a","order_by":5,"name":"Vikas Rajashekar","email":"","orcid":"","institution":"German Research Centre for Artificial Intelligence","correspondingAuthor":false,"prefix":"","firstName":"Vikas","middleName":"","lastName":"Rajashekar","suffix":""},{"id":587741686,"identity":"e9f5d883-13c1-4610-97c0-034efc36be44","order_by":6,"name":"Markus Junker","email":"","orcid":"","institution":"German Research Centre for Artificial Intelligence","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Junker","suffix":""},{"id":587741687,"identity":"8a6f1e7a-f838-4b31-9d01-c7450b77d2f8","order_by":7,"name":"Christopher CM Neumann","email":"","orcid":"","institution":"Charité - University Medicine Berlin","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"CM","lastName":"Neumann","suffix":""},{"id":587741688,"identity":"11c68f9d-193e-44db-8722-712bdf9d1ffd","order_by":8,"name":"Andreas Dengel","email":"","orcid":"","institution":"German Research Centre for Artificial Intelligence","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Dengel","suffix":""},{"id":587741689,"identity":"f648b3d9-d959-420e-8c58-ffeb22be6ab2","order_by":9,"name":"Axel Haferkamp","email":"","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Axel","middleName":"","lastName":"Haferkamp","suffix":""},{"id":587741690,"identity":"7e9fe770-2089-4dd5-9fec-df896703647a","order_by":10,"name":"Thomas Höfner","email":"","orcid":"","institution":"Ordensklinikum Linz Barmherzige Schwestern","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Höfner","suffix":""}],"badges":[],"createdAt":"2026-01-31 07:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8747701/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8747701/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102748986,"identity":"64da688c-d673-48e1-9b51-0a19bf6c08fb","added_by":"auto","created_at":"2026-02-16 09:11:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1120770,"visible":true,"origin":"","legend":"","description":"","filename":"20260204PCManuscriptwithfiguresfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8747701/v1_covered_bfba9d8a-52fc-4ccc-b68c-c94b78aa09ed.pdf"},{"id":102294240,"identity":"1383771e-2fe0-421d-b07a-02fc64d78edd","added_by":"auto","created_at":"2026-02-10 09:49:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1198268,"visible":true,"origin":"","legend":"","description":"","filename":"20260115PCSupplementwithfiguresfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8747701/v1/f936641fc50821408f266be5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eExplainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels\u003c/p\u003e","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":"artificial intelligence, prostate cancer, clinical oncology, deep learning, machine learning, multidisciplinary cancer conferences, treatment recommendation","lastPublishedDoi":"10.21203/rs.3.rs-8747701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8747701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Multidisciplinary cancer conferences (MCC) provide an expert platform for evaluating and selecting the best possible oncological treatment options. Artificial intelligence (AI) can be a highly promising technology to complement additional treatment recommendations (TR). In this context, we developed an AI system supporting prostate cancer (PC) TRs. Data from PC patients receiving MCC recommendations (2015-2022) were converted into a machine-readable format to train classifiers replicating TRs using machine learning and deep learning techniques. A two-step process identified superordinate recommendation categories (high-level) before specifying detailed TR (low-level). AI training was performed on 5478 MCC cases (76 patient input and 23 output parameters). The AI system generated fully automated TR with excellent F1-scores for high-level (e.g. surgery (0.89), radiation therapy (0.81)) and low-level (e.g. prostatectomy (0.99), PSMA-Ligand therapy (0.98)). Explainability is provided by clinical features and their importance score. To our knowledge, this study presents the first AI-generated explainable TR in metastatic and non-metastatic PC with multi-target and feature space, achieving excellent performance results.","manuscriptTitle":"Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 09:49:38","doi":"10.21203/rs.3.rs-8747701/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":"5fc15daa-40a2-4d0b-a320-10e8116a74ec","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62646879,"name":"Biological sciences/Cancer"},{"id":62646880,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62646881,"name":"Health sciences/Oncology"},{"id":62646882,"name":"Health sciences/Urology"}],"tags":[],"updatedAt":"2026-02-15T19:09:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 09:49:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8747701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8747701","identity":"rs-8747701","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}