A Novel Machine Learning based Nomogram to predict Lymph Node Invasion in Prostate Cancer—Updating the Briganti Nomogram for UK Clinical Practice

A Novel Machine Learning based Nomogram to predict Lymph Node Invasion in Prostate Cancer—Updating the Briganti Nomogram for UK Clinical Practice | 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 A Novel Machine Learning based Nomogram to predict Lymph Node Invasion in Prostate Cancer—Updating the Briganti Nomogram for UK Clinical Practice Ranya V Kumar, Prakrit R Kumar, Cristian Ilie, Amit Patel, Vivekanandan Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7502508/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 Introduction According to EAU guidelines, the indication for extended pelvic lymph-node dissection(ePLND) at the time of radical prostatectomy (RP) should be based on validated nomograms. The Briganti 2017(B17) nomogram is considered the most appropriate to predict lymph node invasion (LNI) in our cohort because most patients were diagnosed via systematic biopsies. We aim to perform the first UK-based external validation of B17 and improve its accuracy using machine learning approaches through development of an updated UK-tailored Briganti Nomogram 2025 (UKB25). Methods From 1551 prostate cancer patients who underwent RP, 286 had ePLND between 2010 and 2021 in a tertiary referral centre. The Briganti 2017 variables were analysed with univariate and multivariate logistic regression models predicting LNI. All variables with an OR > 1 were included in the subsequent model. The UKB25 nomogram was developed using penalised logistic regression of the Briganti variables with elastic net regularisation. An optimal threshold was selected to maximise accuracy without increasing false negative rate. Performance metrics were compared between UKB25 and retrospective application of the Briganti 2017 nomogram. Results Of 286 patients, 23(8.0%) had LNI. Application of the Briganti 2017 nomogram, with a 7% threshold, to our population results in 8.7% of patients below the cut-off having LNI. This is unsatisfactory as per Briganti’s definition. Instead, using a 5.8% cutoff, the refined machine-learning-based UKB25 nomogram outperforms the Briganti 2017 nomogram in classification accuracy (McNemar’s test statistic χ2 = 34.2, p < 0.001). Importantly, UKB25 would have spared 52 patients from unnecessary ePLND (162 vs 214), thereby enhancing specificity (38.4% vs 18.6%) and PPV (11.9% vs 8.9%), while maintaining NPV (99% vs 96.1%), sensitivity (95.6% vs 91.3%) and area under ROC (0.785 vs 0.763). This improvement was achieved with a lower rate of missed LNI (4.2%), highlighting its clinical utility in balancing oncological safety with surgical morbidity. Conclusions Although the Briganti nomogram predicts LNI reliably in other populations, its accuracy is limited in the UK. The updated UKB25 nomogram shows superior predictive performance in the UK, reducing the number of unnecessary ePLNDs by one quarter without missing additional LNI-positive patients. Further external validation is warranted before widespread adoption. Prostate cancer Lymph node invasion Nomogram Machine learning Full Text Additional Declarations No competing interests reported. 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-7502508","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515307479,"identity":"082d56ce-938b-47a0-b749-57ff8fcc0d10","order_by":0,"name":"Ranya V Kumar","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Ranya","middleName":"V","lastName":"Kumar","suffix":""},{"id":515307480,"identity":"e4597ba8-7b25-4950-bcf4-262290001ab2","order_by":1,"name":"Prakrit R Kumar","email":"","orcid":"","institution":"University Hospitals Coventry and Warwickshire NHS 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Practice\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Prostate cancer, Lymph node invasion, Nomogram, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7502508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7502508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction\u003c/p\u003e\u003cp\u003eAccording to EAU guidelines, the indication for extended pelvic lymph-node dissection(ePLND) at the time of radical prostatectomy (RP) should be based on validated nomograms. The Briganti 2017(B17) nomogram is considered the most appropriate to predict lymph node invasion (LNI) in our cohort because most patients were diagnosed via systematic biopsies. We aim to perform the first UK-based external validation of B17 and improve its accuracy using machine learning approaches through development of an updated UK-tailored Briganti Nomogram 2025 (UKB25).\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eFrom 1551 prostate cancer patients who underwent RP, 286 had ePLND between 2010 and 2021 in a tertiary referral centre. The Briganti 2017 variables were analysed with univariate and multivariate logistic regression models predicting LNI. All variables with an OR\u0026thinsp;\u0026gt;\u0026thinsp;1 were included in the subsequent model. The UKB25 nomogram was developed using penalised logistic regression of the Briganti variables with elastic net regularisation. An optimal threshold was selected to maximise accuracy without increasing false negative rate. Performance metrics were compared between UKB25 and retrospective application of the Briganti 2017 nomogram.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eOf 286 patients, 23(8.0%) had LNI. Application of the Briganti 2017 nomogram, with a 7% threshold, to our population results in 8.7% of patients below the cut-off having LNI. This is unsatisfactory as per Briganti\u0026rsquo;s definition.\u003c/p\u003e\u003cp\u003eInstead, using a 5.8% cutoff, the refined machine-learning-based UKB25 nomogram outperforms the Briganti 2017 nomogram in classification accuracy (McNemar\u0026rsquo;s test statistic χ2\u0026thinsp;=\u0026thinsp;34.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Importantly, UKB25 would have spared 52 patients from unnecessary ePLND (162 vs 214), thereby enhancing specificity (38.4% vs 18.6%) and PPV (11.9% vs 8.9%), while maintaining NPV (99% vs 96.1%), sensitivity (95.6% vs 91.3%) and area under ROC (0.785 vs 0.763). This improvement was achieved with a lower rate of missed LNI (4.2%), highlighting its clinical utility in balancing oncological safety with surgical morbidity.\u003c/p\u003e\u003cp\u003eConclusions\u003c/p\u003e\u003cp\u003eAlthough the Briganti nomogram predicts LNI reliably in other populations, its accuracy is limited in the UK. The updated UKB25 nomogram shows superior predictive performance in the UK, reducing the number of unnecessary ePLNDs by one quarter without missing additional LNI-positive patients. Further external validation is warranted before widespread adoption.\u003c/p\u003e","manuscriptTitle":"A Novel Machine Learning based Nomogram to predict Lymph Node Invasion in Prostate Cancer—Updating the Briganti Nomogram for UK Clinical Practice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 18:51:08","doi":"10.21203/rs.3.rs-7502508/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":"91bc764c-2d49-4c75-a027-e4698cf104f1","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T09:11:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 18:51:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7502508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7502508","identity":"rs-7502508","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}