In-theatre prostate cancer tissue extraction guidance with Raman spectroscopy

In-theatre prostate cancer tissue extraction guidance with Raman spectroscopy | 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 In-theatre prostate cancer tissue extraction guidance with Raman spectroscopy Max J. Dooley, Hanlin Li, Irene Low, Mary L. Christie, Morgan Pokorny, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8825314/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 This study presents classification models trained to diagnose and grade prostate cancer using fresh prostate biopsies. We compare the performance of classification models with optimised sensitivity and specificity (standard models) with application-specific models designed to maximise sensitivity and negative predictive value (NPV). Standard models achieve 80% sensitivity and 81% specificity. Application-specific models, calibrated to 90% sensitivity and 95% NPV, are intended to provide clinicians with a tool they can use with confidence to support intraoperative decisions, specifically to improve tissue retention during biopsy procedures and to ensure clear surgical margins. To this end, we introduce a novel 5-layer algorithm that combines 5 application-specific models chosen for overall best performance. This algorithm can reduce the number of biopsy samples required for diagnosis by 47% while maintaining 90% sensitivity, 95% NPV, and 62% specificity. All models are independently validated using two large patient cohorts. These results support the targeted use of Raman spectroscopy for real-time tissue analysis in diagnostic and intraoperative settings. The technology’s clinical value as a decision-support tool aligns with the shared goal of pathologists and urologists to reduce the number of prostate biopsy cores while maintaining high sensitivity for clinically significant cancer. Prior studies have improved biopsy efficiency, but their performance has been variable, and concerns remain regarding underdetection of significant disease, revealing the need for approaches that improve biopsy efficiency without increasing diagnostic risk. The technology described here provides a realistic solution for targeted biopsy guidance to support more precise and evidence-based clinical decisions. Biological sciences/Cancer Physical sciences/Engineering Health sciences/Medical research Health sciences/Oncology 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. 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-8825314","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615452002,"identity":"8d62734b-630f-4889-a7bf-943600ac43ce","order_by":0,"name":"Max J. 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We compare the performance of classification models with optimised sensitivity and specificity (standard models) with application-specific models designed to maximise sensitivity and negative predictive value (NPV). Standard models achieve 80% sensitivity and 81% specificity. Application-specific models, calibrated to 90% sensitivity and 95% NPV, are intended to provide clinicians with a tool they can use with confidence to support intraoperative decisions, specifically to improve tissue retention during biopsy procedures and to ensure clear surgical margins. To this end, we introduce a novel 5-layer algorithm that combines 5 application-specific models chosen for overall best performance. This algorithm can reduce the number of biopsy samples required for diagnosis by 47% while maintaining 90% sensitivity, 95% NPV, and 62% specificity. All models are independently validated using two large patient cohorts. 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