Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images | 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 Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images Xiaoge Zhang, Tao Wang, Chao Yan, Fedaa Najdawi, Kai Zhou, Yuan Ma, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5723270/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 Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings. Health sciences/Health care/Diagnosis/Pathology Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformtionofConformalUQEnableTrustworthyDecisions.pdf 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-5723270","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":422013251,"identity":"1b2e7964-1712-4c85-937d-60c713149cb8","order_by":0,"name":"Xiaoge Zhang","email":"data:image/png;base64,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","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoge","middleName":"","lastName":"Zhang","suffix":""},{"id":422013252,"identity":"852c5d2b-4187-4afa-a557-197a24a3dee2","order_by":1,"name":"Tao Wang","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Wang","suffix":""},{"id":422013253,"identity":"1e09abed-8556-4ee7-9ef1-64e1a95fb953","order_by":2,"name":"Chao Yan","email":"","orcid":"https://orcid.org/0000-0002-6719-1388","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Yan","suffix":""},{"id":422013254,"identity":"cb99cdc0-0594-4102-83a0-a8c96439d397","order_by":3,"name":"Fedaa Najdawi","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Fedaa","middleName":"","lastName":"Najdawi","suffix":""},{"id":422013255,"identity":"7de67a6d-1eab-494d-8d78-adbc4a5e4b0d","order_by":4,"name":"Kai Zhou","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhou","suffix":""},{"id":422013256,"identity":"b945ecd7-eee0-423b-8635-f789e9adc501","order_by":5,"name":"Yuan Ma","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Ma","suffix":""},{"id":422013257,"identity":"7556b81c-590a-4e2f-b2fa-3f9e37741f2e","order_by":6,"name":"Yiu-ming Cheung","email":"","orcid":"","institution":"Hong Kong Baptist University","correspondingAuthor":false,"prefix":"","firstName":"Yiu-ming","middleName":"","lastName":"Cheung","suffix":""},{"id":422013258,"identity":"9cec5ecd-4624-4de9-ab16-566d8ab04da1","order_by":7,"name":"Bradley Malin","email":"","orcid":"https://orcid.org/0000-0003-3040-5175","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Bradley","middleName":"","lastName":"Malin","suffix":""}],"badges":[],"createdAt":"2024-12-27 19:00:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5723270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5723270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79318841,"identity":"2e7dce39-e626-4cc4-8472-0782a4cfed95","added_by":"auto","created_at":"2025-03-27 04:12:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":214008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryInformtionofConformalUQEnableTrustworthyDecisions.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5723270/v1/bba11e98ef39d3cea1aa3267.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images","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-5723270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5723270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings.","manuscriptTitle":"Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 04:12:33","doi":"10.21203/rs.3.rs-5723270/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-biomedical-engineering","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natbiomedeng","sideBox":"Learn more about [Nature Biomedical Engineering](http://www.nature.com/natbiomedeng/)","snPcode":"41551","submissionUrl":"https://mts-natbiomedeng.nature.com/cgi-bin/main.plex","title":"Nature Biomedical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"30628ffe-5576-4541-b435-68ea8c3f2ac5","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":44984683,"name":"Health sciences/Health care/Diagnosis/Pathology"},{"id":44984684,"name":"Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer"},{"id":44984685,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2026-04-23T21:15:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-27 04:12:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5723270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5723270","identity":"rs-5723270","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}