Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients

Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients | 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 Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients Abdul Rehman Akbar, Usama Sajjad, Ziyu Su, Wencheng Li, Fei Xing, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8089525/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Recurrence remains a major clinical challenge in surgically resected invasive lung adenocarcinoma, where existing grading and staging systems fail to capture the cellular complexity that underlies tumor aggressiveness. We present PathRosetta , a novel AI model that conceptualizes histopathology as a language, where cells serve as words, spatial neighborhoods form syntactic structures, and tissue architecture composes sentences. By learning this language of histopathology, PathRosetta predicts five-year recurrence directly from hematoxylin-and-eosin (H&E) slides, treating them as documents representing the state of the disease. In a multi-cohort dataset of 289 patients ( 600 slides ), PathRosetta achieved an area under the curve (AUC) of 0.78 ± 0.04 on the internal cohort, significantly outperforming IASLC grading (AUC:0.71), AJCC staging (AUC:0.64), and other state-of-the-art AI models (AUC:0.62–0.67). It yielded a hazard ratio of 9.54 and a concordance index of 0.70 , generalized robustly to external TCGA (AUC:0.75) and CPTAC (AUC:0.76) cohorts, and performed consistently across demographic and clinical subgroups. Beyond whole-slide prediction, PathRosetta uncovered prognostic subgroups within individual cell types , revealing that even within benign epithelial, stromal, or other cells, distinct morpho-spatial phenotypes correspond to divergent outcomes. Moreover, because the model explicitly understands what it is looking at, including cell types, cellular neighborhoods, and higher-order tissue morphology, it is inherently interpretable and can articulate the rationale behind its predictions. These findings establish that representing histopathology as a language enables interpretable and generalizable prognostication from routine histology. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Full Text Additional Declarations No competing interests reported. Supplementary Files PathRosettanpjDigitalMedSupplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers invited by journal 18 Jan, 2026 Editor assigned by journal 19 Nov, 2025 Submission checks completed at journal 19 Nov, 2025 First submitted to journal 11 Nov, 2025 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. 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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-8089525","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":557352208,"identity":"91134924-f22b-47f1-b742-47a57a52a1fa","order_by":0,"name":"Abdul Rehman 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Patients","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8089525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8089525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecurrence remains a major clinical challenge in surgically resected invasive lung adenocarcinoma, where existing grading and staging systems fail to capture the cellular complexity that underlies tumor aggressiveness. We present \u003cb\u003ePathRosetta\u003c/b\u003e, a novel AI model that conceptualizes histopathology as a language, where cells serve as words, spatial neighborhoods form syntactic structures, and tissue architecture composes sentences. By learning this language of histopathology, PathRosetta predicts five-year recurrence directly from hematoxylin-and-eosin (H\u0026amp;E) slides, treating them as documents representing the state of the disease. In a multi-cohort dataset of \u003cb\u003e289 patients\u003c/b\u003e (\u003cb\u003e600 slides\u003c/b\u003e), PathRosetta achieved an area under the curve (AUC) of \u003cb\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/b\u003e on the internal cohort, significantly outperforming IASLC grading (AUC:0.71), AJCC staging (AUC:0.64), and other state-of-the-art AI models (AUC:0.62\u0026ndash;0.67). It yielded a \u003cb\u003ehazard ratio of 9.54\u003c/b\u003e and a \u003cb\u003econcordance index of 0.70\u003c/b\u003e, generalized robustly to external TCGA (AUC:0.75) and CPTAC (AUC:0.76) cohorts, and performed consistently across demographic and clinical subgroups. Beyond whole-slide prediction, PathRosetta uncovered \u003cb\u003eprognostic subgroups within individual cell types\u003c/b\u003e, revealing that even within benign epithelial, stromal, or other cells, distinct morpho-spatial phenotypes correspond to divergent outcomes. Moreover, because the model explicitly understands what it is looking at, including cell types, cellular neighborhoods, and higher-order tissue morphology, it is inherently interpretable and can articulate the rationale behind its predictions. These findings establish that representing histopathology as a language enables interpretable and generalizable prognostication from routine histology.\u003c/p\u003e","manuscriptTitle":"Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:05:24","doi":"10.21203/rs.3.rs-8089525/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T01:18:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T14:56:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T02:51:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T18:42:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T17:03:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T23:02:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14066361793111896362542589366958670355","date":"2026-01-22T07:12:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251475443092653031234209223338980252878","date":"2026-01-21T14:00:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337895706370649246890852170467588635800","date":"2026-01-19T19:46:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79857340564522760235853380062934637748","date":"2026-01-19T16:47:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155296409967793772719470494720392590357","date":"2026-01-19T13:45:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129469373439416229245709800746299395504","date":"2026-01-18T19:33:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86802170644746212086617014896953700827","date":"2026-01-18T16:18:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-18T13:24:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T01:23:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-19T17:52:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2025-11-11T18:12:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"918564c6-5b03-4e02-ba77-0592c87706c5","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59317755,"name":"Biological sciences/Cancer"},{"id":59317756,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59317757,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-30T17:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:05:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8089525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8089525","identity":"rs-8089525","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}