Enhancing Lymph Node Metastases Assessment in Breast Cancer Post-Neoadjuvant Therapy Using artificial intelligence-Driven Diagnostics

Enhancing Lymph Node Metastases Assessment in Breast Cancer Post-Neoadjuvant Therapy Using artificial intelligence-Driven Diagnostics | 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 Enhancing Lymph Node Metastases Assessment in Breast Cancer Post-Neoadjuvant Therapy Using artificial intelligence-Driven Diagnostics Yan Ding, Juan Yu, Min Liu, Xiangyu Liu, Ling Kang, Liujing Huang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9159505/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Neoadjuvant therapy (NAT) is crucial for locally advanced breast cancer, but post-NAT lymph node assessment is challenging due to histological changes. Current methods like immunohistochemistry (IHC) are labor-intensive and imprecise in distinguishing isolated tumor cells (ITCs), micro-metastases (Micro), and macro-metastases (Macro). We aimed to develop and validate an AI-driven model for precise classification of lymph node metastasis status (negative, ITC, Micro, Macro) in breast cancer patients post-NAT. Methods We used a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) framework to analyze 7,764 lymph node samples from 7 cohorts. The CLAM model identifies high-diagnostic-value subregions within whole-slide images (WSIs) and generates high-resolution interpretability heatmaps. Performance was evaluated using binary and multi-class metrics, with external validation on diverse datasets. A human-AI comparative analysis was conducted on 24 patient-derived lymph node sections. Results The AI model achieved an AUROC of 0.97 (95% CI: 0.962–0.977) in binary classification and an overall accuracy of 0.8436 (95% CI: 0.8282–0.8562) for multi-class differentiation. In the human-AI comparison, the model outperformed junior pathologists, reducing diagnostic discrepancies by 83%. Conclusion This study establishes a robust AI model that significantly improves the accuracy and efficiency of post-NAT lymph node metastasis assessment in breast cancer, automating classification and reducing pathologist workload. breast cancer neoadjuvant therapy lymph node metastasis artificial intelligence digital pathology CLAM framework Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 18 Mar, 2026 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-9159505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622301023,"identity":"f5630af4-e9ed-408a-b88d-5bb83558ead4","order_by":0,"name":"Yan Ding","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Ding","suffix":""},{"id":622301024,"identity":"aaf93875-492e-4309-98e8-8e0f112177ca","order_by":1,"name":"Juan Yu","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical 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Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, neoadjuvant therapy, lymph node metastasis, artificial intelligence, digital pathology, CLAM framework","lastPublishedDoi":"10.21203/rs.3.rs-9159505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9159505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNeoadjuvant therapy (NAT) is crucial for locally advanced breast cancer, but post-NAT lymph node assessment is challenging due to histological changes. Current methods like immunohistochemistry (IHC) are labor-intensive and imprecise in distinguishing isolated tumor cells (ITCs), micro-metastases (Micro), and macro-metastases (Macro). We aimed to develop and validate an AI-driven model for precise classification of lymph node metastasis status (negative, ITC, Micro, Macro) in breast cancer patients post-NAT.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) framework to analyze 7,764 lymph node samples from 7 cohorts. The CLAM model identifies high-diagnostic-value subregions within whole-slide images (WSIs) and generates high-resolution interpretability heatmaps. Performance was evaluated using binary and multi-class metrics, with external validation on diverse datasets. A human-AI comparative analysis was conducted on 24 patient-derived lymph node sections.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe AI model achieved an AUROC of 0.97 (95% CI: 0.962\u0026ndash;0.977) in binary classification and an overall accuracy of 0.8436 (95% CI: 0.8282\u0026ndash;0.8562) for multi-class differentiation. In the human-AI comparison, the model outperformed junior pathologists, reducing diagnostic discrepancies by 83%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study establishes a robust AI model that significantly improves the accuracy and efficiency of post-NAT lymph node metastasis assessment in breast cancer, automating classification and reducing pathologist workload.\u003c/p\u003e","manuscriptTitle":"Enhancing Lymph Node Metastases Assessment in Breast Cancer Post-Neoadjuvant Therapy Using artificial intelligence-Driven Diagnostics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 11:22:25","doi":"10.21203/rs.3.rs-9159505/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-22T11:29:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99611204735576257599119713014446162194","date":"2026-04-13T12:57:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T11:57:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T07:42:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T05:36:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research","date":"2026-03-18T12:29:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d461c89-c10f-4304-942b-b2b53b593613","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T11:22:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 11:22:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9159505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9159505","identity":"rs-9159505","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}