Research on Multicenter Ovarian Cancer Diagnosis Based on Federated Learning

Research on Multicenter Ovarian Cancer Diagnosis Based on Federated Learning | 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 Research on Multicenter Ovarian Cancer Diagnosis Based on Federated Learning Jianhu He, Min Wang, Jilei Xiao, Fenfen Wang, Xuan Yang, Liying Song, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8245367/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Due to the challenges of early diagnosis and high heterogeneity, ovarian cancer urgently requires precise diagnostic methods integrating multi-center data. This study establishes a cross-institutional collaboration framework based on federated learning (FL) to develop an auxiliary diagnostic model for benign and malignant ovarian cancer. Methods A total of 1,449 patients (752 benign, 697 malignant) from five hospitals were included. Forty-four laboratory indicators were extracted, and federated learning based on the FedAvg algorithm was conducted on a privacy computing platform developed by Healink to evaluate and compare the performance of four models: logistic regression, Softmax regression, neural network, and XGBoost. Results XGBoost showed the best performance on the test set, with an area under the curve (AUC) of 0.881 (95% CI: 0.864–0.898), an optimal threshold point (FPR = 0.237, TPR = 0.870), and a Youden index of 0.633, significantly outperforming other models (P < 0.05). The neural network demonstrated robust generalization ability, with the smallest AUC difference (0.002) between the training and test sets. Feature importance analysis showed that lactate dehydrogenase (LDH, SHAP value + 0.28 ± 0.12) and platelet count (PLT, SHAP value + 0.25 ± 0.09) were the core predictive indicators, reflecting tumor metabolic activity and coagulation activation respectively, which were highly consistent with the pathological mechanisms of ovarian cancer. Conclusion The federated learning framework effectively integrates multi-center data, and the XGBoost model provides a reliable tool for pre-surgical auxiliary diagnosis of ovarian cancer. Incorporating more clinical features is needed in the future to improve accuracy. Meanwhile, through the ICER economic benefit analysis, it can be proved that the AI diagnostic model improves the health quality of hospitals and patients after treatment. Establishing a more complete long-term disease change model can provide a more comprehensive economic benefit analysis. Ovarian cancer Federated learning Multi-center collaboration XGBoost model AUC Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Feb, 2026 Reviews received at journal 27 Jan, 2026 Reviews received at journal 10 Jan, 2026 Reviewers agreed at journal 28 Dec, 2025 Reviewers agreed at journal 27 Dec, 2025 Reviewers agreed at journal 25 Dec, 2025 Reviewers invited by journal 25 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 30 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8245367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566489492,"identity":"6c9500c6-b7a0-46f6-9884-6bb28c0ee603","order_by":0,"name":"Jianhu He","email":"","orcid":"","institution":"Women's Hospital, Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jianhu","middleName":"","lastName":"He","suffix":""},{"id":566489494,"identity":"fa039888-40c7-43fc-8324-bdd0c4207b32","order_by":1,"name":"Min Wang","email":"","orcid":"","institution":"Shaoxing Maternity and Child Health Care 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This study establishes a cross-institutional collaboration framework based on federated learning (FL) to develop an auxiliary diagnostic model for benign and malignant ovarian cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 1,449 patients (752 benign, 697 malignant) from five hospitals were included. Forty-four laboratory indicators were extracted, and federated learning based on the FedAvg algorithm was conducted on a privacy computing platform developed by Healink to evaluate and compare the performance of four models: logistic regression, Softmax regression, neural network, and XGBoost.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eXGBoost showed the best performance on the test set, with an area under the curve (AUC) of 0.881 (95% CI: 0.864\u0026ndash;0.898), an optimal threshold point (FPR\u0026thinsp;=\u0026thinsp;0.237, TPR\u0026thinsp;=\u0026thinsp;0.870), and a Youden index of 0.633, significantly outperforming other models (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The neural network demonstrated robust generalization ability, with the smallest AUC difference (0.002) between the training and test sets. Feature importance analysis showed that lactate dehydrogenase (LDH, SHAP value\u0026thinsp;+\u0026thinsp;0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12) and platelet count (PLT, SHAP value\u0026thinsp;+\u0026thinsp;0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09) were the core predictive indicators, reflecting tumor metabolic activity and coagulation activation respectively, which were highly consistent with the pathological mechanisms of ovarian cancer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe federated learning framework effectively integrates multi-center data, and the XGBoost model provides a reliable tool for pre-surgical auxiliary diagnosis of ovarian cancer. Incorporating more clinical features is needed in the future to improve accuracy. Meanwhile, through the ICER economic benefit analysis, it can be proved that the AI diagnostic model improves the health quality of hospitals and patients after treatment. Establishing a more complete long-term disease change model can provide a more comprehensive economic benefit analysis.\u003c/p\u003e","manuscriptTitle":"Research on Multicenter Ovarian Cancer Diagnosis Based on Federated Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 04:12:43","doi":"10.21203/rs.3.rs-8245367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-07T14:10:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T12:50:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T18:30:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69122269266589888744342528912858005897","date":"2025-12-28T21:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191097581882448557203963309386218815617","date":"2025-12-28T03:31:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31452103197475840237131644533074536098","date":"2025-12-25T17:28:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-25T15:04:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T12:58:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T12:56:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2025-12-01T02:29:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb3a8a0f-f68d-4cd2-8d4c-e396abfbcae7","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T13:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 04:12:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8245367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8245367","identity":"rs-8245367","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}