Deep Learning for Preoperative MRI-Based Endometrial Cancer Staging Prediction

Deep Learning for Preoperative MRI-Based Endometrial Cancer Staging Prediction | 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 Deep Learning for Preoperative MRI-Based Endometrial Cancer Staging Prediction Caili Gong, Yetong Qi, Ying Su, Tianjiao Li, Yongfeng Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8125996/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Purpose:Endometrial carcinoma ranks among the most common malignancies of the female reproductive system. Accurate early-stage staging is essential for devising appropriate treatment plans and assessing patient prognosis. This study aims to enhance diagnostic precision by overcoming the limitations of traditional imaging methods and existing deep learning models. Methods:To address challenges such as dependency on physician expertise, inefficiency, and deficiencies in feature transmission, boundary detail restoration, and multi-scale feature integration, we propose a novel architecture termed GCMF-UNet. Furthermore, for classification tasks, we introduce MSFA-Net, which integrates a ResNet-18 backbone with a multi-scale feature aggregation module, squeeze-and-excitation (SE) attention, and a Swin Transformer for global contextual modeling. Results:Experimental results indicate that GCMF-UNet surpasses the traditional U-Net by 4.1%–5.5% in key evaluation metrics, including Accuracy and Recall. In classification performance, MSFA-Net achieves a 2.4%–3.7% improvement over baseline models across multiple quantitative indicators, demonstrating enhanced capability in identifying critical lesion regions. Conclusion:The proposed GCMF-UNet and MSFA-Net architectures effectively mitigate limitations of conventional diagnostic and deep learning approaches, offering more accurate lesion segmentation and classification. These advancements provide a promising foundation for improving automated diagnosis and staging of endometrial carcinoma. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Endometrial Cancer Deep learning Medical image segmentation Tumor classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 26 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 24 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. We do this by developing innovative software and high quality services for the global research community. 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Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometrial Cancer, Deep learning, Medical image segmentation, Tumor classification","lastPublishedDoi":"10.21203/rs.3.rs-8125996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8125996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose:Endometrial carcinoma ranks among the most common malignancies of the female reproductive system. Accurate early-stage staging is essential for devising appropriate treatment plans and assessing patient prognosis. This study aims to enhance diagnostic precision by overcoming the limitations of traditional imaging methods and existing deep learning models.\u003c/p\u003e\n\u003cp\u003eMethods:To address challenges such as dependency on physician expertise, inefficiency, and deficiencies in feature transmission, boundary detail restoration, and multi-scale feature integration, we propose a novel architecture termed GCMF-UNet. Furthermore, for classification tasks, we introduce MSFA-Net, which integrates a ResNet-18 backbone with a multi-scale feature aggregation module, squeeze-and-excitation (SE) attention, and a Swin Transformer for global contextual modeling.\u003c/p\u003e\n\u003cp\u003eResults:Experimental results indicate that GCMF-UNet surpasses the traditional U-Net by 4.1%–5.5% in key evaluation metrics, including Accuracy and Recall. In classification performance, MSFA-Net achieves a 2.4%–3.7% improvement over baseline models across multiple quantitative indicators, demonstrating enhanced capability in identifying critical lesion regions.\u003c/p\u003e\n\u003cp\u003eConclusion:The proposed GCMF-UNet and MSFA-Net architectures effectively mitigate limitations of conventional diagnostic and deep learning approaches, offering more accurate lesion segmentation and classification. These advancements provide a promising foundation for improving automated diagnosis and staging of endometrial carcinoma.\u003c/p\u003e","manuscriptTitle":"Deep Learning for Preoperative MRI-Based Endometrial Cancer Staging Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 06:07:51","doi":"10.21203/rs.3.rs-8125996/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-06T05:39:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T17:53:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248638549104030453512684614560412044619","date":"2026-01-23T18:42:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310230266007575396619487667251045213815","date":"2026-01-23T12:44:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T09:59:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152473930880511140706823245389208148674","date":"2025-12-11T07:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232309210707116023637890615009825973046","date":"2025-12-11T05:51:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T02:46:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-26T15:50:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-25T04:29:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-25T04:27:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ebfde2e-19ae-4bc3-ab65-2553c6201642","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":59471110,"name":"Biological sciences/Cancer"},{"id":59471111,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59471112,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-21T05:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 06:07:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8125996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8125996","identity":"rs-8125996","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}