Differentiating Endometrial Stromal Sarcoma from Cellular Leiomyoma Based on a Nomogram Integrating Multimodal MRI and Clinical Data

Differentiating Endometrial Stromal Sarcoma from Cellular Leiomyoma Based on a Nomogram Integrating Multimodal MRI and Clinical Data | 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 Differentiating Endometrial Stromal Sarcoma from Cellular Leiomyoma Based on a Nomogram Integrating Multimodal MRI and Clinical Data Yue Zhou, Yuanyuan Lu, Feiran Zhang, Yan Ning, Wentao Jin, Shulei Cai, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8312169/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This retrospective, dual-center study developed and validated a nomogram combining quantitative ADC metrics and MRI features to differentiate endometrial stromal sarcoma (ESS) from cellular leiomyoma (CL). A total of 155 women were included (ESS: n = 57, CL: n = 98), divided into a derivation cohort (n = 111) and an external validation cohort (n = 44). All participants underwent preoperative contrast-enhanced pelvic MRI. A nomogram incorporating irregular margin, cystic areas, and mean ADC value was constructed. The model achieved an area under the curve (AUC) of 0.828 (95% CI: 0.742–0.911) in the derivation cohort and 0.873 (95% CI: 0.768–0.977) in the external validation cohort. Sensitivity and specificity were 78.5% and 87.9% in the derivation set, and 87.5% and 75.0% in the validation set, respectively. In conclusion, this nomogram provides a simple, accurate, and non-invasive tool for the preoperative differentiation of ESS from CL and has been implemented as a user-friendly web-based calculator to facilitate clinical use. Biological sciences/Cancer Health sciences/Diseases Health sciences/Medical research Health sciences/Oncology Endometrial Stromal Sarcoma Leiomyoma Cellular Magnetic Resonance Imaging Nomogram Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Posted 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-8312169","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617491932,"identity":"0b01e482-feec-41a6-aa4f-eb23bf2bbff9","order_by":0,"name":"Yue Zhou","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhou","suffix":""},{"id":617491933,"identity":"80f73d6a-a41c-4704-bc73-21438984cfd2","order_by":1,"name":"Yuanyuan Lu","email":"","orcid":"","institution":"Tongji 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