Contrast-enhanced CT-based deep radiomics strategy reveals the lymph node status in bladder cancer and its biological significance

Contrast-enhanced CT-based deep radiomics strategy reveals the lymph node status in bladder cancer and its biological significance | 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 Contrast-enhanced CT-based deep radiomics strategy reveals the lymph node status in bladder cancer and its biological significance Lijuan Wang, Zixiao Liu, Yang Wang, Xing Tang, Haolin Huang, Wei Hu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4093179/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 Purpose: To precisely evaluate bladder cancer (BCa) lymph node (LN) status, we proposed a deep radiomics model that combines local and global information on the contrast-enhanced (CECT) images, and investigated the biological significance of image features for LN metastasis prediction. Methods: Patients’ pretherapy CECT images with pathologically confirmed LN status of BCa were retrospectively collected from four clinical centers and publicly shared on TCIA. Radiomics models, deep learning models, and hybrid models integrating both radiomics and deep learning features with different classifiers were developed. Receiver operating characteristic analysis, Mann–Whitney U tests, and decision curve analysis (DCA) were used for diagnostic performance evaluation. Finally, weighted correlation network analysis and functional enrichment analysis were adopted to determine the biological pathways significantly associated with these image features. Results: Eighty patients were finally enrolled in this study. The hybrid model with support vector machine classifier obtained the best performance, with the sensitivity of 0.923 and area under the curve of 0.960 (95% CI: 0.884, 1.000) after 20-round 5-fold cross-validation, which significantly (p < 0.05) outperformed the other models for the LN metastasis prediction. DCA further showed great clinical benefit of the hybrid model for the prediction task. Additionally, Six biological pathways associated with immune inflammatory response, tumor transformation and lymphatic metastasis were significantly correlated with these image features for the LN status identification. Conclusion: The hybrid model could achieve favorable sensitivity and precision for BCa LN metastasis prediction. Key biological pathways were closely associated with image features in the hybrid model for LN metastasis prediction. bladder cancer lymph node metastasis image features deep learning biological significance Full Text Additional Declarations No competing interests reported. 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-4093179","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280903242,"identity":"debaf338-b446-4745-bcd9-cc4a135e7c63","order_by":0,"name":"Lijuan Wang","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Wang","suffix":""},{"id":280903243,"identity":"b63b26af-6419-47b3-af87-f1dbec5942e3","order_by":1,"name":"Zixiao Liu","email":"","orcid":"","institution":"Air Force Medical 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13:16:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4093179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4093179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58062026,"identity":"02acfb99-bbf1-466e-8ad8-edcf969c9fee","added_by":"auto","created_at":"2024-06-10 15:52:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8637188,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4093179/v1_covered_65a84bcd-7127-4e9b-b733-2d55cc1053fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contrast-enhanced CT-based deep radiomics strategy reveals the lymph node status in bladder cancer and its biological 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significance","lastPublishedDoi":"10.21203/rs.3.rs-4093179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4093179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose: To precisely evaluate bladder cancer (BCa) lymph node (LN) status, we proposed a deep radiomics model that combines local and global information on the contrast-enhanced (CECT) images, and investigated the biological significance of image features for LN metastasis prediction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Patients’ pretherapy CECT images with pathologically confirmed LN status of BCa were retrospectively collected from four clinical centers and publicly shared on TCIA. Radiomics models, deep learning models, and hybrid models integrating both radiomics and deep learning features with different classifiers were developed. Receiver operating characteristic analysis, Mann–Whitney U tests, and decision curve analysis (DCA) were used for diagnostic performance evaluation. Finally, weighted correlation network analysis and functional enrichment analysis were adopted to determine the biological pathways significantly associated with these image features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Eighty patients were finally enrolled in this study. The hybrid model with support vector machine classifier obtained the best performance, with the sensitivity of 0.923 and area under the curve of 0.960 (95% CI: 0.884, 1.000) after 20-round 5-fold cross-validation, which significantly (p \u0026lt; 0.05) outperformed the other models for the LN metastasis prediction. DCA further showed great clinical benefit of the hybrid model for the prediction task. Additionally, Six biological pathways associated with immune inflammatory response, tumor transformation and lymphatic metastasis were significantly correlated with these image features for the LN status identification.\u003c/p\u003e\n\u003cp\u003eConclusion: The hybrid model could achieve favorable sensitivity and precision for BCa LN metastasis prediction. 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