Integration of 2D/3D deep learning and radiomics for predicting lymphovascular invasion in T1-stage invasive lung adenocarcinoma: a multicenter study

Integration of 2D/3D deep learning and radiomics for predicting lymphovascular invasion in T1-stage invasive lung adenocarcinoma: a multicenter study | 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 Integration of 2D/3D deep learning and radiomics for predicting lymphovascular invasion in T1-stage invasive lung adenocarcinoma: a multicenter study Xiuhua Peng, Shan Pi, Hongxing Zhao, Hupo Bian, Wenhui Li, Dongping Deng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5122152/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 We developed a combined model integrating radiomics,2D deep learning (DL), and 3D DL to predict lymphovascular invasion (LVI) status in patients with T1-stage invasive lung adenocarcinoma (LUAD). This retrospective study included 334 patients who underwent radical surgery from four academic medical centers. We constructed corresponding predictive models by extracting and analyzing conventional radiomic features, 2D DL features, and 3D DL features from the tumor regions in CT images. These features were then integrated to develop a combined model to identify LVI status in T1-stage invasive LUAD patients. The model's performance was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC). The established combined model demonstrated excellent performance in distinguishing LVI, with predictive capabilities superior to individual models, yielding AUC values of 0.958（95%CI :0.9294 - 0.9863）, 0.886（95%CI : 0.7938 - 0.9786）, and 0.884（95%CI : 0.8277 - 0.9401） for the training, internal validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the net benefit provided by the combined model surpassed that of other radiomic models, offering critical information for treatment decision-making. Biological sciences/Cancer Health sciences/Oncology Invasive lung adenocarcinoma deep learning radiomics lymphovascular invasion 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. 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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-5122152","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434065071,"identity":"46e48800-5f4b-496a-a4fe-b23f1733ddd4","order_by":0,"name":"Xiuhua Peng","email":"","orcid":"","institution":"Department of Radiology, The First People’s Hospital of Huzhou","correspondingAuthor":false,"prefix":"","firstName":"Xiuhua","middleName":"","lastName":"Peng","suffix":""},{"id":434065072,"identity":"322d009b-522a-4aad-8378-dfdfe67b82cd","order_by":1,"name":"Shan Pi","email":"","orcid":"","institution":"Department of Radiology, The Third Affiliated 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