Predicting Lung Cancer Development using a Multimodal Longitudinal Feature Analysis Framework

Predicting Lung Cancer Development using a Multimodal Longitudinal Feature Analysis Framework | 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 Predicting Lung Cancer Development using a Multimodal Longitudinal Feature Analysis Framework Ning Xiao, Jiahao Han, Yan Qiang, Juanjuan Zhao, Yan Geng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3872105/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 The prediction of pulmonary nodules being canceration is of vital importance to the diagnosis and treatment of early lungadenocarcinoma. However, most traditional research paradigms only focus on image data at a single point in time, whichmakes it easy to ignore the correlation of lung computer tomography (CT) at multiple points in time. Based on the the correlationbetween longitudinal images and medical characteristics of patients with pulmonary nodules, a multimodal feature analysisframework is proposed in this paper.The multimodal analysis framework systematically evaluates the risk of cancer progressionin patients by incorporating diverse sources of information, including radiological features, depth features, and relevant riskfactors within lung cancer imaging. The experiment result show that the accuracy of nodule canceration prediction reached89.15%, which proved the effectiveness of the method proposed in this study. This comprehensive framework holds greatpromise in contributing to more precise diagnostics and personalized treatment strategies in the context of lung cancer. Biological sciences/Cancer/Cancer imaging Biological sciences/Cancer/Lung cancer Physical sciences/Mathematics and computing/Computer science 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-3872105","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":272146375,"identity":"9f441bed-f63a-4dfe-bb1e-e0feb7becbd3","order_by":0,"name":"Ning Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3OsWrDMBCAYQmDvVzQKuHSZzjjoRRE8yoqAU0a8gjO0izKHmhfoltHFQ1Z/ADpVJeAu2TI2NBAK8+N43TLoH84uIMPjpBY7BLLZrNmhxxYVoVNdic1QMD7YjmVV8K6sOlzCNc6h52WuFRnEnQmfIUeUHy2OShJWGaQ7F/6iahqRB7ITW7KEpQmwm6RLup+wqhFhYHcPhq6GX15gmuDCX3oJ2kC6FT32Nuq8aB+yHiIsDS9rxxqwDUpNqAcQT5AhE08rVCCsKYsntQEeN1OXxcnCL5/zL8PBz5m2arlW3V3zeaT52Z/gvwJuuH+AWKxWCx2pF/L5E6EaQeMawAAAABJRU5ErkJggg==","orcid":"","institution":"Shanxi University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"Ning","middleName":"","lastName":"Xiao","suffix":""},{"id":272146376,"identity":"31864421-2578-40c3-a6cd-4ac00fb4f7ea","order_by":1,"name":"Jiahao Han","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Han","suffix":""},{"id":272146377,"identity":"bdd2bfb1-edd7-4fe1-b567-e7dbe43fa87f","order_by":2,"name":"Yan Qiang","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Qiang","suffix":""},{"id":272146378,"identity":"c2919b80-81d9-4147-9d65-93f048fada23","order_by":3,"name":"Juanjuan Zhao","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Juanjuan","middleName":"","lastName":"Zhao","suffix":""},{"id":272146379,"identity":"ea7387e0-db30-415d-9da4-f278f24538ca","order_by":4,"name":"Yan Geng","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Geng","suffix":""}],"badges":[],"createdAt":"2024-01-17 06:59:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3872105/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3872105/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58175648,"identity":"fa31c8cf-fe26-4c3d-be68-de46f4a58ca3","added_by":"auto","created_at":"2024-06-12 04:37:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2964368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872105/v1_covered_4b640adf-30ea-4ffc-8bd3-7aaa84fe0c65.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Lung Cancer Development using a Multimodal Longitudinal Feature Analysis Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3872105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3872105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The prediction of pulmonary nodules being canceration is of vital importance to the diagnosis and treatment of early lungadenocarcinoma. However, most traditional research paradigms only focus on image data at a single point in time, whichmakes it easy to ignore the correlation of lung computer tomography (CT) at multiple points in time. Based on the the correlationbetween longitudinal images and medical characteristics of patients with pulmonary nodules, a multimodal feature analysisframework is proposed in this paper.The multimodal analysis framework systematically evaluates the risk of cancer progressionin patients by incorporating diverse sources of information, including radiological features, depth features, and relevant riskfactors within lung cancer imaging. The experiment result show that the accuracy of nodule canceration prediction reached89.15%, which proved the effectiveness of the method proposed in this study. 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