{"paper_id":"0aa71108-e3aa-4df6-a74c-9f8cb031d4e6","body_text":"A Multimodal fusion model for predicting nasopharyngeal necrosis after reirradiation in recurrent nasopharyngeal carcinoma | 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 A Multimodal fusion model for predicting nasopharyngeal necrosis after reirradiation in recurrent nasopharyngeal carcinoma Xingwang Gao, Yinglin Peng, Jingjing Miao, Jiaxin Lin, Zhaodong Fei, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8516588/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 Objectives To develop a multimodal AI model integrating CT imaging, radiotherapy dose, and clinical features for predicting nasopharyngeal necrosis risk after re-irradiation in recurrent nasopharyngeal carcinoma (NPC). Methods This retrospective study included 126 patients with recurrent NPC (44 with necrosis and 82 without necrosis). Clinical baseline characteristics, radiomic features from planning CT images, and three-dimensional radiotherapy dose-derived radiomic features were collected and extracted. After two-stage feature selection, three single-modality and four multimodal fusion models were developed and evaluated on internal and external test sets using AUC, accuracy, sensitivity, and specificity. Results The multimodal fusion model demonstrated overall superior performance compared to all single-modality models. The CT-dose fusion model achieved AUCs of 0.869 and 0.972 in the internal and external test sets, respectively, outperforming the single CT, single-dose, and single clinical models. Conclusion The CT-dose fusion model demonstrated robust performance with strong generalizability for predicting nasopharyngeal necrosis, providing a valuable tool for early identification of high-risk patients and showing promise for clinical application. Biological sciences/Cancer Health sciences/Medical research Health sciences/Oncology Recurrent nasopharyngeal carcinoma radiation therapy nasopharyngeal necrosis multimodal omics features machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.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. 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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-8516588\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":574138169,\"identity\":\"92eafe79-a2d5-4643-8198-becdaa8bca26\",\"order_by\":0,\"name\":\"Xingwang Gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University Cancer Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xingwang\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"},{\"id\":574138170,\"identity\":\"36c0f360-1134-450d-91a9-215f17a1d657\",\"order_by\":1,\"name\":\"Yinglin 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carcinoma\",\"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\":\"info@researchsquare.com\",\"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\":\"Recurrent nasopharyngeal carcinoma, radiation therapy, nasopharyngeal necrosis, multimodal omics features, machine learning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8516588/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8516588/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eObjectives\\u003c/h2\\u003e \\u003cp\\u003eTo develop a multimodal AI model integrating CT imaging, radiotherapy dose, and clinical features for predicting nasopharyngeal necrosis risk after re-irradiation in recurrent nasopharyngeal carcinoma (NPC).\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThis retrospective study included 126 patients with recurrent NPC (44 with necrosis and 82 without necrosis). Clinical baseline characteristics, radiomic features from planning CT images, and three-dimensional radiotherapy dose-derived radiomic features were collected and extracted. After two-stage feature selection, three single-modality and four multimodal fusion models were developed and evaluated on internal and external test sets using AUC, accuracy, sensitivity, and specificity.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eThe multimodal fusion model demonstrated overall superior performance compared to all single-modality models. The CT-dose fusion model achieved AUCs of 0.869 and 0.972 in the internal and external test sets, respectively, outperforming the single CT, single-dose, and single clinical models.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThe CT-dose fusion model demonstrated robust performance with strong generalizability for predicting nasopharyngeal necrosis, providing a valuable tool for early identification of high-risk patients and showing promise for clinical application.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Multimodal fusion model for predicting nasopharyngeal necrosis after reirradiation in recurrent nasopharyngeal carcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-14 20:24:49\",\"doi\":\"10.21203/rs.3.rs-8516588/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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}}],\"origin\":\"\",\"ownerIdentity\":\"895aab2e-d1ab-43da-b1fb-ceb095350ac5\",\"owner\":[],\"postedDate\":\"January 14th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":61093446,\"name\":\"Biological sciences/Cancer\"},{\"id\":61093447,\"name\":\"Health sciences/Medical research\"},{\"id\":61093448,\"name\":\"Health sciences/Oncology\"}],\"tags\":[],\"updatedAt\":\"2026-02-18T09:41:25+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-14 20:24:49\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8516588\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8516588\",\"identity\":\"rs-8516588\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}