UTUC-PNet: A Multimodal Deep Learning Model with Feature Attribution for Postoperative Recurrence Risk Prediction

UTUC-PNet: A Multimodal Deep Learning Model with Feature Attribution for Postoperative Recurrence Risk Prediction | 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 UTUC-PNet: A Multimodal Deep Learning Model with Feature Attribution for Postoperative Recurrence Risk Prediction Deshan Liu, Tong Zhao, Yuchao Wang, Xingfei Li, Bo Fan, Lingling Fang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7580591/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 Background and Objective: Upper tract urothelial carcinoma (UTUC) is a highly aggressive malignancy characterized by a high postoperative recurrence rate, posing substantial challenges in clinical management and prognosis. Accurate prediction of recurrence risk is critical for optimizing treatment strategies. However, conventional imaging-based assessments predominantly rely on expert interpretation, which is subjective and limited in capturing subtle prognostic features. Methods: This study proposes UTUC-PNet, a deep learning-based recurrence risk prediction framework that integrates contrast-enhanced CT imaging and key clinical indicators. The model combines deep feature learning with a multimodal fusion strategy to extract recurrence-associated patterns. A logistic regression-based feature attribution module is incorporated to quantify the contribution of clinical and imaging-derived variables, ensuring interpretability. A complete prediction pipeline is constructed, including feature representation, weighted risk computation, and score calibration. Results: UTUC-PNet demonstrated competitive predictive performance compared to conventional CNN models, with improvements in accuracy, generalization, and interpretability. On a real-world dataset, the model achieved an accuracy of 95.03% and an F1-score of 97.62%. The interpretability mechanism provides clinically meaningful insights by highlighting influential features in risk assessment. Conclusions: The proposed framework provides a promising and interpretable approach for recurrence risk stratification in postoperative UTUC patients. While the results are encouraging, further validation on larger, multi-center cohorts is required before clinical application. UTUC recurrence risk prediction multimodal neural network model 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-7580591","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526794170,"identity":"1a3579cb-4a43-48c6-af94-afd9c0cfa958","order_by":0,"name":"Deshan Liu","email":"","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":false,"prefix":"","firstName":"Deshan","middleName":"","lastName":"Liu","suffix":""},{"id":526794171,"identity":"e351218d-0299-431a-86ad-09b4657c3e2b","order_by":1,"name":"Tong Zhao","email":"","orcid":"","institution":"Liaoning Normal 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Accurate prediction of recurrence risk is critical for optimizing treatment strategies. However, conventional imaging-based assessments predominantly rely on expert interpretation, which is subjective and limited in capturing subtle prognostic features.\u003c/p\u003e\u003cp\u003eMethods: This study proposes UTUC-PNet, a deep learning-based recurrence risk prediction framework that integrates contrast-enhanced CT imaging and key clinical indicators. The model combines deep feature learning with a multimodal fusion strategy to extract recurrence-associated patterns. A logistic regression-based feature attribution module is incorporated to quantify the contribution of clinical and imaging-derived variables, ensuring interpretability. A complete prediction pipeline is constructed, including feature representation, weighted risk computation, and score calibration.\u003c/p\u003e\u003cp\u003eResults: UTUC-PNet demonstrated competitive predictive performance compared to conventional CNN models, with improvements in accuracy, generalization, and interpretability. On a real-world dataset, the model achieved an accuracy of 95.03% and an F1-score of 97.62%. The interpretability mechanism provides clinically meaningful insights by highlighting influential features in risk assessment.\u003c/p\u003e\u003cp\u003eConclusions: The proposed framework provides a promising and interpretable approach for recurrence risk stratification in postoperative UTUC patients. While the results are encouraging, further validation on larger, multi-center cohorts is required before clinical application.\u003c/p\u003e","manuscriptTitle":"UTUC-PNet: A Multimodal Deep Learning Model with Feature Attribution for Postoperative Recurrence Risk Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 09:56:22","doi":"10.21203/rs.3.rs-7580591/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"c55040c9-92af-457b-89f9-d3a1f043c526","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-05T11:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 09:56:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7580591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7580591","identity":"rs-7580591","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}