Survival Prediction for Bladder Cancer Using Multimodal Data With Quantum Neural Networks and Transformer Architectures

Survival Prediction for Bladder Cancer Using Multimodal Data With Quantum Neural Networks and Transformer Architectures | 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 Survival Prediction for Bladder Cancer Using Multimodal Data With Quantum Neural Networks and Transformer Architectures Zhouyuan Qin¹, Hui Zhou¹, Yangsheng Hu¹, Jiang Lu, Jianfeng He¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6957444/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background: To address the challenges of cross-modal information fusion in high-dimensional multimodal medical data for cancer prognosis, this study presents a hybrid diagnostic accuracy model for cancer survival prediction, integrating quantum computing with classical deep learning in a retrospective analysis of bladder cancer patients. Methods: We propose QTMPN (Quantum-Transformer Multimodal Prognostic Network), a novel framework integrating quantum neural networks (QNNs), Transformers, and graph neural networks (GNNs). For high-dimensional whole-slide pathological images (WSIs), a quantum feature extractor (QFE) is designed using parallel quantum encoding and a hybrid quantum network to capture long-range dependencies. Multimodal data—including clinical and image features—are fused via a Transformer-GNN Collaborative Fusion (TCF) module employing attention-guided dynamic graphs. Results: Evaluated on the TCGA-BLCA dataset, QTMPN attained a survival prediction accuracy of 76.1% , outperforming baseline models such as PARADIGM and CMTA (up to 70.0%). This improvement suggests the model’s enhanced capability to capture cross-modal prognostic features. Further ablation experiment validated the effectiveness of the hybrid QNNs feature extract part (QFE) in QTMPN. Conclusions: QTMPN presents a promising quantum-classical framework for survival risk prediction in bladder cancer, effectively modeling complex multimodal interactions. The approach contributes to improving prognostic accuracy in oncology and supporting precision medicine. Biological sciences/Cancer/Cancer imaging Biological sciences/Cancer/Cancer genomics Biological sciences/Cancer/Cancer models Biological sciences/Cancer/Urological cancer/Bladder cancer Multimodal data fusion Quantum neural network Bladder cancer Survival prognosis Transformer architecture Hybrid quantum-classical model. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Nov, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviews received at journal 05 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers invited by journal 24 Jul, 2025 Editor invited by journal 18 Jul, 2025 Editor assigned by journal 07 Jul, 2025 Submission checks completed at journal 02 Jul, 2025 First submitted to journal 02 Jul, 2025 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-6957444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490082743,"identity":"30d689b4-a53c-4fbc-9ccc-612b8c9b6809","order_by":0,"name":"Zhouyuan Qin¹","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhouyuan","middleName":"","lastName":"Qin¹","suffix":""},{"id":490082744,"identity":"8b77989c-8e6d-4be4-aa98-9a6bd0cd2177","order_by":1,"name":"Hui Zhou¹","email":"","orcid":"","institution":"Kunming University of Science and 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Quantum neural network, Bladder cancer, Survival prognosis, Transformer architecture, Hybrid quantum-classical model.","lastPublishedDoi":"10.21203/rs.3.rs-6957444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6957444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eTo address the challenges of cross-modal information fusion in high-dimensional multimodal medical data for cancer prognosis, this study presents a hybrid diagnostic accuracy model for cancer survival prediction, integrating quantum computing with classical deep learning in a retrospective analysis of bladder cancer patients.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe propose QTMPN (Quantum-Transformer Multimodal Prognostic Network), a novel framework integrating quantum neural networks (QNNs), Transformers, and graph neural networks (GNNs). For high-dimensional whole-slide pathological images (WSIs), a quantum feature extractor (QFE) is designed using parallel quantum encoding and a hybrid quantum network to capture long-range dependencies. Multimodal data\u0026mdash;including clinical and image features\u0026mdash;are fused via a Transformer-GNN Collaborative Fusion (TCF) module employing attention-guided dynamic graphs.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eEvaluated on the TCGA-BLCA dataset, QTMPN attained a survival prediction accuracy of \u003cb\u003e76.1%\u003c/b\u003e, outperforming baseline models such as PARADIGM and CMTA (up to 70.0%). This improvement suggests the model\u0026rsquo;s enhanced capability to capture cross-modal prognostic features. Further ablation experiment validated the effectiveness of the hybrid QNNs feature extract part (QFE) in QTMPN.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eQTMPN presents a promising quantum-classical framework for survival risk prediction in bladder cancer, effectively modeling complex multimodal interactions. 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