Lung Cancer Multimodal Auxiliary Diagnosis Based on Entropy Weight Decision Fusion

Lung Cancer Multimodal Auxiliary Diagnosis Based on Entropy Weight Decision Fusion | 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 Lung Cancer Multimodal Auxiliary Diagnosis Based on Entropy Weight Decision Fusion Haixiang Zhang, Yuhong Tang, Peipei Li, Weijian Fan, Xiangzi Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8576661/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Lung cancer is one of the malignant tumors with the highest incidence and mortality rates worldwide. Traditional clinical diagnosis relies heavily on physicians' experience, which is associated with problems such as strong subjectivity, high rates of misdiagnosis and missed diagnosis, and significant disparities in regional medical standards. With the breakthroughs of deep learning in computer vision and natural language processing (NLP), multimodal data-driven auxiliary diagnosis—integrating computed tomography (CT) images and clinical text—has emerged as a research hotspot. However, simple concatenation of heterogeneous image and text data often fails to achieve effective feature alignment, leading to suboptimal medical decision-making performance. To address the aforementioned issues, this paper proposes a lung cancer multimodal auxiliary diagnosis model based on entropy weight decision fusion. Methods: This study adopted a retrospective cohort design, enrolling 5,847 patients from 2020 to 2025 (including 1,823 lung cancer patients, 2,253 normal control patients, and 1,771 pulmonary nodule control patients) for the analysis of their images and CT reports. Three datasets were constructed, with each randomly sampled from the original dataset. The study incorporated ViT (Vision Transformer) and BERT (Bidirectional Encoder Representations from Transformers) as feature extractors for images and text, respectively, to extract high-dimensional semantic features from lung CT images and CT Imaging Report . Secondly, independent classifiers based on Multi-Layer Perceptron (MLP) were established to convert the embedding vectors of different modalities into predicted probability distributions (Logits). Finally, the entropy weight method was employed to adaptively fuse the decision results of images and text. Model performance was validated using 5-fold cross-validation, with evaluation metrics including the Area Under the Receiver Operating Characteristic Curve (AUC), Accuracy , Precision ,Recall , and F1-score. Results: The proposed method in this study can fully leverage the complementary information from CT images and imaging text multimodality. On the clinical lung cancer dataset, it achieved an accuracy of 0.9375, a precision of 0.9324, a Recall of 0.9322 ,and an F1-score of 0.9322, significantly improving diagnostic performance. Conclusion: This study validates that decision fusion of multimodal data outperforms single-modality models in terms of diagnostic Accuracy, Precision, and Recall on real-world lung cancer datasets. It provides an effective solution for clinical auxiliary diagnosis of lung cancer. ViT (Vision Transformer) BERT (Bidirectional Encoder Representations from Transformers) Deep learning Entropy weight decision fusion Lung cancer classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Mar, 2026 Reviews received at journal 01 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 26 Jan, 2026 Editor assigned by journal 18 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 11 Jan, 2026 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-8576661","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580848419,"identity":"4c690022-0b7e-4ac3-a3c9-2ee114d1e38e","order_by":0,"name":"Haixiang Zhang","email":"","orcid":"","institution":"The Second People’s Hospital of Hefei","correspondingAuthor":false,"prefix":"","firstName":"Haixiang","middleName":"","lastName":"Zhang","suffix":""},{"id":580848420,"identity":"fcd7cd13-2c8f-4c66-9d6e-d9653e5428f4","order_by":1,"name":"Yuhong Tang","email":"","orcid":"","institution":"Hefei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuhong","middleName":"","lastName":"Tang","suffix":""},{"id":580848421,"identity":"12e6238a-a2df-45d0-bd7a-bd891fd3793a","order_by":2,"name":"Peipei Li","email":"","orcid":"","institution":"Hefei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Li","suffix":""},{"id":580848422,"identity":"faba811c-8bba-4560-89e3-c738aca429a4","order_by":3,"name":"Weijian Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACef7+BwcSKmrk2NgbiNRiOOMM44MHZ44Z8/McINaaAznMhg/bmBMlZyQQqYOx4ewxicQ2tgSDm4833mCosYkmqIWduS9NIuGcTJ7B7bRiC4ZjabkNhG05YCaRUMZWbHA7x0yCseEwYS0MBxKAWtiYEzfcPEO0lhxjgwSg92fO4CFSi+GMY4kPEsCBDPRLAjF+kedvPnDwBzgqD2+88aHGhgiHIQEDiQRSlEO0kKpjFIyCUTAKRgYAADUcRLzJt7CzAAAAAElFTkSuQmCC","orcid":"","institution":"The Second People’s Hospital of Hefei","correspondingAuthor":true,"prefix":"","firstName":"Weijian","middleName":"","lastName":"Fan","suffix":""},{"id":580848424,"identity":"82c460c1-60fe-4a72-a2c9-187565e9a3a2","order_by":4,"name":"Xiangzi Chen","email":"","orcid":"","institution":"The Second People’s Hospital of Hefei","correspondingAuthor":false,"prefix":"","firstName":"Xiangzi","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-12 02:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8576661/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8576661/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101397806,"identity":"767734d8-e521-46d6-aead-6eabb810f48d","added_by":"auto","created_at":"2026-01-29 09:37:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":512177,"visible":true,"origin":"","legend":"","description":"","filename":"LungCancerMultimodalAuxiliaryDiagnosisBasedonEntropyWeightDecisionFusion0106.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8576661/v1_covered_c67198d0-dd04-47f2-bf9b-e1c03a57a3ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lung Cancer Multimodal Auxiliary Diagnosis Based on Entropy Weight Decision Fusion","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ViT (Vision Transformer), BERT (Bidirectional Encoder Representations from Transformers), Deep learning, Entropy weight decision fusion, Lung cancer classification","lastPublishedDoi":"10.21203/rs.3.rs-8576661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8576661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Lung cancer is one of the malignant tumors with the highest incidence and mortality rates worldwide. Traditional clinical diagnosis relies heavily on physicians' experience, which is associated with problems such as strong subjectivity, high rates of misdiagnosis and missed diagnosis, and significant disparities in regional medical standards. With the breakthroughs of deep learning in computer vision and natural language processing (NLP), multimodal data-driven auxiliary diagnosis—integrating computed tomography (CT) images and clinical text—has emerged as a research hotspot. However, simple concatenation of heterogeneous image and text data often fails to achieve effective feature alignment, leading to suboptimal medical decision-making performance. To address the aforementioned issues, this paper proposes a lung cancer multimodal auxiliary diagnosis model based on entropy weight decision fusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study adopted a retrospective cohort design, enrolling 5,847 patients from 2020 to 2025 (including 1,823 lung cancer patients, 2,253 normal control patients, and 1,771 pulmonary nodule control patients) for the analysis of their images and CT reports. Three datasets were constructed, with each randomly sampled from the original dataset. The study incorporated ViT (Vision Transformer) and BERT (Bidirectional Encoder Representations from Transformers) as feature extractors for images and text, respectively, to extract high-dimensional semantic features from lung CT images and CT Imaging Report . Secondly, independent classifiers based on Multi-Layer Perceptron (MLP) were established to convert the embedding vectors of different modalities into predicted probability distributions (Logits). Finally, the entropy weight method was employed to adaptively fuse the decision results of images and text. Model performance was validated using 5-fold cross-validation, with evaluation metrics including the Area Under the Receiver Operating Characteristic Curve (AUC), Accuracy , Precision ,Recall , and F1-score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The proposed method in this study can fully leverage the complementary information from CT images and imaging text multimodality. On the clinical lung cancer dataset, it achieved an accuracy of 0.9375, a precision of 0.9324, a Recall of 0.9322 ,and an F1-score of 0.9322, significantly improving diagnostic performance.\u003c/p\u003e\n\u003cp\u003eConclusion: This study validates that decision fusion of multimodal data outperforms single-modality models in terms of diagnostic Accuracy, Precision, and Recall on real-world lung cancer datasets. It provides an effective solution for clinical auxiliary diagnosis of lung cancer.\u003c/p\u003e","manuscriptTitle":"Lung Cancer Multimodal Auxiliary Diagnosis Based on Entropy Weight Decision Fusion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 20:14:35","doi":"10.21203/rs.3.rs-8576661/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-17T11:34:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T00:51:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329922996246566809114297879337705694607","date":"2026-01-29T00:40:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135535551472342362878172231616372987971","date":"2026-01-27T03:24:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T03:11:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-18T20:03:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T03:18:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioMedical Engineering OnLine","date":"2026-01-12T02:48:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e53a392-7a26-43cd-9250-676bc402f878","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T15:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 20:14:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8576661","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8576661","identity":"rs-8576661","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}