Development and validation of a clinical prediction model to aid radiologists optimize thyroid C-TIRADS classification

Development and validation of a clinical prediction model to aid radiologists optimize thyroid C-TIRADS classification | 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 Development and validation of a clinical prediction model to aid radiologists optimize thyroid C-TIRADS classification yu liang, yuan zou, zhou zou, bo ren, jing zhang, qin chen, Peng He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3831900/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 Aims To develop and validate a clinical prediction model to aid radiologist optimize the diagnostic classification of Chinese thyroid imaging reporting and data system (C-TIRADS) . Materials and methods A total of 1,659 patients from two hospitals participated in the study. The derivation set comprised 909 patients for model development and internal validation, while the external validation set included 750 patients. Multinomial logistics regression was used to establish the prediction model. In the derivation set, the ROC curve assessed model performance, and a line chart provided an intuitive visualization. For external validation, ROC and calibration curves were plotted to evaluate discrimination and calibration. Results The data revealed that C-TIRADS classification, abnormal sonogram of cervical lymph nodes, and changes in thyroid nodule size were the most significant predictors of C-TIRADS optimization. The predictive performance of the C-TIRADS optimized nomogram was 0.730 (95%CI 0.697-0.762), with a sensitivity of 63.2%, specificity of 74.9%, and accuracy of 67.7%. A risk threshold of 60% or higher effectively defines C-TIRADS optimization, while a threshold below 30% indicates inefficiency. The calibration curve indicates good consistency, and the clinical decision-making and impact curve demonstrate a favorable net benefit. Moreover, external validation confirms excellent discrimination in predicting C-TIRADS optimization (C index: 0.865, 95%CI 0.839-0.891). Conclusions We established an optimized C-TIRADS model that combines the imaging features of thyroid nodules with clinical risk factors. It may help radiologists to improve the diagnostic efficiency of TIRADS classification. Full Text Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.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. 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-3831900","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264997784,"identity":"b92bca7a-a0ac-4d78-a09d-3802615a3412","order_by":0,"name":"yu liang","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"yu","middleName":"","lastName":"liang","suffix":""},{"id":264997785,"identity":"eb09336f-b8b7-404b-a83e-1f15dacadf11","order_by":1,"name":"yuan zou","email":"","orcid":"","institution":"Affiliated Hospital of 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