Develop and validate clinical-radiomics models to predict the risk of postoperative bleeding after percutaneous nephrolithotomy for single stone

Develop and validate clinical-radiomics models to predict the risk of postoperative bleeding after percutaneous nephrolithotomy for single stone | 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 Develop and validate clinical-radiomics models to predict the risk of postoperative bleeding after percutaneous nephrolithotomy for single stone Dan Zeng, HongJin Shi, Ming Qiu, Haifeng Wang, Bing Hai, Jinsong Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9123726/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 To develop and validate clinical-radiomics models for predicting the risk of severe postoperative bleeding in patients with solitary renal or upper ureteral stones undergoing percutaneous nephrolithotomy (PCNL), clinical and imaging data of 190 patients who underwent PCNL at a single tertiary care center from January 2022 to March 2024 were retrospectively analyzed. Patients were divided into a bleeding group and a non-bleeding group based on the occurrence of severe postoperative bleeding. Clinical variables with statistically significant differences between the two groups were incorporated into the models. After delineating regions of interest (ROI) on preoperative CT images, radiomics features were extracted, and the least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and dimensionality reduction. A total of 12 clinical-radiomics machine learning (ML) models were constructed by combining clinical factors with the selected radiomics features. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA). The results showed that Linear Support Vector Classifier (Linear SVC), Gradient Boosting (GB), and Logistic Regression (LR) demonstrated superior predictive accuracy and discriminative ability, with GB achieving the best performance. GB, Extreme Gradient Boosting (XGBoost), Linear SVC, Support Vector Machine (SVM), and LR showed balanced sensitivity and specificity. DCA revealed that most of the models in this study have high clinical applicability. In conclusion, the ML models incorporating clinical variables and CT-based radiomics features demonstrate good performance in the early prediction of severe postoperative bleeding in patients with solitary renal or upper ureteral stones undergoing PCNL, and can assist clinicians in making early interventions to enhance the safety of PCNL. Percutaneous nephrolithotomy Radiomics Machine learning Postoperative bleeding Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf 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-9123726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613989230,"identity":"9978dce0-d217-4be2-a1a0-bc2fadf03835","order_by":0,"name":"Dan Zeng","email":"","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Zeng","suffix":""},{"id":613989235,"identity":"2bd022bb-ac37-4c58-829b-b8476b4fa87e","order_by":1,"name":"HongJin Shi","email":"","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical 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The results showed that Linear Support Vector Classifier (Linear SVC), Gradient Boosting (GB), and Logistic Regression (LR) demonstrated superior predictive accuracy and discriminative ability, with GB achieving the best performance. GB, Extreme Gradient Boosting (XGBoost), Linear SVC, Support Vector Machine (SVM), and LR showed balanced sensitivity and specificity. DCA revealed that most of the models in this study have high clinical applicability. 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