{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T18:24:48Z","timestamp":1791829488601,"version":"4.3.4"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018556","name":"Science and Technology Program of Suzhou","doi-asserted-by":"publisher","award":["SLJ201906"],"award-info":[{"award-number":["SLJ201906"]}],"id":[{"id":"10.13039\/501100018556","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>This study aimed to develop a preoperative predictive model for pathological grading of bladder urothelial carcinoma by integrating multi-parameter, thin-slice enhanced computed tomography (CT) texture features with relevant clinical indicators.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>CT images and clinical data were retrospectively collected from 372 individuals diagnosed with bladder urothelial carcinoma at our institution between January 2015 and October 2020. The cohort was categorized into high-grade urothelial carcinoma (HGUC; n = 190) and low-grade urothelial carcinoma (LGUC; n = 182). Participants were randomly assigned to a training group (n = 259) and a validation group (n = 113) in a 7:3 ratio. Regions of interest (ROIs) were delineated on all enhanced CT images using 3D-Slicer software, and 1,223 texture features encompassing first-order, second-order, high-order, and filtered attributes were extracted. Features with an intraclass correlation coefficient (ICC) above 0.75 were retained for further analysis via least absolute shrinkage and selection operator (LASSO) regression. A logistic regression model was constructed based on the selected features to develop a clinical prediction tool. The model\u2019s performance was evaluated using the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Eleven radiomics features demonstrated significant associations with the pathological grade of bladder urothelial carcinoma. Among the models evaluated, the logistic regression model exhibited the highest discriminative power, with an area under the curve (AUC) of 0.858. Multivariate analysis identified age and proteinuria as independent predictors. The integrated model, incorporating both clinical and imaging features, outperformed models based on clinical or radiomic data alone (AUC = 0.864).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study presents the first CT-based nomogram that integrates multiparametric radiomic features with comprehensive clinical indicators to preoperatively predict pathological grade in bladder urothelial carcinoma. The model offers a robust, accurate, and non-invasive tool that can facilitate individualized treatment planning and enhance clinical decision-making.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2025.1661979","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T12:09:35Z","timestamp":1756296575000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Development of a radiomics and clinical feature-based nomogram for preoperative prediction of pathological grade in bladder 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