Mp30-19 a Ct Urography-Based Nomogram for Predicting Preoperative Pathological Grade of Upper Urinary Tract Urothelial Carcinoma

You have accessJournal of UrologyImaging/Uroradiology II (MP30)1 May 2024MP30-19 A CT UROGRAPHY-BASED NOMOGRAM FOR PREDICTING PREOPERATIVE PATHOLOGICAL GRADE OF UPPER URINARY TRACT UROTHELIAL CARCINOMA YangHuang Zheng, HongJin Shi, Shi Fu, and Jinsong Zhang YangHuang ZhengYangHuang Zheng , HongJin ShiHongJin Shi , Shi FuShi Fu , and Jinsong ZhangJinsong Zhang View All Author Informationhttps://doi.org/10.1097/01.JU.0001009416.90901.7b.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: UTUC (Upper urinary tract urothelial carcinoma) is a relatively uncommon malignant neoplasm that arises in the renal pelvis and ureter, constituting merely 5-10% of all cases of urothelial carcinoma.Precise preoperative identification of the tumor's pathological grade is essential to guide subsequent treatment strategies effectively.Therefore, we have developed and validated a CT urography (CTU)-based nomogram for preoperative assessment of tumor pathological grading in UTUC. METHODS: A retrospective study was conducted on a total of 140 patients with UTUC who underwent CTU from January 2017 to August 2023. The features of the maximum cross-section of the tumor were extracted separately on the unenhanced, medullary, and excretion phases of CTU. Max-Relevance and Min-Redundancy (mRMR) and Least absolute shrinkage and selection operator algorithms (Lasso) were employed for feature screening. Univariate and multivariate logistic regression analyses (LR) were performed to identify the best independent clinical characteristics risk factors. Radiomics scores (Radscores) were calculated based on the screened features for each patient. Subsequently, a combined radiomics nomogram was established using both the best independent clinical characteristics risk factors and radiomics scores. The performance of the models was evaluated using the area under curve (AUC), DeLong tests, and decision curve analysis. RESULTS: A total of 98 patients (mean age: 64.5±10.5 years; 30 males) were included in the training set, while 42 patients (mean age: 65.3±9.78 years; 17 males) were included in the validation set. Hydronephrosis was an independent influencing factor of clinical baseline characteristics (p<0.05). The radiomics scores in the training set and validation set were significantly different between the low-grade group and the high-grade group (both p<0.05). In the LR model, the AUC of the validation set was 0.766 (95%CI 0.554-0.977), and the accuracy was 0.786. In addition, in the combined radiomics nomogram, the AUC of the validation set was 0.847 (95%CI 0.652-1.000), and the accuracy was 0.857. CONCLUSIONS: The radiomics-based nomogram developed in this study demonstrates good predictive performance, offering a noninvasive approach to predict the pathological grade of UTUC, and is anticipated to serve as a valuable adjunctive tool for routine preoperative biopsy. Source of Funding: Not applicable © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e501 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information YangHuang Zheng More articles by this author HongJin Shi More articles by this author Shi Fu More articles by this author Jinsong Zhang More articles by this author Expand All Advertisement PDF downloadLoading ...