Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study

作者全名："Liu, Huayun; Wei, Zongjie; Xv, Yingjie; Tan, Hao; Liao, Fangtong; Lv, Fajin; Jiang, Qing; Chen, Tao; Xiao, Mingzhao"

作者地址："[Liu, Huayun; Wei, Zongjie; Xv, Yingjie; Tan, Hao; Liao, Fangtong; Xiao, Mingzhao] Chongqing Med Univ, Affiliated Hosp 1, Dept Urol, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Lv, Fajin] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing, Peoples R China; [Jiang, Qing] Chongqing Med Univ, Dept Cardiol, Affiliated Hosp 2, Chongqing, Peoples R China; [Chen, Tao] Chongqing Med Univ, Dept Gastroenterol, Affiliated Hosp 1, Chongqing, Peoples R China"

通信作者："Xiao, MZ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Urol, 1 Youyi Rd, Chongqing 400016, Peoples R China."

来源：INSIGHTS INTO IMAGING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001082593200001

JCR分区：Q1

影响因子：4.1

年份：2023

卷号：14

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Radiomics; Clear cell renal cell carcinoma; Leibovich; Tomograph; Prognosis

摘要："ObjectiveTo develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC).MethodsA total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).ResultsThe AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809-0.914), 0.853 (95% CI: 0.785-0.921), and 0.837 (95% CI: 0.714-0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495-8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118-149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821-0.923), 0.865 (95% CI: 0.800-0.930), and 0.848 (95% CI: 0.728-0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups.ConclusionsThe radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients.Critical relevance statementThe triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC.Key points center dot The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature.center dot Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC.center dot This study provides a non-invasive method to stratify patients with localized ccRCC.Key points center dot The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature.center dot Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC.center dot This study provides a non-invasive method to stratify patients with localized ccRCC.Key points center dot The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature.center dot Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC.center dot This study provides a non-invasive method to stratify patients with localized ccRCC."

基金机构："We appreciate all radiologists, pathologists, and related staff in the First Affiliated Hospital of Chongqing Medical University and the Second Affiliated Hospital of Chongqing Medical University for their assistance in data collection, pathology evaluatio"

基金资助正文："We appreciate all radiologists, pathologists, and related staff in the First Affiliated Hospital of Chongqing Medical University and the Second Affiliated Hospital of Chongqing Medical University for their assistance in data collection, pathology evaluation, and ROI delineated."