开发和验证基于多相 CT 的放射组学模型，用于局限性透明细胞肾细胞癌 (ccRCC) 患者的术前风险分层。共有 425 名局限性 ccRCC 患者入组，并分为训练组、验证组和外部测试组。从三相 CT 图像（未增强、动脉和静脉）中提取放射组学特征，并通过最小绝对收缩和选择算子 (LASSO) 回归算法构建放射组学特征。计算每位患者的放射组学评分（Rad-score）。通过结合重要的临床因素和 Rad 评分，建立了放射组学模型并将其可视化为列线图。通过受试者工作特征曲线、校准曲线和决策曲线分析（DCA）评估放射组学模型的预测性能。三相放射组学特征的AUC分别达到0.862（95% CI：0.809-0.914）、0.853（95%三个队列的 CI：0.785-0.921）和 0.837（95% CI：0.714-0.959）分别高于动脉、静脉和未增强的放射组学特征。多变量逻辑回归分析显示，Rad评分（OR：4.066，95％CI：3.495-8.790）和肾静脉侵犯（OR：12.914，95％CI：1.118-149.112）是独立的预测因子，用于开发放射组学模型。放射组学模型显示出良好的校准和辨别力，三个队列的 AUC 分别为 0.872 (95% CI: 0.821-0.923)、0.865 (95% CI: 0.800-0.930) 和 0.848 (95% CI: 0.728-0.967)，分别。 DCA显示了放射组学模型在预测Leibovich风险组方面的临床实用性。放射组学模型可以作为一种无创且有用的工具来预测局限性ccRCC患者的Leibovich风险组。基于三相CT的放射组学模型取得了良好的效果在术前预测局限性 ccRCC 患者的 Leibovich 风险组方面的表现。因此，它可以作为局部ccRCC患者术前风险分层的无创有效工具。 • 基于三相CT的放射组学特征比单相放射组学特征具有更好的性能。 • 放射组学在术前预测 ccRCC 的 Leibovich 风险组方面具有前景。 • 这项研究提供了一种对局部 ccRCC 患者进行分层的非侵入性方法。© 2023。欧洲放射学会 (ESR)。

To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC).A 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).The 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.The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients.The 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.• The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC.© 2023. European Society of Radiology (ESR).