Xu Jinbin, Gao Shuntian, Zhu Qin, Dai Fuyang, Sun Ciming, Lee Weijen, Ye Yuedian, Deng Gengguo, Huang Zhansen, Li Xiaoming, Li Jiang, Cheong Samun, Huang Qunxiong, Di Jinming
Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Abdom Radiol (NY). 2025 Apr 18. doi: 10.1007/s00261-025-04956-2.
This study aimed to investigate the value of integrating computed tomography (CT)-based tumor radiomics features with clinical parameters for preoperative prediction of microvascular invasion (MVI) in clear cell renal cell carcinoma (ccRCC).
We retrospectively analyzed data from a single-center cohort of ccRCC patients. Radiomics features were extracted from preoperative multiphasic CT scans (unenhanced, corticomedullary, and nephrographic phases). Following dimensionality reduction and feature selection, eight machine learning algorithms were evaluated to identify the optimal radiomics model. Independent clinical predictors were determined through univariate and multivariate analyses. A nomogram integrating the radiomics signature (rad-score) with significant clinical parameters was subsequently developed. Model performance was assessed using the area under the curve (AUC), decision curve analysis (DCA), and calibration curve analysis (CAC).
Of 143 initially enrolled patients, 110 met inclusion criteria after screening, with 5502 radiomics features extracted. The support vector classifier (SVM) model demonstrated the highest discriminative ability, achieving mean AUCs of 0.976 (training cohort) and 0.892 (test cohort), significantly outperforming the clinical model (training AUC = 0.935, test AUC = 0.933). The nomogram showed superior diagnostic performance, with AUCs of 0.958 (test). DCA and CAC confirmed its clinical utility and robustness.
Multiparametric CT radiomics models enable non-invasive prediction of MVI status in ccRCC, with the SVM-based algorithm showing optimal performance. The integrated nomogram provides excellent and consistent diagnostic accuracy, offering a valuable preoperative tool for clinical decision-making.
本研究旨在探讨基于计算机断层扫描（CT）的肿瘤放射组学特征与临床参数相结合在透明细胞肾细胞癌（ccRCC）微血管侵犯（MVI）术前预测中的价值。
我们回顾性分析了来自单中心ccRCC患者队列的数据。从术前多期CT扫描（平扫、皮髓质期和肾实质期）中提取放射组学特征。在进行降维和特征选择后，评估了八种机器学习算法以确定最佳放射组学模型。通过单因素和多因素分析确定独立的临床预测因素。随后开发了一个将放射组学特征（rad-score）与重要临床参数相结合的列线图。使用曲线下面积（AUC）、决策曲线分析（DCA）和校准曲线分析（CAC）评估模型性能。
143例初始入组患者中，筛选后110例符合纳入标准，共提取5502个放射组学特征。支持向量分类器（SVM）模型表现出最高的判别能力，训练队列的平均AUC为0.976，测试队列的平均AUC为0.892，显著优于临床模型（训练AUC = 0.935，测试AUC = 0.933）。列线图显示出卓越的诊断性能，测试AUC为0.958。DCA和CAC证实了其临床实用性和稳健性。
多参数CT放射组学模型能够对ccRCC的MVI状态进行无创预测，基于SVM的算法表现出最佳性能。综合列线图提供了出色且一致的诊断准确性，为临床决策提供了有价值的术前工具。
