From the Departments of Medical Ultrasound.
Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region.
J Comput Assist Tomogr. 2021;45(5):696-703. doi: 10.1097/RCT.0000000000001211.
The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients.
Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM.
The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup.
The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.
本研究旨在构建并验证一种计算机断层扫描（CT）放射组学模型，用于术前预测透明细胞肾细胞癌（ccRCC）患者的同步远处转移（SDM）。
本研究共纳入 172 例 ccRCC 患者。对增强 CT 图像进行手动勾画，并提取 2994 个定量放射组学特征。对放射组学特征进行归一化处理并进行假设检验。应用最小绝对收缩和选择算子（LASSO）进行降维、特征选择和模型构建。通过受试者工作特征曲线分析验证预测模型的性能。进行多变量和亚组分析以验证放射组学评分作为 SDM 的独立预测因子。
患者按 6:4 的比例随机分为训练集（n=104）和验证集（n=68）。通过 LASSO 回归进行降维，使用 9 个放射组学特征构建 SDM 预测模型。该模型在训练集（曲线下面积，0.89；95%置信区间，0.81-0.97）和验证集（曲线下面积，0.83；95%置信区间，0.69-0.95）中均具有中等性能。多变量分析表明，CT 放射组学特征是 ccRCC 临床参数的独立危险因素。亚组分析显示，SDM 与放射组学特征之间存在显著关联，除肾脏下极亚组外。
基于 CT 的放射组学模型可作为一种非侵入性、个性化的方法，用于预测 ccRCC 患者的 SDM。
