Gao Ruizhi, Qin Hui, Lin Peng, Ma Chenjun, Li Chengyang, Wen Rong, Huang Jing, Wan Da, Wen Dongyue, Liang Yiqiong, Huang Jiang, Li Xin, Wang Xinrong, Chen Gang, He Yun, Yang Hong
Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Front Oncol. 2021 Jul 6;11:613668. doi: 10.3389/fonc.2021.613668. eCollection 2021.
The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).
A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer-Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan-Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis.
A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation.
The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.
本研究旨在全面调查一种整合增强计算机断层扫描（CECT）影像组学特征与临床病理参数的影像组学列线图在肾透明细胞癌（KIRC）中的预后价值。
本回顾性研究纳入了来自训练队列和验证队列的136例和78例KIRC患者。组内相关系数（ICC）用于评估影像组学特征提取的可重复性。在训练队列中，采用单因素Cox分析、最小绝对收缩和选择算子（LASSO）以及多因素Cox分析来构建影像组学特征和临床特征。通过多因素Cox分析建立了一个包含影像组学特征和临床病理参数的预后列线图。在训练队列中评估列线图的预测能力[相对操作特征曲线（ROC）、一致性指数（C指数）、Hosmer-Lemeshow检验和校准曲线]，并在验证队列中进行验证。将患者分为高风险组和低风险组，采用Kaplan-Meier（KM）方法来确定所建立模型的预测能力。此外，通过加权基因共表达网络分析（WGCNA）确定与影像组学风险评分相关的基因，并用于进行功能分析。
从CECT图像的感兴趣肿瘤体积（VOI）中总共获取了2944个影像组学特征。影像组学特征（包括10个选定特征）和临床特征（包括3个选定的临床变量）在训练队列和验证队列中均表现良好[曲线下面积（AUC），影像组学特征分别为0.897和0.712；临床特征分别为0.827和0.822]。影像组学预后列线图在训练队列中表现出良好的性能和校准（AUC，0.896，C指数，0.846），并在验证队列中得到验证（AUC，0.768）。KM曲线表明，高风险组的无进展生存期（PFI）时间明显短于低风险组。功能分析表明，影像组学特征与T细胞活化显著相关。
结合CECT影像组学和临床病理特征的列线图在预测KIRC患者的PFI方面表现出优异的能力，这可能有助于癌症患者的临床管理和预后评估。
