Department of Medical Image, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 West Yanta Road, Xi'an, 710061, Shaanxi, China.
Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China.
Radiol Med. 2022 Aug;127(8):837-847. doi: 10.1007/s11547-022-01526-0. Epub 2022 Jul 14.
To investigate the performance of CT radiomics in predicting the overall survival (OS) of patients with stage III clear cell renal carcinoma (ccRCC) after radical nephrectomy.
The 132 patients with stage III ccRCC undergoing radical nephrectomy were collected, and the patients were divided into training set (n = 79) and validation set (n = 53). The ccRCC was segmented and 396 radiomics features were extracted. After dimensionality reduction, radiomics score (RS) was obtained. COX regression was used to construct Model 1 (clinical variables + CT findings) and Model 2 (clinical variables + CT findings + RS) in the training set to predict the OS of patients, and then, the performance of the two models in the two data sets was compared.
In the training set, Akaike information criterion, C-index, and corrected C-index were 295.51, 0.744, and 0.728 for Model 1, and 271.78, 0.805, and 0.799 for Model 2, respectively. In the validation set, the corresponding values were 185.68, 0.701, and 0.699 for Model 1, and 175.99, 0.768, and 0.768 for Model 2. The calibration curves showed that both models had good calibration degrees in the validation set. Compared with Model 1, the continuous net reclassification index and integrated discrimination improvement index of Model 2 in the two data sets were positively improved.
The two prediction models showed high performance in the evaluation of OS of stage III ccRCC patients after radical nephrectomy, among which Model 2 based on ISUP grade and RS was more concise and efficient.
研究 CT 放射组学在预测根治性肾切除术后 III 期透明细胞肾细胞癌（ccRCC）患者总生存（OS）中的性能。
共收集 132 例接受根治性肾切除术的 III 期 ccRCC 患者，将患者分为训练集（n=79）和验证集（n=53）。对 ccRCC 进行分割并提取 396 个放射组学特征。经过降维处理，得到放射组学评分（RS）。在训练集中，采用 COX 回归构建模型 1（临床变量+CT 表现）和模型 2（临床变量+CT 表现+RS）来预测患者的 OS，并比较两个模型在两个数据集的性能。
在训练集中，模型 1 的 Akaike 信息准则、C 指数和校正 C 指数分别为 295.51、0.744 和 0.728，模型 2 分别为 271.78、0.805 和 0.799。在验证集中，模型 1 的相应值分别为 185.68、0.701 和 0.699，模型 2 分别为 175.99、0.768 和 0.768。校准曲线显示，两个模型在验证集中均具有良好的校准度。与模型 1 相比，模型 2 在两个数据集的连续净重新分类指数和综合判别改善指数均有显著提高。
这两个预测模型在评估根治性肾切除术后 III 期 ccRCC 患者的 OS 方面表现出较高的性能，其中基于 ISUP 分级和 RS 的模型 2 更为简洁高效。
