南方医科大学学报

南方医科大学学报 ›› 2021, Vol. 41 ›› Issue (9): 1358-1365.doi: 10.12122/j.issn.1673-4254.2021.09.10

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基于术前CT影像组学列线图可预测Ⅰ~Ⅲ期肾透明性细胞癌术后复发

张海捷，殷 夫，陈梦林，漆安琪，杨丽洋，崔维维，杨姗姗，文 戈   

  1. 深圳大学第一附属医院PET/CT中心，信息工程学院，广东 深圳 518052；南方医科大学南方医院影像系，广东 广州 510515
  • 出版日期:2021-09-20 发布日期:2021-09-30

Predicting postoperative recurrence of stage I-III renal clear cell carcinoma based on preoperative CT radiomics feature nomogram

ZHANG Haijie, YIN Fu, CHEN Menglin, QI Anqi, YANG Liyang, CUI Weiwei, YANG Shanshan, WEN Ge   

  1. PET/CT Center, First Affiliated Hospital of Shenzhen University, Shenzhen 518052, China; Shenzhen University School of Information Engineering, Shenzhen 518052, China; Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
  • Online:2021-09-20 Published:2021-09-30

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摘要： 目的 探讨Ⅰ~Ⅲ期肾透明细胞癌术后复发的术前CT影像组学特征并构建列线图，以期为肾癌个体化治疗提供参考。方法 回顾性收集256例（训练集175例，测试集81例）肾透明细胞癌患者的临床病理及 CT 资料。利用 ITK-SNAP 软件和PyRadiomics计算平台对肿瘤的容积图像进行分割和特征提取。训练集中，基于lasso-CV算法进行特征筛选，并计算影像组学评分Rad_score；利用单因素和多因素逻辑回归分析筛选临床病理及CT特征为Clinic因素；构建Rad_score、Clinic、Rad_score+Clinic列线图，并在测试集中进行验证。评估列线图的辨别度和校准度，应用决策曲线分析评估其临床应用价值。结果 6个影像组学特征最终用于计算Rad_score。Clinic因素为KPS评分、血小板、钙化和TNM临床分期。在辨别度方面，Rad_score+Clinic列线图的效能（训练集AUC 0.84，测试集AUC 0.85）显著高于Rad_score列线图（训练集AUC 0.78，P=0.029；测试集AUC0.77，P=0.025）和 Clinic列线图（训练集AUC 0.77，P=0.014，测试集AUC 0.77，P=0.011）。校准度方面，Rad_score+Clinic列线图拟合优度检验为训练集P=0.065，测试集P=0.628。决策曲线分析显示，加入Rad_score后的Rad_score+Clinic列线图比单纯Clinic列线图应用价值高。结论 基于术前CT影像组学特征的列线图预测Ⅰ~Ⅲ期肾透明细胞癌术后复发有较高的效能，可为肾癌个体化治疗提供参考。

关键词: 肾透明细胞癌；术后复发；列线图；影像组学

Abstract: Objective To explore the preoperative radiomics features (RFs) and construct a nomogram for predicting postoperative recurrence of stage I-III clear cell renal carcinoma (ccRCC). Methods The clinicopathological data and preoperative enhanced CT images collected from 256 patients with ccRCC were used as the training dataset (175 patients) and test dataset (81 patients). The enhanced CT images of the tumor were segmented using ITK-SNAP software, and the RFs were extracted using the PyRadiomics computing platform. In the training dataset, the RFs were screened based on Lasso-CV algorithm, and the Rad_score was calculated. The Clinic factors were screened by univariate and multivariate logistic regression analysis of the clinical and pathological factors and CT characteristics. The Rad_score, Clinic、Rad_score + Clinic nomograms were constructed and verified using the test dataset. The performance, discrimination power and calibration of the nomograms were compared, and their clinical value was evaluated using decision curve analysis. Results Six RFs were retained to calculate the Rad_score. The Clinic factors included Rad_score, KPS score, platelet, calcification and TNM clinical stage. In terms of discrimination, the Rad_score + Clinic nomogram showed better performance (AUC=0.84 for training set; AUC=0.85 for test set) than the Rad_score nomogram (AUC=0.78 for training set, P=0.029; AUC=0.77 for Test set, P=0.025) and Clinic nomogram (AUC=0.77 for training set, P=0.014; AUC=0.77 for test set, P=0.011). In terms of calibration, the P value for goodness of fit test of the Rad_score+Clinic nomogram was 0.065 for the training set and 0.628 for the test set. Decision curve analysis showed a greater clinical value of the Rad_score+Clinic nomogram with Rad_score than the Clinic nomogram without Rad_score. Conclusion The nomogram based on preoperative CT RFs has a high value for predicting postoperative recurrence of stage I-III ccRCC to facilitate individualized treatment of RCC.

Key words: clear cell renal carcinoma; postoperative recurrence; nomogram; radiomics features