Wang Ruihui, Hu Zhengyu, Shen Xiaoyong, Wang Qidong, Zhang Liang, Wang Minhong, Feng Zhan, Chen Feng
Department of Radiology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Radiology, Second People's Hospital of Yuhang District, Hangzhou, China.
Front Oncol. 2021 Feb 25;11:543854. doi: 10.3389/fonc.2021.543854. eCollection 2021.
To examine the ability of computed tomography radiomic features in multivariate analysis and construct radiomic model for identification of the the WHO/ISUP pathological grade of clear cell renal cell carcinoma (ccRCC).
This was a retrospective study using data of four hospitals from January 2018 to August 2019. There were 197 patients with a definitive diagnosis of ccRCC by post-surgery pathology or biopsy. These subjects were divided into the training set (n = 122) and the independent external validation set (n = 75). Two phases of Enhanced CT images (corticomedullary phase, nephrographic phase) of ccRCC were used for whole tumor Volume of interest (VOI) plots. The IBEX radiomic software package in Matlab was used to extract the radiomic features of whole tumor VOI images. Next, the Mann-Whitney U test and minimum redundancy-maximum relevance algorithm(mRMR) was used for feature dimensionality reduction. Next, logistic regression combined with Akaike information criterion was used to select the best prediction model. The performance of the prediction model was assessed in the independent external validation cohorts. Receiver Operating Characteristic curve (ROC) was used to evaluate the discrimination of ccRCC in the training and independent external validation sets.
The logistic regression prediction model constructed with seven radiomic features showed the best performance in identification for WHO/ISUP pathological grades. The Area Under Curve (AUC) of the training set was 0.89, the sensitivity comes to 0.85 and specificity was 0.84. In the independent external validation set, the AUC of the prediction model was 0.81, the sensitivity comes to 0.58, and specificity was 0.95.
A radiological model constructed from CT radiomic features can effectively predict the WHO/ISUP pathological grade of CCRCC tumors and has a certain clinical generalization ability, which provides an effective value for patient prognosis and treatment.
探讨计算机断层扫描（CT）影像组学特征在多变量分析中的能力，并构建影像组学模型以识别透明细胞肾细胞癌（ccRCC）的世界卫生组织/国际泌尿病理学会（WHO/ISUP）病理分级。
这是一项回顾性研究，使用了2018年1月至2019年8月四家医院的数据。197例患者经手术病理或活检确诊为ccRCC。这些受试者被分为训练集（n = 122）和独立外部验证集（n = 75）。ccRCC的两个增强CT图像期（皮质髓质期、肾实质期）用于绘制全肿瘤感兴趣区（VOI）。使用Matlab中的IBEX影像组学软件包提取全肿瘤VOI图像的影像组学特征。接下来，使用曼-惠特尼U检验和最小冗余-最大相关性算法（mRMR）进行特征降维。然后，结合赤池信息准则的逻辑回归用于选择最佳预测模型。在独立外部验证队列中评估预测模型的性能。采用受试者操作特征曲线（ROC）评估训练集和独立外部验证集中ccRCC的鉴别能力。
由七个影像组学特征构建的逻辑回归预测模型在识别WHO/ISUP病理分级方面表现最佳。训练集的曲线下面积（AUC）为0.89，敏感性为0.85，特异性为0.84。在独立外部验证集中，预测模型的AUC为0.81，敏感性为0.58，特异性为0.95。
由CT影像组学特征构建的放射学模型可以有效预测CCRCC肿瘤的WHO/ISUP病理分级，具有一定的临床泛化能力，为患者的预后和治疗提供了有效价值。
