He Xiaopeng, Zhang Hanmei, Zhang Tong, Han Fugang, Song Bin
Department of Radiology, West China Hospital of Sichuan University, Chengdu.
Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China.
Medicine (Baltimore). 2019 Jan;98(2):e13957. doi: 10.1097/MD.0000000000013957.
To evaluate the values of conventional image features (CIFs) and radiomic features (RFs) extracted from multi-detector computed tomography (MDCT) images for predicting low- and high-grade clear cell renal cell carcinoma (ccRCC).Two hundred twenty-seven patients with ccRCC were retrospectively recruited. Five hundred seventy features including 14 CIFs and 556 RFs were extracted from MDCT images of each ccRCC. The CIFs were extracted manually and RFs by the free software-MaZda. Least absolute shrinkage and selection operator (Lasso) was applied to shrink the high-dimensional data set and select the features. Five predictive models for predicting low- and high-grade ccRCC were constructed by the selected CIFs and RFs. The 5 models were as follows: model of minimum mean squared error (minMSE) of CIFs (CIF-minMSE), minMSE of cortico-medullary phase (CMP) of kidney (CMP-minMSE), minMSE of parenchyma phase (PP) of kidney (PP-minMSE), the combined model of CIF-minMSE and CMP-minMSE (CIF-CMP-minMSE), and the combined model of CIF-minMSE and PP-minMSE (CIF-PP-minMSE). The Lasso regression equation of each model was constructed, and the predictive values were calculated. The receiver operating characteristic (ROC) curves of predictive values of the 5 models were drawn by SPSS19.0, and the areas under the curves (AUCs) were calculated.According to Lasso regression, 12, 19 and 10 features were respectively selected from the CIFs, RFs of CMP image and that of PP images to construct the 5 predictive models. The models ordered by their AUCs from large to small were CIF-CMP-minMSE (AUC: 0.986), CIF-PP-minMSE (AUC: 0.981), CIF-minMSE (AUC: 0.980), CMP-minMSE (AUC: 0.975), and PP-minMSE (AUC: 0.963). The maximum diameter of the largest axial section of ccRCC had a maximum weight in predicting the grade of ccRCC among all the features, and its cutoff value was 6.15 cm with a sensitivity of 0.901, a specificity of 0.963, and an AUC of 0.975.When combined with CIFs, RFs extracted from MDCT images contributed to the larger AUC of the predictive model, but were less valuable than CIFs when used alone. The CIF-CMP-minMSE was the optimal predictive model. The maximum diameter of the largest axial section of ccRCC had the largest weight in all features.
评估从多排螺旋计算机断层扫描（MDCT）图像中提取的传统图像特征（CIFs）和影像组学特征（RFs）对预测低级别和高级别透明细胞肾细胞癌（ccRCC）的价值。回顾性纳入227例ccRCC患者。从每个ccRCC的MDCT图像中提取了包括14个CIFs和556个RFs在内的570个特征。CIFs通过手动提取，RFs通过免费软件MaZda提取。应用最小绝对收缩和选择算子（Lasso）来收缩高维数据集并选择特征。通过选定的CIFs和RFs构建了5个预测低级别和高级别ccRCC的预测模型。这5个模型如下：CIFs的最小均方误差（minMSE）模型（CIF-minMSE）、肾皮质髓质期（CMP）的minMSE模型（CMP-minMSE）、肾实质期（PP）的minMSE模型（PP-minMSE）、CIF-minMSE和CMP-minMSE的联合模型（CIF-CMP-minMSE）以及CIF-minMSE和PP-minMSE的联合模型（CIF-PP-minMSE）。构建了每个模型的Lasso回归方程，并计算预测值。用SPSS19.0绘制5个模型预测值的受试者工作特征（ROC）曲线，并计算曲线下面积（AUCs）。根据Lasso回归，分别从CIFs、CMP图像的RFs和PP图像的RFs中选择12、19和10个特征来构建5个预测模型。按AUCs从大到小排序的模型依次为CIF-CMP-minMSE（AUC：0.986）、CIF-PP-minMSE（AUC：0.981）、CIF-minMSE（AUC：0.980）、CMP-minMSE（AUC：0.975）和PP-minMSE（AUC：0.963）。在所有特征中，ccRCC最大轴位截面的最大直径在预测ccRCC分级方面权重最大，其截断值为6.15 cm，敏感性为0.901，特异性为0.963，AUC为0.975。与CIFs联合时，从MDCT图像中提取的RFs有助于预测模型获得更大的AUC，但单独使用时比CIFs价值小。CIF-CMP-minMSE是最佳预测模型。ccRCC最大轴位截面的最大直径在所有特征中权重最大。
