Uchida Yusuke, Yoshida Soichiro, Arita Yuki, Shimoda Hiroki, Kimura Koichiro, Yamada Ichiro, Tanaka Hajime, Yokoyama Minato, Matsuoka Yoh, Jinzaki Masahiro, Fujii Yasuhisa
Department of Urology, Tokyo Medical and Dental University Graduate School, 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan.
Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo 160-8582, Japan.
Diagnostics (Basel). 2022 Mar 26;12(4):817. doi: 10.3390/diagnostics12040817.
Preoperative imaging differentiation between ChRCC and RO is difficult with conventional subjective evaluation, and the development of quantitative analysis is a clinical challenge. Forty-nine patients underwent partial or radical nephrectomy preceded by MRI and followed by pathological diagnosis with ChRCC or RO (ChRCC: n = 41, RO: n = 8). The whole-lesion volume of interest was set on apparent diffusion coefficient (ADC) maps of 1.5T-MRI. The importance of selected texture features (TFs) was evaluated, and diagnostic models were created using random forest (RF) analysis. The Mean Decrease Gini as calculated through RF analysis was the highest for mean_ADC_value. ChRCC had a significantly lower mean_ADC_value than RO (1.26 vs. 1.79 × 10−3 mm2/s, p < 0.0001). Feature selection by the Boruta method identified the first-quartile ADC value and GLZLM_HGZE as important features. ROC curve analysis showed that there was no significant difference in the classification performances between the mean_ADC_value-only model and the Boruta model (AUC: 0.954 vs. 0.969, p = 0.236). The mean ADC value had good predictive ability for the distinction between ChRCC and RO, comparable to that of the combination of TFs optimized for the evaluated cohort. The mean ADC value may be useful in distinguishing between ChRCC and RO.
在传统主观评估中，术前影像很难区分透明细胞肾细胞癌（ChRCC）和嫌色细胞肾细胞癌（RO），开展定量分析是一项临床挑战。49例患者在接受MRI检查后进行了部分或根治性肾切除术，术后经病理诊断为ChRCC或RO（ChRCC：n = 41，RO：n = 8）。在1.5T-MRI的表观扩散系数（ADC）图上设定全病灶感兴趣区。评估所选纹理特征（TFs）的重要性，并使用随机森林（RF）分析创建诊断模型。通过RF分析计算出的平均基尼系数降低值，mean_ADC_value最高。ChRCC的mean_ADC_value显著低于RO（1.26对1.79×10−3 mm2/s，p < 0.0001）。通过Boruta方法进行特征选择，确定四分位数ADC值和灰度共生矩阵高灰度区游程长度矩阵（GLZLM_HGZE）为重要特征。ROC曲线分析表明，仅mean_ADC_value模型和Boruta模型的分类性能无显著差异（AUC：0.954对0.969，p = 0.236）。平均ADC值对区分ChRCC和RO具有良好的预测能力，与为评估队列优化的TFs组合相当。平均ADC值可能有助于区分ChRCC和RO。
