Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China.
Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China.
Abdom Radiol (NY). 2021 Sep;46(9):4289-4300. doi: 10.1007/s00261-021-03090-z. Epub 2021 Apr 28.
The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT).
Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model.
Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05).
This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.
旨在探讨基于平扫 CT（UECT）纹理分析预测局限性透明细胞肾细胞癌（ccRCC）世界卫生组织（WHO）/国际泌尿病理学会（ISUP）分级的价值。
本研究回顾性收集了经病理证实的局限性 ccRCC 患者 104 例，均行 UECT 扫描。所有病例均根据 WHO/ISUP 分级分为低级别（n=53）和高级别（n=51），并按 7:3 的比例随机分为训练集和测试集。使用 UECT 图像的 3D-ROI 分割提取 93 个纹理特征（一阶、灰度共生矩阵[GLCM]、灰度游程长度矩阵[GLRLM]、灰度大小区域矩阵[GLSZM]、邻域灰度差矩阵[NGTDM]和灰度依赖矩阵[GLDM]特征）。采用单因素分析和最小绝对收缩选择算子（LASSO）回归进行特征降维，采用逻辑回归分类器建立预测模型。采用受试者工作特征（ROC）曲线、柱状图和校准曲线评估预测模型的性能。
降维筛选出 8 个最佳纹理特征（最大值、中位数、依赖方差[DV]、长游程强调[LRE]、游程熵[RE]、灰度不均匀性[GLN]、灰度方差[GLV]和大面积低灰度强调[LALGLE]），然后根据这些特征的线性组合建立预测模型。该模型在训练集的准确性、敏感度、特异度和 AUC 分别为 86.1%、91.4%、81.1%和 0.937，在测试集的准确性、敏感度、特异度和 AUC 分别为 81.2%、81.2%、81.2%和 0.844。校准曲线显示训练集和测试集的校准均良好（P>0.05）。
本研究表明，基于 UECT 纹理分析的放射组学模型能够准确评估局限性 ccRCC 的 WHO/ISUP 分级。
