Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
Radiol Med. 2020 Aug;125(8):754-762. doi: 10.1007/s11547-020-01169-z. Epub 2020 Mar 19.
To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade.
Seventy-one ccRCC patients (31 low grade and 40 high grade) were included in this study. Tumors were manually segmented on CT images followed by the application of three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) on delineated tumor volumes. Overall, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association between each feature and the histological condition. Multivariate analysis involved the use of machine learning (ML) algorithms and the following three feature selection algorithms: the least absolute shrinkage and selection operator, Student's t test, and minimum Redundancy Maximum Relevance. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under the receiver operating characteristic curve (AUC) metric.
The univariate analysis demonstrated that among the different image sets, 128 bin-discretized images have statistically significant different texture parameters with a mean AUC of 0.74 ± 3 (q value < 0.05). The three ML-based classifiers showed proficient discrimination between high and low-grade ccRCC. The AUC was 0.78 for logistic regression, 0.62 for random forest, and 0.83 for the SVM model, respectively.
CT radiomic features can be considered as a useful and promising noninvasive methodology for preoperative evaluation of ccRCC Fuhrman grades.
确定基于 CT 影像组学的术前预测透明细胞肾细胞癌（ccRCC）分级的最佳分类方法。
本研究纳入了 71 例 ccRCC 患者（低级别 31 例，高级别 40 例）。在 CT 图像上手动对肿瘤进行分割，然后对勾画的肿瘤体积应用三种图像预处理技术（拉普拉斯高斯、小波滤波器和强度值离散化）。总体上，从每个分割的肿瘤体积中提取了 2530 个放射组学特征（肿瘤形状和大小、强度统计和纹理）。单变量分析用于评估每个特征与组织学条件之间的关联。多变量分析涉及使用机器学习（ML）算法和以下三种特征选择算法：最小绝对收缩和选择算子、学生 t 检验和最小冗余最大相关性。然后，使用这些选择的特征构建三个分类模型（SVM、随机森林和逻辑回归），以在肾切除术中区分高低级别 ccRCC。最后，使用受试者工作特征曲线（AUC）下面积（AUROC）度量在 bootstrap 验证队列上评估多变量模型性能。
单变量分析表明，在不同的图像集中，128 -bin 离散化图像具有统计学上显著不同的纹理参数，平均 AUC 为 0.74±3（q 值 < 0.05）。基于三种 ML 的分类器在高低级别 ccRCC 之间显示出出色的区分能力。逻辑回归的 AUC 为 0.78，随机森林为 0.62，SVM 模型为 0.83。
CT 放射组学特征可被视为术前评估 ccRCC Fuhrman 分级的一种有用且有前途的非侵入性方法。
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