Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China.
Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, Guangdong, China.
Eur Radiol. 2020 Feb;30(2):1254-1263. doi: 10.1007/s00330-019-06384-5. Epub 2019 Aug 29.
To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC).
This study retrospectively collected 163 patients with pathologically proven small renal mass, including 118 RCC and 45 AMLwvf patients. Target region of interest (ROI) delineation, followed by texture feature extraction, was performed on a representative slice with the largest lesion area on each phase of the four-phase CT images. Fifteen concatenations of the four-phasic features were fed into 224 classification models (built with 8 classifiers and 28 feature selection methods), classification performances of the 3360 resultant discriminative models were compared, and the top-ranked features were analyzed.
Image features extracted from the unenhanced phase (UP) CT image demonstrated dominant classification performances over features from other three phases. The two discriminative models "SVM + t_score" and "SVM + relief" achieved the highest classification AUC of 0.90. The 10 top-ranked features from UP included 1 shape feature, 5 first-order statistics features, and 4 texture features, where the shape feature and the first-order statistics features showed superior discriminative capabilities in differentiating RCC vs. AMLwvf through the t-SNE visualization.
Image features extracted from UP are sufficient to generate accurate differentiation between AMLwvf and RCC using machine learning-based classification model.
• Radiomics extracted from unenhanced CT are sufficient to accurately differentiate angiomyolipoma without visible fat and renal cell carcinoma using machine learning-based classification model. • The highest discriminative models achieved an AUC of 0.90 and were based on the analysis of unenhanced CT, alone or in association with images obtained at the nephrographic phase. • Features related to shape and to histogram analysis (first-order statistics) showed superior discrimination compared with gray-level distribution of the image (second-order statistics, commonly called texture features).
探究基于机器学习的不同分类模型在鉴别小（<4cm）无可见脂肪肾血管平滑肌脂肪瘤（AMLwvf）和肾细胞癌（RCC）方面的区分能力。
本研究回顾性收集了 163 例经病理证实的小肾肿块患者的资料，包括 118 例 RCC 和 45 例 AMLwvf 患者。对每个患者的最大病变面积的代表性切片进行目标区域兴趣（ROI）勾画，然后提取纹理特征。将四个时相 CT 图像的特征进行 15 次串联，输入到 224 个分类模型（由 8 个分类器和 28 个特征选择方法构建）中，比较了 3360 个判别模型的分类性能，并分析了排名靠前的特征。
与其他三个时相的特征相比，平扫 CT 图像（UP）提取的图像特征表现出更好的分类性能。两个有判别力的模型“SVM+t_score”和“SVM+relief”的分类 AUC 最高，达到 0.90。来自 UP 的 10 个排名靠前的特征包括 1 个形状特征、5 个一阶统计特征和 4 个纹理特征，其中形状特征和一阶统计特征通过 t-SNE 可视化显示出在区分 RCC 与 AMLwvf 方面具有优越的判别能力。
基于机器学习的分类模型使用 UP 提取的图像特征足以准确区分 AMLwvf 和 RCC。
基于机器学习的分类模型使用平扫 CT 提取的影像组学足以准确区分无可见脂肪肾血管平滑肌脂肪瘤和肾细胞癌。
最高判别模型的 AUC 达到 0.90，基于平扫 CT 分析，单独或与肾实质期图像联合应用。
与图像灰度分布（二阶统计，通常称为纹理特征）相比，形状和直方图分析（一阶统计）相关的特征显示出更好的判别能力。
