1 Department of Radiology, University of Southern California, 1520 San Pablo St, HC2 L1600, Los Angeles, CA 90033.
2 Institute of Urology, University of Southern California, Los Angeles, CA.
AJR Am J Roentgenol. 2018 Dec;211(6):W288-W296. doi: 10.2214/AJR.18.19551. Epub 2018 Sep 21.
The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses.
In a retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients. Whole-lesion ROIs were manually segmented and coregistered from the standard-of-care multiphase contrast-enhanced CT (CECT) scans of these patients. Pathologic diagnosis of all tumors was obtained after surgical resection. CECT images of the renal masses were used as inputs to a CECT texture analysis panel comprising 31 texture metrics derived with six texture methods. Stepwise logistic regression analysis was used to select the best predictor among all candidate predictors from each of the texture methods, and their performance was quantified by AUC.
Among the texture predictors aiding renal mass subtyping were entropy, entropy of fast-Fourier transform magnitude, mean, uniformity, information measure of correlation 2, and sum of averages. These metrics had AUC values ranging from good (0.80) to excellent (0.98) across the various subtype comparisons. The overall CECT-based tumor texture model had an AUC of 0.87 (p < 0.05) for differentiating benign from malignant renal masses.
The CT texture statistical model studied was accurate for differentiating benign from malignant solid enhancing lipid-poor renal masses.
本研究旨在评估从临床 CT 提取的纹理特征对鉴别良恶性实性强化乏脂性肾脏肿块的准确性。
在对 174 例主要为实性、非肉眼含脂、强化的肾脏肿块的回顾性病例对照研究中，发现 129 例恶性肾细胞癌病例，包括透明细胞、乳头状和嫌色细胞亚型。良性肾脏肿块-嗜酸细胞瘤和乏脂性血管平滑肌脂肪瘤-在 45 例患者中发现。对这些患者的标准多期对比增强 CT（CECT）扫描进行手动分割和配准，获得全病灶 ROI。对所有肿瘤进行手术切除后的病理诊断。将肾脏肿块的 CECT 图像作为输入，使用包含 6 种纹理方法的 31 种纹理特征的 CECT 纹理分析面板。使用逐步逻辑回归分析从每个纹理方法的所有候选预测因子中选择最佳预测因子，并通过 AUC 对其性能进行量化。
在辅助肾肿瘤亚型分类的纹理预测因子中包括熵、快速傅里叶变换幅度的熵、均值、均匀性、相关 2 信息测度和平均值之和。这些指标在各种亚型比较中的 AUC 值范围从良好（0.80）到优秀（0.98）。基于整个 CECT 的肿瘤纹理模型在区分良性和恶性肾脏肿块方面的 AUC 值为 0.87（p<0.05）。
研究的 CT 纹理统计模型在区分良恶性实性强化乏脂性肾脏肿块方面具有准确性。
