使用增强 CT 特定感兴趣区鉴别无可见脂肪的肾血管平滑肌脂肪瘤与小透明细胞肾细胞癌：一种新的定量工具组合。

Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools.

机构信息

Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.

Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.

出版信息

Cancer Imaging. 2021 Jul 5;21(1):47. doi: 10.1186/s40644-021-00417-3.

BACKGROUND

To investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC).

METHODS

Four-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated.

RESULTS

Among the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC = 0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986).

CONCLUSION

The combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.

背景

探讨使用对比增强 CT 特定感兴趣区（ROI）区分无可见脂肪的肾血管平滑肌脂肪瘤（AML.wovf）和小透明细胞肾细胞癌（ccRCC）的价值。

方法

回顾性分析经组织病理学证实的 31 例 AML.wovf 和 74 例 ccRCC 的四期（平扫期 [PCP]、皮质期 [CMP]、肾实质期 [NP]和排泄期 [EP]）对比增强 CT 图像。使用不同 ROI（小：ROI（1），更小：ROI（2），大：ROI（3））获取肿瘤 CT 衰减值（AVT）、净增强值（NEV）、相对增强比（RER）、肿瘤异质性程度（HDT）和标准化异质性比（SHR），并对 AML.wovf 和 ccRCC 之间的这些定量数据差异进行统计学分析。使用多元回归筛选各扫描期的主要鉴别因素，建立并评估预测模型。

结果

在不同 ROI 确定的定量参数中，ROI（2）测量的增强程度和 ROI（3）测量的增强异质性优于 ROI（1），更有助于区分 AML.wovf 和 ccRCC。受试者工作特征（ROC）曲线显示，ROI（2）测量的 CMP 时 RER（2）、NP 时 RER（2）和 ROI（3）测量的 CMP 时 HDT、NP 时 SHR 的曲线下面积（AUC）较高（AUC=0.876、0.849、0.837 和 0.800）。结合增强期获得的人口统计学数据、形态特征和定量数据的预测模型优于平扫期获得的定量数据，用于区分 AML.wovf 和 ccRCC。其中，CMP 模型为最佳预测模型，AUC 最高（AUC=0.986）。