CT 特征和人口统计学数据在 2 型乳头状肾细胞癌与乏脂性血管平滑肌脂肪瘤和嗜酸细胞瘤鉴别诊断中的价值。
The value of CT features and demographic data in the differential diagnosis of type 2 papillary renal cell carcinoma from fat-poor angiomyolipoma and oncocytoma.
机构信息
Medical Imaging Center, The First Affiliated Hospital, Jinan University, Guangzhou, China.
Department of Radiology, The University of Hong Kong-Shenzhen Hospital, No.1 Haiyuan Road Futian District, Shenzhen, 518000, Guangdong, China.
出版信息
Abdom Radiol (NY). 2022 Nov;47(11):3838-3846. doi: 10.1007/s00261-022-03644-9. Epub 2022 Sep 9.
PURPOSES
To determine the CT features and demographic data predictive of type 2 papillary renal cell carcinoma (PRCC) that can help distinguish this neoplasm from fat-poor angiomyolipoma (fpAML) and oncocytoma.
METHODS
Fifty-four patients with type 2 PRCC, 48 with fpAML, and 47 with oncocytoma in the kidney from multiple centers were retrospectively reviewed. The demographic data and CT features of type 2 PRCC were analyzed and compared with those of fpAML and oncocytoma by univariate analysis and multiple logistic regression analysis to determine the predictive factors for differential diagnosis. Then, receiver operating characteristic (ROC) curve analysis was performed to further assess the logistic regression model and set the threshold level values of the numerical parameters.
RESULTS
Older age (≥ 46.5 years), unenhanced lesion-to-renal cortex attenuation (RLRCA) < 1.21, corticomedullary ratio of lesion to renal cortex net enhancement (RLRCNE) < 0.32, and size ≥ 30.1 mm were independent predictors for distinguishing type 2 PRCC from fpAML (OR 14.155, 8.332, and 57.745, respectively, P < 0.05 for all). The area under the curve (AUC) of the multiple logistic regression model in the ROC curve analysis was 0.970. In the combined evaluation, the four independent predictors had a sensitivity and specificity of 0.896 and 0.889, respectively. A corticomedullary RLRCNE < 0.61, irregular shape, and male sex were independent predictors for the differential diagnosis of type 2 PRCC from oncocytoma (OR 15.714, 12.158, and 6.175, respectively, P < 0.05 for all). In the combined evaluation, the three independent predictors had a sensitivity and specificity of 0.889 and 0.979, respectively. The AUC of the multiple logistic regression model in the ROC curve analysis was 0.964.
CONCLUSION
The combined application of CT features and demographic data had good ability in distinguishing type 2 PRCC from fpAML and oncocytoma, respectively.
目的
确定有助于将 2 型乳头状肾细胞癌（PRCC）与乏脂性血管平滑肌脂肪瘤（fpAML）和嗜酸细胞瘤相区分的 CT 特征和人口统计学数据。
方法
回顾性分析了来自多个中心的 54 例 2 型 PRCC、48 例 fpAML 和 47 例嗜酸细胞瘤患者的人口统计学数据和 CT 特征。通过单因素分析和多因素逻辑回归分析比较了 2 型 PRCC 与 fpAML 和嗜酸细胞瘤的这些数据，以确定用于鉴别诊断的预测因素。然后，进行接收者操作特征（ROC）曲线分析，以进一步评估逻辑回归模型，并确定数值参数的阈值水平值。
结果
年龄较大（≥46.5 岁）、增强前病灶与肾皮质衰减比（RLRCA）<1.21、皮质-髓质病变与肾皮质净增强比（RLRCNE）<0.32、病灶大小≥30.1mm 是将 2 型 PRCC 与 fpAML 区分开来的独立预测因素（OR 分别为 14.155、8.332 和 57.745，P<0.05）。ROC 曲线分析中多因素逻辑回归模型的曲线下面积（AUC）为 0.970。在联合评估中，四个独立预测因素的敏感性和特异性分别为 0.896 和 0.889。皮质-髓质 RLRCNE<0.61、不规则形状和男性是将 2 型 PRCC 与嗜酸细胞瘤区分开来的独立预测因素（OR 分别为 15.714、12.158 和 6.175，P<0.05）。在联合评估中，三个独立预测因素的敏感性和特异性分别为 0.889 和 0.979。ROC 曲线分析中多因素逻辑回归模型的 AUC 为 0.964。
结论
CT 特征和人口统计学数据的联合应用在分别将 2 型 PRCC 与 fpAML 和嗜酸细胞瘤区分方面具有良好的能力。
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