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Abstract：
OBJECTIVE: We aimed to explore the diagnostic efficacy of MR texture analysis and imaging signs in the differentiation of renal oncocytoma from renal cell carcinoma (RCC).
METHODS: From January 2015 to March 2019, a total of 168 localized solid renal masses (37 oncocytomas, 131 RCCs) were retrospectively included. Two radiologists reviewed complete MR images and recorded imaging presentation. Texture parameters were extracted from 3D ROIs on axial FSE-T2WI. Univariate and multivariate logistic regressions were used for feature selection and nomogram construction. The diagnostic performances were assessed by receiver operating characteristic (ROC) curves.
RESULTS: Cystic change, hemorrhage, SEI and four texture parameters significantly correlated with oncocytoma in the training cohort. For differentiating oncocytoma from RCC, the nomogram yielded an AUC of 0.874 in the training cohort and 0.830 in the testing cohort. For differentiating oncocytoma from chRCC, the nomogram had an AUC of 0.889 in the training cohort and 0.861 in the testing cohort. For differentiating oncocytoma from pRCC, the nomogram had an AUC of 0.932 in the training cohort and 0.792 in the testing cohort. For differentiating oncocytoma from ccRCC, the nomogram had an AUC of 0.829 in the training cohort and 0.813 in the testing cohort.
CONCLUSIONS: The diagnostic nomogram combining MR texture parameters with imaging signs performed well in differentiating oncocytomas with localized RCC and its subtypes.
CONCLUSIONS: Few articles reported using the combination of MR texture analysis with imaging signs in differentiating RCC from oncocytoma. Our study established a useful nomogram in subtype characterization.
METHODS: From January 2015 to March 2019, a total of 168 localized solid renal masses (37 oncocytomas, 131 RCCs) were retrospectively included. Two radiologists reviewed complete MR images and recorded imaging presentation. Texture parameters were extracted from 3D ROIs on axial FSE-T2WI. Univariate and multivariate logistic regressions were used for feature selection and nomogram construction. The diagnostic performances were assessed by receiver operating characteristic (ROC) curves.
RESULTS: Cystic change, hemorrhage, SEI and four texture parameters significantly correlated with oncocytoma in the training cohort. For differentiating oncocytoma from RCC, the nomogram yielded an AUC of 0.874 in the training cohort and 0.830 in the testing cohort. For differentiating oncocytoma from chRCC, the nomogram had an AUC of 0.889 in the training cohort and 0.861 in the testing cohort. For differentiating oncocytoma from pRCC, the nomogram had an AUC of 0.932 in the training cohort and 0.792 in the testing cohort. For differentiating oncocytoma from ccRCC, the nomogram had an AUC of 0.829 in the training cohort and 0.813 in the testing cohort.
CONCLUSIONS: The diagnostic nomogram combining MR texture parameters with imaging signs performed well in differentiating oncocytomas with localized RCC and its subtypes.
CONCLUSIONS: Few articles reported using the combination of MR texture analysis with imaging signs in differentiating RCC from oncocytoma. Our study established a useful nomogram in subtype characterization.
摘要：
目的：我们旨在探讨MR纹理分析和影像学征象对肾嗜酸细胞瘤与肾细胞癌（RCC）的鉴别诊断效能。
方法：从2015年1月至2019年3月，共168例局部实性肾脏肿块（37例嗜酸细胞瘤，131个RCC）被回顾性纳入。两名放射科医生回顾了完整的MR图像并记录了影像学表现。从轴向FSE-T2WI上的3DROI提取纹理参数。单变量和多变量逻辑回归用于特征选择和列线图构建。通过受试者工作特征（ROC）曲线评估诊断性能。
结果：囊性变化，出血,在训练队列中，SEI和四个质地参数与嗜酸细胞瘤显着相关。为了区分嗜酸细胞瘤和RCC，列线图在训练队列中的AUC为0.874,在测试队列中的AUC为0.830.为了区分嗜酸细胞瘤和chRCC，列线图在训练队列中的AUC为0.889,在测试队列中的AUC为0.861.为了区分嗜酸细胞瘤和pRCC，列线图在训练队列中的AUC分别为0.932和0.792.为了区分嗜酸细胞瘤和ccRCC，列线图在训练队列中的AUC为0.829,在测试队列中的AUC为0.813.
结论：结合MR纹理参数和影像学征象的诊断列线图在区分与局部RCC及其亚型的嗜酸细胞瘤方面表现良好。
结论：很少有文章报道使用MR纹理分析与影像学征象相结合来区分RCC和嗜酸细胞瘤。我们的研究在亚型表征中建立了有用的列线图。
方法：从2015年1月至2019年3月，共168例局部实性肾脏肿块（37例嗜酸细胞瘤，131个RCC）被回顾性纳入。两名放射科医生回顾了完整的MR图像并记录了影像学表现。从轴向FSE-T2WI上的3DROI提取纹理参数。单变量和多变量逻辑回归用于特征选择和列线图构建。通过受试者工作特征（ROC）曲线评估诊断性能。
结果：囊性变化，出血,在训练队列中，SEI和四个质地参数与嗜酸细胞瘤显着相关。为了区分嗜酸细胞瘤和RCC，列线图在训练队列中的AUC为0.874,在测试队列中的AUC为0.830.为了区分嗜酸细胞瘤和chRCC，列线图在训练队列中的AUC为0.889,在测试队列中的AUC为0.861.为了区分嗜酸细胞瘤和pRCC，列线图在训练队列中的AUC分别为0.932和0.792.为了区分嗜酸细胞瘤和ccRCC，列线图在训练队列中的AUC为0.829,在测试队列中的AUC为0.813.
结论：结合MR纹理参数和影像学征象的诊断列线图在区分与局部RCC及其亚型的嗜酸细胞瘤方面表现良好。
结论：很少有文章报道使用MR纹理分析与影像学征象相结合来区分RCC和嗜酸细胞瘤。我们的研究在亚型表征中建立了有用的列线图。
