Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis.

Abstract

The objective of this study was to determine whether quantitative texture analysis of MR images would improve the ability to distinguish papillary renal cell carcinoma (RCC) subtypes, compared with analysis of qualitative MRI features alone. A total of 47 pathologically proven papillary RCC tumors were retrospectively evaluated, with 31 (66%) classified as type 1 tumors and 16 (34%) classified as type 2 tumors. MR images were reviewed by two readers to determine tumor size, signal intensity, heterogeneity, enhancement pattern, margins, perilesional stranding, vein thrombosis, and metastasis. Quantitative texture analysis of gray-scale images was performed. A logistic regression was derived from qualitative and quantitative features. Model performance was compared with and without texture features. The significant qualitative MR features noted were necrosis, enhancement appearance, perilesional stranding, and metastasis. A multivariable model based on qualitative features did not identify any factor as an independent predictor of a type 2 tumor. The logistic regression model for predicting papillary RCCs on the basis of qualitative and quantitative analysis identified probability of the 2D volumetric interpolated breath-hold examination (VIBE) sequence (AUC value, 0.87; 95% CI, 0.77-0.98) as an independent predictor of a type 2 tumor. No difference in the model AUC value was noted when texture features were included in the analysis; however, the model had increased sensitivity and an improved predictive value without loss of specificity. The addition of texture analysis to analysis of conventional qualitative MRI features increased the probability of predicting a type 2 papillary RCC tumor, which may be clinically important.