Intensity ratio curve analysis of small renal masses on T2-weighted magnetic resonance imaging: Differentiation of fat-poor angiomyolipoma from renal cell carcinoma.

OBJECTIVES: To assess the diagnostic ability of a pixel intensity-based analysis in evaluating the magnetic resonance imaging characteristics of small renal masses, especially in differentiating fat-poor angiomyolipoma from renal cell carcinoma. METHODS: T2-weighted images from 121 solid small renal masses (<4 cm) without visible fat (14 fat-poor angiomyolipomas, 92 clear cell renal cell carcinomas, six chromophobe renal cell carcinomas and nine papillary renal cell carcinomas) were retrospectively evaluated. An intensity ratio curve was plotted using intensity ratios, which were ratios of signal intensities of tumor pixels (each pixel along a linear region of interest drawn across the renal tumor on T2-weighted image) to the signal intensity of a normal renal cortex. The diagnostic ability of the intensity ratio curve analysis was evaluated. RESULTS: The tumors were classified into three types: intensity ratio fat-poor angiomyolipoma (n = 19) with no pseudocapsule, iso-low intensity and no heterogeneity; intensity ratio clear cell renal cell carcinoma (n = 76) with a pseudocapsule, iso-high intensity and heterogeneity; and other type of intensity ratio (n = 26), including tumors that did not fall into the above two categories. The sensitivity/specificity/accuracy of the intensity ratio curve analysis in diagnosing fat-poor angiomyolipoma was 93%/94%/94%, respectively. When the intensity ratio curve analysis was applied only to the tumor with undetermined radiological diagnosis, the sensitivity for diagnosing fat-poor angiomyolipoma compared with subjective reading alone significantly improved (93% vs 50%; P = 0.014). CONCLUSIONS: Our novel semiquantitative model for combined assessment of key features of fat-poor angiomyolipoma, including low intensity, homogeneity and absence of a pseudocapsule on T2-weighted image, might make diagnosis of fat-poor angiomyolipoma more accurate.