Prediction of Growth Rate of Solid Renal Masses: Utility of MR Imaging Features-Preliminary Experience

Purpose: To retrospectively assess the relationships between a number of magnetic resonance (MR) imaging features and growth rate of solid renal masses in patients undergoing active surveillance. Materials and Methods: This retrospective study was institutional review board approved, with waiver of informed consent. This study was HIPAA compliant. Forty-four patients (mean age, 70 years +/- 13 [standard deviation]; 31 men, 13 women) with 47 solid renal masses measuring at least 1 cm who underwent two contrast material-enhanced MR imaging studies at least 3 months apart were included. The initial MR imaging study was evaluated independently by two radiologists for an array of imaging features, with differences resolved by consensus. Later, the two readers in consensus measured tumor volume on the first and last study to calculate tumor doubling time (DT). Associations between MR imaging features and DT were assessed by using generalized estimating equations and mixed model analyses. Interreader agreement was assessed with k coefficients. Results: k coefficients ranged from 0.62 to 0.92. Mean DT of the 47 masses was 530 days. Five (11%) masses decreased in size. Twenty-five of 47 masses were classified as slow growing on the basis of a DT of more than 2 years. There was significantly slower growth among masses showing homogeneity on T2-weighted images (P = .036) and a nearly significant slower growth rate among masses showing homogeneity on postcontrast images (P = .065) and hypointensity on T2-weighted images (P = .074). There was a significant correlation between initial volume and growth rate among lesions larger than 3 cm (r = 0.79, P = .041) but not among smaller lesions (r = -0.02, P = .911). Multivariate analysis identified age (odds ratio = 0.92, P = .015) and homogeneity on T2-weighted images (odds ratio = 4.47, P = .037) as independent predictors of slow growth. Conclusion: The results suggest MR imaging features may have a role in predicting growth rate of solid renal masses during active surveillance.