Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User’s Guide

Anup Shetty, Tyler J. Fraum, David Ballard, Mark J. Hoegger, Malak Itani, Mohamed Rajput, Michael H. Lanier, Brian M. Cusworth, Amanda L. Mehrsheikh, Jorge A. Cabrera-Lebron, Jia Chu, Christopher R. Cunningham, Ryan S. Hirschi, Mahati Mokkarala, Jackson G. Unteriner, Eric Kim, Cary Siegel, Daniel Ludwig

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician.

Original languageEnglish
Article numbere220209
JournalRadiographics
Volume43
Issue number7
DOIs
StatePublished - Jul 2023

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