医学
肾透明细胞癌
肾细胞癌
放射科
金标准(测试)
活检
磁共振弥散成像
医学物理学
磁共振成像
病理
作者
Anup S. Shetty,Tyler J. Fraum,David H. Ballard,Mark J. Hoegger,Malak Itani,Mohamed Z. Rajput,M Lanier,Brian Cusworth,Amanda L. Mehrsheikh,Jorge A. Cabrera-Lebron,J W Chu,Christopher Cunningham,Ryan Hirschi,Mahati Mokkarala,Jackson G. Unteriner,Eric Kim,Cary Siegel,Daniel Ludwig
出处
期刊:Radiographics
[Radiological Society of North America]
日期:2023-07-01
卷期号:43 (7)
被引量:1
摘要
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. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.
科研通智能强力驱动
Strongly Powered by AbleSci AI