Faculty, Staff and Student Publications
Language
English
Publication Date
3-1-2025
Journal
Radiology
DOI
10.1148/radiol.240308
PMID
40100027
PMCID
PMC11950888
PubMedCentral® Posted Date
3-18-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Despite the successful application of Imaging Reporting and Data Systems to improve the radiologic description and management of disease in many organs, one does not yet exist for the kidney. Instead, the radiologic approach to the kidney has focused on the Bosniak classification system, which is based on imaging characteristics for cystic renal masses, and detecting macroscopic fat within solid renal masses. Radiologically, cystic and solid renal masses are categorized and evaluated separately because of historical precedent, differences in appearance at imaging, and differences in biologic behavior. However, the World Health Organization classification of renal neoplasms does not support such separation. Further, the primary goal has been cancer diagnosis. Differentiating benign from malignant masses is important, but data show that many renal cancers, particularly when small, will not cause harm. Therefore, a critical goal of any unifying, single, imaging-based classification of kidney masses (ie, a Kidney Imaging Reporting and Data System) should be predicting the biologic behavior or aggressiveness of suspected kidney cancer. This system could inform the need for treatment or active surveillance and reduce prevalent overdiagnosis and overtreatment. This review describes the rationale for and challenges in creating such a system and the research needed for it to be developed.
Keywords
Humans, Kidney Neoplasms, Evidence-Based Medicine, Radiology Information Systems, Diagnosis, Differential
Published Open-Access
yes
Recommended Citation
Silverman, Stuart G; Pedrosa, Ivan; Schieda, Nicola; et al., "In Pursuit of KI-RADS: Toward a Single, Evidence-based Imaging Classification of Renal Masses" (2025). Faculty, Staff and Student Publications. 6405.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6405
Graphical Abstract
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