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Updated: May 21, 2025

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
Published on: March 30, 2015
In Pursuit of KI-RADS: Toward a Single, Evidence-based Imaging Classification of Renal Masses
Stuart G Silverman1, Ivan Pedrosa1, Nicola Schieda1
1From the Division of Abdominal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (S.G.S.); Department of Radiology (I.P.), Advanced Imaging Research Center (I.P.), Department of Urology (I.P., V.M., P.K.), Kidney Cancer Program, Simmons Comprehensive Cancer Center (I.P., V.M., P.K.), and Department of Pathology (P.K.), University of Texas Southwestern Medical Center, Dallas, Tex; Department of Radiology, University of Ottawa, Ottawa, Canada (N.S.); and Departments of Radiology and Urology, Michigan Medicine, Ann Arbor, Mich (M.S.D.).
None:
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.

