RSNA 2016

Abstract Archives of the RSNA, 2016


GU202-SD-SUA3

Discrimination of Clear Cell Renal Cell Carcinoma from Oncocytoma and Fat-Poor Angiomyolipoma on MDCT Using Peak Lesion Enhancement Relative to Uninvolved Renal Parenchyma

Sunday, Nov. 27 12:30PM - 1:00PM Room: GU/UR Community, Learning Center Station #3



Heidi Coy, Los Angeles, CA (Presenter) Nothing to Disclose
Jonathan R. Young, MD, Los Angeles, CA (Abstract Co-Author) Nothing to Disclose
Michael L. Douek, MD, MBA, Los Angeles, CA (Abstract Co-Author) Nothing to Disclose
Matthew S. Brown, PhD, Los Angeles, CA (Abstract Co-Author) Nothing to Disclose
James Sayre, PhD, Los Angeles, CA (Abstract Co-Author) Nothing to Disclose
Steven S. Raman, MD, Santa Monica, CA (Abstract Co-Author) Nothing to Disclose
PURPOSE

Although a renal mass can have imaging features of a typical clear cell renal cell carcinoma (ccRCC) on MDCT, up to 30% of these are found to be benign after surgery. Most commonly oncocytoma (Onc) and fat-poor angiomyolipoma (fpAML). Discrimination between ccRCC, and Onc or fpAML on imaging would preclude the need for biopsy, and could alter management between surgery and ablation, or active surveillance and no further evaluation. The purpose of our study is to discriminate ccRCC from Onc and fpAML on MDCT using peak lesion enhancement relative to renal cortex.

METHOD AND MATERIALS

With IRB approval for this HIPAA-compliant retrospective study, we queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases (unenhanced, corticomedullary (CM), nephrographic (NP), and excretory (EX)). The entire lesion was segmented in each phase. A CAD algorithm determined a 0.5cm region of interest (ROI) of peak lesion enhancement ≤300HU within the 3D lesion contour. A 0.5cm ROI was placed in enhancing renal cortex. A radiologist approved all lesion contours and ROI placement. Relative enhancement (RE) was calculated as: (lesion ROI-cortex ROI)/ (cortex ROI)* 100%). A model was derived using logistical regression with RE of ccRCC, Onc, and fpAML as input. Discrimination was evaluated using receiver operator characteristic (ROC) curves. 

RESULTS

141 patients (61% men, 39% women) with 156 unique renal masses (99 (63%) ccRCC, 43 (28%) Onc, 14 (9%) fpAML) were analyzed. Mean lesion size in ccRCC= 3.1 cm (range 0.8-6.4), Onc=3.0 cm (range 1.0-6.5), and fpAML=2.2 cm (range 0.7-3.6). In discriminating ccRCC from Onc, the model had an AUC of 0.797 (0.726-0.869 95% CI) in the CM phase, 0.598 (0.499-0.697 95% CI) in the NP phase, and 0.672 (0.576-.0.768 95% CI) in the EX phase. In discriminating ccRCC from fpAML, the model had an AUC of 0.858 (0.767-0.952 95% CI) in the CM phase, 0.913 (0.837-0.988 95% CI) in the NP phase, and 0.913 (0.836-.0.989 95% CI) in the EX phase.

CONCLUSION

RE in the CM phase helps discriminate Onc from ccRCC with an AUC of 0.797, while the NP and EX phases help to discriminate fpAML from ccRCC with an AUC of 0.913. 

CLINICAL RELEVANCE/APPLICATION

CAD derived RE provides an objective and reproducible measure for the clinician to use when stratifying patients to specific therapeutic pathways, helping to ensure optimal patient outcomes.