MP19-03 THE SHAPELY RENAL MASS: CONTOUR EVALUATION OF RENAL CELL CARCINOMA

Abstract

You have accessJournal of UrologyImaging/Radiology: Uroradiology III1 Apr 2016MP19-03 THE SHAPELY RENAL MASS: CONTOUR EVALUATION OF RENAL CELL CARCINOMA Felix Yap, Darryl Hwang, Steven Cen, Xuejun Zhang, Andre Luis de Castro Abreu, Mihir Desai, Monish Aron, Inderbir Gill, and Vinay Duddalwar Felix YapFelix Yap More articles by this author , Darryl HwangDarryl Hwang More articles by this author , Steven CenSteven Cen More articles by this author , Xuejun ZhangXuejun Zhang More articles by this author , Andre Luis de Castro AbreuAndre Luis de Castro Abreu More articles by this author , Mihir DesaiMihir Desai More articles by this author , Monish AronMonish Aron More articles by this author , Inderbir GillInderbir Gill More articles by this author , and Vinay DuddalwarVinay Duddalwar More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.2751AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Because clear cell renal cell carcinomas (ccRCC) can be difficult to differentiate from papillary RCC (pRCC) using enhancement properties alone, various metrics have been studied to help distinguish them including tumor shape. Visual analysis of a tumor contour however is subjective and susceptible to inter or intra-observer variability. We designed a computer algorithm using quantitative metrics to analyze shape features of renal tumors in three dimensions to create an objective and reproducible method of evaluating renal mass shape. We also investigated whether quantitative contour analysis can be utilized to objectively distinguish between ccRCC and pRCC on cross-sectional imaging. METHODS Computerized tomography (CT) images in 87 patients with RCC, including 68 diagnosed with clear cell RCC and 19 diagnosed with papillary RCC between 2011 and 2014 were extracted using an automated segmentation algorithm. Point coordinates of the tumor contours in each transverse slice were input into a MATLAB (MathWorks) algorithm. Weighted averages of ten shape metrics were calculated per tumor: compactness, mean radial distance (RD), RD standard deviation (RDSD), RD area ratio (RDAR), zero crossings (ZC), entropy, Feret ratio (FR), convex hull area (CHA) and perimeter (CHP) ratios, and elliptic compactness (EC). Shape parameters were also calculated in sagittal and coronal dimensions. The parameters were compared using the Wilcoxon rank-sum test to test the hypothesis that ccRCCs demonstrate more morphologic irregularity than pRCCs. RESULTS Quantitative contour analysis was technically successful in all 87 cases. There were significant differences between ccRCC and pRCC in 4 parameters in the coronal and sagittal dimensions: CHA, CHP, EC, and ZC. These parameters however did not show significant differences in the transverse dimension. CONCLUSIONS Computerized RCC image analysis using shape metrics is technically feasible and efficient. Most metrics confirm that ccRCC have a nonspheroidal shape. 4 of the metrics may help distinguish ccRCC from pRCC, and may be additional quantifiable parameters that can help differentiate other renal tumors. Future studies will determine further clinical applications of this contour analysis and its potential for better understanding of natural history, behavior, and growth of renal tumors. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e204 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Felix Yap More articles by this author Darryl Hwang More articles by this author Steven Cen More articles by this author Xuejun Zhang More articles by this author Andre Luis de Castro Abreu More articles by this author Mihir Desai More articles by this author Monish Aron More articles by this author Inderbir Gill More articles by this author Vinay Duddalwar More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...