Quantitative Contour Analysis as an Image-Based Discriminator between Benign and Malignant Renal Tumors

To investigate whether morphologic analysis can differentiate benign versus malignant renal tumors on clinically acquired imaging.

Between 2009 and 2014, 3D tumor volumes were manually segmented from contrast-enhanced computerized tomography (CT) images from 150 patients with predominantly solid, non-macroscopic fat-containing renal tumors: 100 renal cell carcinomas (RCC) and 50 benign lesions (e. g. oncocytoma, lipid-poor angiomyolipoma). Tessellated 3D tumor models were created from segmented voxels using MATLAB code. Eleven shape descriptors were calculated: sphericity, compactness, mean radial distance (RD), RD standard deviation, RD area ratio, zero crossings, entropy, Feret ratio, convex hull area (CHA) and perimeter (CHP) ratios, and elliptic compactness (EC). Morphometric parameters were compared using Wilcoxon rank-sum test to investigate whether malignant renal masses demonstrate more morphologic irregularity than benign ones.

Only CHP in sagittal orientation (median 0.96 vs. 0.97) and EC in coronal orientation (median 0.92 vs. 0.93) differed significantly between malignant and benign masses (p = 0.04). When comparing these two metrics between coronal and sagittal orientations, similar but nonsignificant trends emerged (p = 0.07). Other metrics tested were not significantly different in any imaging plane.

Computerized image analysis is feasible using shape descriptors that otherwise cannot be visually assessed and used without quantification. Shape analysis via the transverse orientation may be reasonable, but encompassing all three planar dimensions to characterize tumor contour can achieve a more comprehensive evaluation. Two shape metrics (CHP and EC) may help distinguish benign from malignant renal tumors, an often challenging goal to achieve on imaging and biopsy.

Urology. 2018 Jan 02 [Epub ahead of print]

Felix Y Yap, Darryl H Hwang, Steven Y Cen, Bino A Varghese, Bhushan Desai, Brian D Quinn, Megha Nayyar Gupta, Nieroshan Rajarubendra, Mihir M Desai, Manju Aron, Gangning Liang, Monish Aron, Inderbir S Gill, Vinay A Duddalwar

Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA. Electronic address: ., Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Institute of Urology and the Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA.

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