Quantitative Contour Analysis as an Image-based Discriminator Between Benign and Malignant Renal Tumors.

Autor: Yap FY; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA. Electronic address: felix.yap@med.usc.edu., Hwang DH; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Cen SY; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Varghese BA; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Desai B; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Quinn BD; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Gupta MN; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Rajarubendra N; Institute of Urology and the Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Desai MM; Institute of Urology and the Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Aron M; Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Liang G; Institute of Urology and the Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Aron M; Institute of Urology and the Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Gill IS; Institute of Urology and the Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA., Duddalwar VA; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA.
Jazyk: angličtina
Zdroj: Urology [Urology] 2018 Apr; Vol. 114, pp. 121-127. Date of Electronic Publication: 2018 Jan 02.
DOI: 10.1016/j.urology.2017.12.018
Abstrakt: Objective: To investigate whether morphologic analysis can differentiate between benign and malignant renal tumors on clinically acquired imaging.
Materials and Methods: Between 2009 and 2014, 3-dimensional tumor volumes were manually segmented from contrast-enhanced computerized tomography (CT) images from 150 patients with predominantly solid, nonmacroscopic fat-containing renal tumors: 100 renal cell carcinomas and 50 benign lesions (eg, oncocytoma and lipid-poor angiomyolipoma). Tessellated 3-dimensional tumor models were created from segmented voxels using MATLAB code. Eleven shape descriptors were calculated: sphericity, compactness, mean radial distance, standard deviation of the radial distance, radial distance area ratio, zero crossing, entropy, Feret ratio, convex hull area and convex hull perimeter ratios, and elliptic compactness. Morphometric parameters were compared using the Wilcoxon rank-sum test to investigate whether malignant renal masses demonstrate more morphologic irregularity than benign ones.
Results: 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 = .04). When comparing these 2 metrics between coronal and sagittal orientations, similar but nonsignificant trends emerged (P = .07). Other metrics tested were not significantly different in any imaging plane.
Conclusion: 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 3 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.
(Copyright © 2017 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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