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

Urology. 2018 Apr;114:121-127. doi: 10.1016/j.urology.2017.12.018. Epub 2018 Jan 2.


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.

MeSH terms

  • Adenoma, Oxyphilic / diagnostic imaging*
  • Adenoma, Oxyphilic / pathology
  • Algorithms
  • Angiomyolipoma / diagnostic imaging*
  • Angiomyolipoma / pathology
  • Carcinoma, Renal Cell / diagnostic imaging*
  • Carcinoma, Renal Cell / pathology
  • Contrast Media
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Imaging, Three-Dimensional
  • Kidney Neoplasms / diagnostic imaging*
  • Kidney Neoplasms / pathology*
  • Observer Variation
  • Tomography, X-Ray Computed
  • Tumor Burden


  • Contrast Media

Supplementary concepts

  • Oncocytoma, renal