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Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes

NCJ Number
Rahul Rajendran; Kevan Iffrig; Deepak K Pruthi; Allison Wheeler; Brian Neuman; Dharam Kaushik; Ahmed M Mansour; Karen Panetta; Sos Agaian; Michael A. Liss
Date Published
April 2019

This article details research associated with the development of software to assess the potential aggressiveness of incidentally detected renal masses.


The objective of this research was to develop software to assess the potential aggressiveness of an incidentally detected renal mass using images. Methodology used in this study included thirty randomly selected patients who underwent nephrectomy for renal cell carcinoma (RCC) had their images independently reviewed by engineers. Tumor “Roughness” was based on image algorithm of tumor topographic features visualized on computed tomography (CT) scans. Univariant and multivariant statistical analyses are utilized for analysis. Results of the research investigated 30 subjects that underwent partial or radical nephrectomy. After excluding poor image-rendered images, 27 patients remained (benign cyst=1, oncocytoma=2, clear cell RCC=15, papillary RCC=7, and chromophobe RCC=2). The mean roughness score for each mass is 1.18, 1.16, 1.27, 1.52, and 1.56 units, respectively. Renal masses were correlated with tumor roughness. However, tumor size itself was larger in benign tumors. Linear regression analysis noted that the roughness score is the most influential on the model with all other demographics being equal including tumor size. Conclusion of the research found that using basic CT imaging software, tumor topography (“roughness”) can be quantified and correlated with histologies such as RCC subtype and could lead to determining aggressiveness of small renal masses. (Published Abstract Provided)