Multi-platform fractal dimension analysis of renal masses from multiphase contrast-enhanced computed tomography as markers for tumor subtyping

Morphological metrics such as fractal dimension (FD) have shown value as diagnostic and prognostic markers in diverse cancers. A lack of procedural consensus on fractal techniques may lead to a non-generalization of results across different studies. This study reports variations of Computed Tomography (CT) derived FD renal masses across different fractal analysis implementations. The Fraclac grayscale pixel size 512x512 pixel setting Area Under Curve (AUC) showed the highest AUC value (0.59) among all pixel settings in classifying clear cell renal cell carcinoma (ccRCC) vs. Oncocytoma and liquid poor angiomyolipoma (AML). Similarly, for the multiphase analysis, we also explored MATLAB grayscale pixel sizes from 7x7 to 256x256 pixels. Results showed that the 64x64 pixel setting had the highest AUC of 0.60-0.72 for ccRCC vs. Oncocytoma and AML and AUC of 0.58-0.69 for chromophobe renal cell carcinoma (RCC) vs Oncocytoma.

Automated summary

Morphological metrics like fractal dimension (FD) have been proven to be valuable in diagnosing and predicting various cancers. The lack of procedural consensus in fractal techniques may hinder the generalizability of results across different studies. This study investigates the variations of FD in renal masses derived from Computed Tomography (CT) using different fractal analysis implementations.