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.
Clear cell renal cell carcinoma (ccRCC) is a common cancer and could result in poor prognosis. Understanding individual tumor immune microenvironment (TIME) in ccRCC patients may predict prognosis and response to therapy. In this work, we explore the concept of using radiomic features extracted from computer tomography (CT) imaging to correlate the TIME measurements from multiplex immunohistochemistry (mIHC) analysis. Since CT imaging has long been the standard for evaluation of RCCs, it has the potential to provide noninvasive approximations of the tissue-based mIHC biomarkers. We selected two biomarkers that were grounded by clinical research: PD-L1 expression and CD8+PD-1+ T cell to CD8+ T cell ratio of the tumor epithelium. Then we extracted these two markers from a preliminary set of 52 patients using automated mIHC analysis. We used Random Forest, AdaBoost and ElasticNet to classify each sample as either expressing high or low levels of these markers. We found the radiomic features can correlate tumor epithelium PD-L1 >5%, PD-L1 >10%, and CD8+PD1+/CD8+ >37% with AUROC 0.75, 0.85 and 0.71, respectively.
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