This study indicates that some demographic and histopathologic features of CM in this population differ from those in the literature. More studies including cohort studies are needed to fully describe the nature and survival rate of CM in this area.

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To segment the kidney and its large tumors, we combine a deep neural network and thresholding technique. The deep network segments kidney, and its output is used to detect probable renal tumors. We compare the kidney volume with a normal kidney shape. Incomplete shapes are searched for tumors. Using a seed point the center of the tumor cluster is defined. Then, the pixels of a slice is labeled as normal or abnormal. The labeled pixels are post-processed using morphological filters to refine the result. The outcome of the algorithm is the tumor volume.