Renal Cell Carcinoma Staging with Learnable Image Histogram-Based Deep Neural Network

Renal cell carcinoma (RCC) is the seventh most common cancer worldwide, accounting for an estimated 140,000 global deaths annually. An important RCC prognostic predictor is its 'stage' for which the tumor-node-metastasis (TNM) staging system is used. Although TNM staging is performed by radiologists via pre-surgery volumetric medical image analysis, a recent study suggested that such staging may be performed by studying the image features of the RCC from computed tomography (CT) data. Currently TNM staging mostly relies on laborious manual processes based on visual inspection of 2D CT image slices that are time-consuming and subjective; a recent study reported about similar to 25% misclassification in their patient pools. Recently, we proposed a learnable image histogram based deep neural network approach (ImHist-Net) for RCC grading, which is capable of learning textural features directly from the CT images. In this paper, using a similar architecture, we perform the stage low (I/II) and high (III/IV) classification for RCC in CT scans. Validated on a clinical CT dataset of 159 patients from the TCIA database, our method classified RCC low and high stages with about 83% accuracy.