UCSF

Tumor grading is an important prognostic parameter for renal cell carcinoma (RCC). However, current grading schemes require an invasive surgical procedure, putting patients at risks including increased risk of hemorrhage, infection, renal failure, or death. Furthermore, low grade RCC is indolent with low mortality risk and may not require treatment. Therefore, a pre-operative and non-invasive assessment of malignancy grade may be beneficial and facilitate optimal timing of treatment. In recent years, deep learning-based image analysis has gained wide popularity in cancer prognosis and prediction. The goal of this study is to investigate the feasibility and performance of a deep-learning-based model for clear cell RCC grading prediction from contrast-enhance computed tomography (CECT). After institutional review board approval, an institutional pathology database was queried for all renal biopsies between December 2002 and October 2018. All included patients received a CT with a non-contrast and at least one post-contrast series. All patients have Fuhrman grade confirmation from surgical pathology, with CT scan prior to the procedure. Tumors were manually annotated by a radiologist on either the corticomedullary or nephrographic phase CECT. Rectangular regions of interest (ROI) were drawn on each slice throughout the tumor and used as inputs to the Deep CNN ResNet50. A binary label of low grade or high grade was assigned to each patient. Sensitivity, specificity, accuracy, and AUC were calculated based on a five-fold cross-validation. Preliminary results from a small subset of data suggests that a deep learning model can be used to predict clear cell RCC grading based on CT imaging prior to surgical procedures.