Background Renal cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ablation, increasingly utilized to treat small renal masses and preserve renal parenchyma, require precise preoperative imaging interpretation. We sought to develop and evaluate a convolutional neural network (CNN), a type of deep learning artificial intelligence, to act as a surgical planning aid by determining renal tumor and kidney volumes via segmentation on single-phase computed tomography (CT). Materials and Methods After institutional review board approval, the CT images of 319 patients were retrospectively analyzed. Two distinct CNNs were developed for (1) bounding cube localization of the right and left hemi-abdomen and (2) segmentation of the renal parenchyma and tumor within each bounding cube. Training was performed on a randomly selected cohort of 269 patients. CNN performance was evaluated on a separate cohort of 50 patients using Sorensen-Dice coefficients (which measures the spatial overlap between the manually segmented and neural network derived segmentations) and Pearson correlation coefficients. Experiments were run on a GPU-optimized workstation with a single NVIDIA GeForce GTX Titan X (12GB, Maxwell architecture). Results Median Dice coefficients for kidney and tumor segmentation were 0.970 and 0.816, respectively; Pearson correlation coefficients between CNN-generated and human-annotated estimates for kidney and tumor volume were 0.998 and 0.993 (p < 0.001), respectively. End-to-end trained CNNs were able to perform renal parenchyma and tumor segmentation on a new test case in an average of 5.6 seconds. Conclusions Initial experience with automated deep learning artificial intelligence demonstrates that it is capable of rapidly and accurately segmenting kidneys and renal tumors on single-phase contrast-enhanced CT scans and calculating tumor and renal volumes.
Journal of endourology. 2021 Apr 13 [Epub ahead of print]
Roozbeh Houshyar, Justin Glavis-Bloom, Thanh-Lan Bui, Chantal Chahine, Michelle D Bardis, Alexander Ushinsky, Hanna Liu, Param Bhatter, Elliott Lebby, Dylann Fujimoto, William Grant, Karen Tran-Harding, Jaime Landman, Daniel S Chow, Peter D Chang
University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States., Washington University in St Louis School of Medicine, 12275, Mallinckrodt Institute of Radiology, St Louis, Missouri, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States; ., University of California Irvine, Urology, 333 City Blvd West, Orange, California, United States, 92868; ., University of California Irvine School of Medicine, 12219, Radiological Sciences, 101 The City Dr S, Orange, California, United States, 92697-3950.