Development and Validation of a Deep-Learning Model to Assist with Renal Cell Carcinoma Histopathologic Interpretation.

To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) on histopathology specimens, as well as differentiate RCC histologic subtype and grade.

Digital hematoxylin and eosin stained biopsy images were downloaded from The Cancer Genome Atlas. A CNN model was trained on 100um2 samples of either normal (3,000 samples) or RCC (12,168 samples) tissue samples from 42 patients. RCC specimens included clear cell, chromophobe, and papillary histiotypes, as well as tissue of Fuhrman grades 1 through 4. Model testing was performed on an additional held-out cohort of benign and RCC specimens. Model performance was assessed on the basis of diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

The CNN model achieved an overall accuracy of 99.1% in the testing cohort for distinguishing normal parenchyma from RCC (sensitivity 100%, specificity 97.1%). Accuracy for distinguishing between clear cell, papillary, and chromophobe histiotypes was 97.5%. Accuracy for predicting Fuhrman grade was 98.4%.

CNNs are able to rapidly and accurately identify the presence of RCC, distinguish RCC histologic subtypes, and identify tumor grade by analyzing histopathology specimens.

Urology. 2020 Jul 22 [Epub ahead of print]

Michael Fenstermaker, Scott A Tomlins, Karandeep Singh, Jenna Wiens, Todd M Morgan

Department of Urology, University of Michigan, Ann Arbor, MI. Electronic address: ., Department of Pathology, University of Michigan, Ann Arbor, MI; University of Michigan Rogel Cancer Center, Ann Arbor, MI., Department of Internal Medicine, University of Michigan, Ann Arbor, MI., Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI., Department of Urology, University of Michigan, Ann Arbor, MI; University of Michigan Rogel Cancer Center, Ann Arbor, MI.