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Renal cell carcinoma (RCC) is the most common renal cancer in adults. Thehistopathologic classification of RCC is essential for diagnosis, prognosis,and management of patients. Reorganization and classification of complexhistologic patterns of RCC on biopsy and surgical resection slides under amicroscope remains a heavily specialized, error-prone, and time-consuming taskfor pathologists. In this study, we developed a deep neural network model thatcan accurately classify digitized surgical resection slides and biopsy slidesinto five related classes: clear cell RCC, papillary RCC, chromophobe RCC,renal oncocytoma, and normal. In addition to the whole-slide classificationpipeline, we visualized the identified indicative regions and features onslides for classification by reprocessing patch-level classification results toensure the explainability of our diagnostic model. We evaluated our model onindependent test sets of 78 surgical resection whole slides and 79 biopsyslides from our tertiary medical institution, and 69 randomly selected surgicalresection slides from The Cancer Genome Atlas (TCGA) database. The average areaunder the curve (AUC) of our classifier on the internal resection slides,internal biopsy slides, and external TCGA slides is 0.98, 0.98 and 0.99,respectively. Our results suggest that the high generalizability of ourapproach across different data sources and specimen types. More importantly,our model has the potential to assist pathologists by (1) automaticallypre-screening slides to reduce false-negative cases, (2) highlighting regionsof importance on digitized slides to accelerate diagnosis, and (3) providingobjective and accurate diagnosis as the second opinion.

Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides