  Renal cell carcinoma (RCC) is the most common renal cancer in adults. The
histopathologic classification of RCC is essential for diagnosis, prognosis,
and management of patients. Reorganization and classification of complex
histologic patterns of RCC on biopsy and surgical resection slides under a
microscope remains a heavily specialized, error-prone, and time-consuming task
for pathologists. In this study, we developed a deep neural network model that
can accurately classify digitized surgical resection slides and biopsy slides
into five related classes: clear cell RCC, papillary RCC, chromophobe RCC,
renal oncocytoma, and normal. In addition to the whole-slide classification
pipeline, we visualized the identified indicative regions and features on
slides for classification by reprocessing patch-level classification results to
ensure the explainability of our diagnostic model. We evaluated our model on
independent test sets of 78 surgical resection whole slides and 79 biopsy
slides from our tertiary medical institution, and 69 randomly selected surgical
resection slides from The Cancer Genome Atlas (TCGA) database. The average area
under 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 our
approach across different data sources and specimen types. More importantly,
our model has the potential to assist pathologists by (1) automatically
pre-screening slides to reduce false-negative cases, (2) highlighting regions
of importance on digitized slides to accelerate diagnosis, and (3) providing
objective and accurate diagnosis as the second opinion.
