Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides.

ABSTRACT: 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 reprocessin

SUBMITTER: Zhu M 

PROVIDER: S-EPMC8007643 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature