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...

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Autores principales:	Mengdan Zhu, Bing Ren, Ryland Richards, Matthew Suriawinata, Naofumi Tomita, Saeed Hassanpour
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6602e0ca0c374c8196a2819c9620653f
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spelling	oai:doaj.org-article:6602e0ca0c374c8196a2819c9620653f2021-12-02T14:24:56ZDevelopment and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides10.1038/s41598-021-86540-42045-2322https://doaj.org/article/6602e0ca0c374c8196a2819c9620653f2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86540-4https://doaj.org/toc/2045-2322Abstract 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 917 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 (95% confidence interval (CI): 0.97–1.00), 0.98 (95% CI: 0.96–1.00) and 0.97 (95% CI: 0.96–0.98), 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.Mengdan ZhuBing RenRyland RichardsMatthew SuriawinataNaofumi TomitaSaeed HassanpourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mengdan Zhu Bing Ren Ryland Richards Matthew Suriawinata Naofumi Tomita Saeed Hassanpour Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides
description	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 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 917 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 (95% confidence interval (CI): 0.97–1.00), 0.98 (95% CI: 0.96–1.00) and 0.97 (95% CI: 0.96–0.98), 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.
format	article
author	Mengdan Zhu Bing Ren Ryland Richards Matthew Suriawinata Naofumi Tomita Saeed Hassanpour
author_facet	Mengdan Zhu Bing Ren Ryland Richards Matthew Suriawinata Naofumi Tomita Saeed Hassanpour
author_sort	Mengdan Zhu
title	Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides
title_short	Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides
title_full	Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides
title_fullStr	Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides
title_full_unstemmed	Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides
title_sort	development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6602e0ca0c374c8196a2819c9620653f
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Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides

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