Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

Abstract The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for...

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Autores principales:	Shivam Kalra, H. R. Tizhoosh, Sultaan Shah, Charles Choi, Savvas Damaskinos, Amir Safarpoor, Sobhan Shafiei, Morteza Babaie, Phedias Diamandis, Clinton J. V. Campbell, Liron Pantanowitz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/236179d0be3f4c138cb25ddc2e00fa92
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spelling	oai:doaj.org-article:236179d0be3f4c138cb25ddc2e00fa922021-12-02T15:53:00ZPan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence10.1038/s41746-020-0238-22398-6352https://doaj.org/article/236179d0be3f4c138cb25ddc2e00fa922020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0238-2https://doaj.org/toc/2398-6352Abstract The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas, TCGA) of whole-slide images from almost 11,000 patients. We successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000 × 1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative “majority voting” to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen section slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%). The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype.Shivam KalraH. R. TizhooshSultaan ShahCharles ChoiSavvas DamaskinosAmir SafarpoorSobhan ShafieiMorteza BabaiePhedias DiamandisClinton J. V. CampbellLiron PantanowitzNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Shivam Kalra H. R. Tizhoosh Sultaan Shah Charles Choi Savvas Damaskinos Amir Safarpoor Sobhan Shafiei Morteza Babaie Phedias Diamandis Clinton J. V. Campbell Liron Pantanowitz Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
description	Abstract The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas, TCGA) of whole-slide images from almost 11,000 patients. We successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000 × 1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative “majority voting” to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen section slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%). The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype.
format	article
author	Shivam Kalra H. R. Tizhoosh Sultaan Shah Charles Choi Savvas Damaskinos Amir Safarpoor Sobhan Shafiei Morteza Babaie Phedias Diamandis Clinton J. V. Campbell Liron Pantanowitz
author_facet	Shivam Kalra H. R. Tizhoosh Sultaan Shah Charles Choi Savvas Damaskinos Amir Safarpoor Sobhan Shafiei Morteza Babaie Phedias Diamandis Clinton J. V. Campbell Liron Pantanowitz
author_sort	Shivam Kalra
title	Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
title_short	Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
title_full	Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
title_fullStr	Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
title_full_unstemmed	Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
title_sort	pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/236179d0be3f4c138cb25ddc2e00fa92
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Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

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