A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals

Dongguang Li, Jacob R. Bledsoe, Yu Zeng, Wei Liu, Yiguo Hu, Ke Bi, Aibin Liang and Shaoguang Li ()
Additional contact information
Dongguang Li: University of Massachusetts Medical School
Jacob R. Bledsoe: University of Massachusetts Memorial Medical Center
Yu Zeng: Tongji Hospital of Tongji University School of Medicine
Wei Liu: The First Affiliated Hospital of Soochow University
Yiguo Hu: West China Hospital, Sichuan University
Ke Bi: Tongji Hospital of Tongji University School of Medicine
Aibin Liang: Tongji Hospital of Tongji University School of Medicine
Shaoguang Li: University of Massachusetts Medical School

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide treatment options, but this requirement is difficult to meet. Although artificial intelligence (AI) helps to reduce the labor of reading pathologic slides, diagnostic accuracy has not reached a clinically usable level. Establishment of an AI model often demands big datasets and an ability to handle large variations in sample preparation and image collection. Here, we establish a highly accurate deep learning platform, consisting of multiple convolutional neural networks, to classify pathologic images by using smaller datasets. We analyze human diffuse large B-cell lymphoma (DLBCL) and non-DLBCL pathologic images from three hospitals separately using AI models, and obtain a diagnostic rate of close to 100 percent (100% for hospital A, 99.71% for hospital B and 100% for hospital C). The technical variability introduced by slide preparation and image collection reduces AI model performance in cross-hospital tests, but the 100% diagnostic accuracy is maintained after its elimination. It is now clinically practical to utilize deep learning models for diagnosis of DLBCL and ultimately other human hematopoietic malignancies.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-19817-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19817-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-19817-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().