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Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma

Authors :
Zaneta Swiderska-Chadaj
Konnie M. Hebeda
Geert Litjens
Michiel van den Brand
Source :
Virchows Archiv, 479, 3, pp. 617-621, Virchows Archiv, 479, 617-621, Virchows Archiv
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

Abstract

In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slides of diffuse large B-cell lymphoma. The H&E-stained slides of 287 cases from 11 hospitals were used for training and evaluation. The overall sensitivity to detect MYC rearrangement was 0.93 and the specificity 0.52, showing that prediction of MYC translocation based on morphology alone was possible in 93% of MYC-rearranged cases. This would allow a simple and fast prescreening, saving approximately 34% of genetic tests with the current algorithm. Electronic supplementary material The online version of this article (10.1007/s00428-020-02931-4) contains supplementary material, which is available to authorized users.

Details

ISSN :
14322307 and 09456317
Volume :
479
Database :
OpenAIRE
Journal :
Virchows Archiv
Accession number :
edsair.doi.dedup.....0658884898bdc886772f6f9480e7fe7c
Full Text :
https://doi.org/10.1007/s00428-020-02931-4