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Updated: Dec 7, 2025

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma
Published on: January 9, 2019
Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma
Zaneta Swiderska-Chadaj1,2, Konnie M Hebeda3, Michiel van den Brand3,4
1Department of Pathology, Radboud University Medical Center, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands. zaneta.swiderska@gmail.com.
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.
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