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Proceedings Paper

Predicting MYC translocation in HE specimens of diffuse large B-cell lymphoma through deep learning
Author(s): Zaneta Swiderska-Chadaj; Konnie Hebeda; Michiel van den Brand; Geert Litjens
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Paper Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common type of B-cell lymphoma. It is characterized by a heterogeneous morphology, genetic changes and clinical behavior. A small specific subgroup of DLBCL, harbouring a MYC gene translocation is associated with worse patient prognosis and outcome. Typically, the MYC translocation is assessed with a molecular test (FISH), that is expensive and time-consuming. Our hypothesis is that genetic changes, such as translocations could be visible as changes in the morphology of an HE-stained specimen. However, it has not proven possible to use morphological criteria for the detection of a MYC translocation in the diagnostic setting due to lack of specificity.

In this paper, we apply a deep learning model to automate detection of the MYC translocations in DLBCL based on HE-stained specimens. The proposed method works at the whole-slide level and was developed based on a multicenter data cohort of 91 patients. All specimens were stained with HE, and the MYC translocation was confirmed using fluorescence in situ hybridization (FISH). The system was evaluated on an additional 66 patients, and obtained AUROC of 0.83 and accuracy of 0.77. The proposed method presents proof of a concept giving insights in the applicability of deep learning methods for detection of a genetic changes in DLBCL. In future work we will evaluate our algorithm for automatic pre-screen of DLBCL specimens to obviate FISH analysis in a large number of patients.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132010 (16 March 2020); doi: 10.1117/12.2549650
Show Author Affiliations
Zaneta Swiderska-Chadaj, Radboud Univ. Medical Ctr. (Netherlands)
Konnie Hebeda, Radboud Univ. Medical Ctr. (Netherlands)
Michiel van den Brand, Radboud Univ. Medical Ctr. (Netherlands)
Geert Litjens, Radboud Univ. Medical Ctr. (Netherlands)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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