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

Authors :
Swiderska-Chadaj, Z.S.
Hebeda, K.M.
Brand, M. van den
Litjens, G.J.
Swiderska-Chadaj, Z.S.
Hebeda, K.M.
Brand, M. van den
Litjens, G.J.
Source :
Virchows Archiv; 617; 621; 0945-6317; 3; vol. 479; ~Virchows Archiv~617~621~~~0945-6317~3~479~~
Publication Year :
2021

Abstract

Contains fulltext : 245153.pdf (Publisher’s version ) (Open Access)<br />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.

Details

Database :
OAIster
Journal :
Virchows Archiv; 617; 621; 0945-6317; 3; vol. 479; ~Virchows Archiv~617~621~~~0945-6317~3~479~~
Publication Type :
Electronic Resource
Accession number :
edsoai.on1292977308
Document Type :
Electronic Resource