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Medientyp:
E-Artikel
Titel:
Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma
Beteiligte:
Swiderska-Chadaj, Zaneta;
Hebeda, Konnie M.;
van den Brand, Michiel;
Litjens, Geert
Erschienen:
Springer Science and Business Media LLC, 2021
Erschienen in:
Virchows Archiv, 479 (2021) 3, Seite 617-621
Sprache:
Englisch
DOI:
10.1007/s00428-020-02931-4
ISSN:
1432-2307;
0945-6317
Entstehung:
Anmerkungen:
Beschreibung:
AbstractIn 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.