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

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-st...

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Detalles Bibliográficos
Autores principales: Swiderska-Chadaj, Zaneta, Hebeda, Konnie M., van den Brand, Michiel, Litjens, Geert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448690/
https://www.ncbi.nlm.nih.gov/pubmed/32979109
http://dx.doi.org/10.1007/s00428-020-02931-4
Descripción
Sumario: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.