Automated identification of cell populations in flow cytometry data with transformers.
Comput Biol Med
; 144: 105314, 2022 05.
Article
em En
| MEDLINE
| ID: mdl-35247762
ABSTRACT
Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents. A strong prognostic factor in ALL is given by the Minimal Residual Disease (MRD), which is a measure for the number of leukemic cells persistent in a patient. Manual MRD assessment from Multiparameter Flow Cytometry (FCM) data after treatment is time-consuming and subjective. In this work, we present an automated method to compute the MRD value directly from FCM data. We present a novel neural network approach based on the transformer architecture that learns to directly identify blast cells in a sample. We train our method in a supervised manner and evaluate it on publicly available ALL FCM data from three different clinical centers. Our method reaches a median F1 score of ≈0.94 when evaluated on 519 B-ALL samples and shows better results than existing methods on 4 different datasets.
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MEDLINE
Assunto principal:
Leucemia-Linfoma Linfoblástico de Células Precursoras
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article