Automated identification of cell populations in flow cytometry data with transformers

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 F-1 score of approximate to 0.94 when evaluated on 519 B-ALL samples and shows better results than existing methods on 4 different datasets.

Automated summary

This study presents an automated method to compute the Minimal Residual Disease (MRD) value directly from FCM data in Acute Lymphoblastic Leukemia (ALL) patients. The method utilizes a neural network based on the transformer architecture to identify blast cells in samples. Evaluation on multiple datasets shows that the proposed method outperforms existing methods.