Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers

Elpiniki Maria Lygizou, Michael Reiter, Margarita Maurer-Granofszky, Michael Dworzak, Radu Grosu
6/26/2024
cs.LGq-bio.QM

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Abstract

Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this malignancy is immunophenotyping based on Multiparameter Flow Cytometry (FCM). However, this approach is manual, and thus time-consuming and subjective. To alleviate this situation, we propose in this paper the FCM-Former, a machine learning, self-attention based FCM-diagnostic tool, automating the immunophenotyping assessment in Childhood Acute Leukemia. The FCM-Former is trained in a supervised manner, by directly using flow cytometric data. Our FCM-Former achieves an accuracy of 96.5% assigning lineage to each sample among 960 cases of either acute B-cell, T-cell lymphoblastic, and acute myeloid leukemia (B-ALL, T-ALL, AML). To the best of our knowledge, the FCM-Former is the first work that automates the immunophenotyping assessment with FCM data in diagnosing pediatric Acute Leukemia.

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Cite this paper

@article{lygizou2024automated,
  title  = {Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers},
  author = {Elpiniki Maria Lygizou and Michael Reiter and Margarita Maurer-Granofszky and Michael Dworzak and Radu Grosu},
  year   = {2024},
  eprint = {2406.18309},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.18309v1}
}

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