Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers
This addresses the time-consuming and subjective diagnostic problem for pediatric acute leukemia patients, representing an incremental improvement by automating an existing manual method.
The paper tackles the manual and subjective immunophenotyping assessment for childhood acute leukemia by proposing FCM-Former, a self-attention-based tool that automates the process, achieving 96.5% accuracy in lineage assignment across 960 cases.
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.