Applications of machine learning for immunophenotypic measurable residual disease assessment in acute myeloid leukemia

Abstract

Immunophenotypic detection and quantification of residual leukemic cells by multiparameter flow cytometry is increasingly adopted in the clinical practice of acute myeloid leukemia (AML) to assess measurable residual disease (MRD). However, MRD levels quantified by manual gating analysis can differ based on differences in gating strategy between trained operators and clinical centers. Manual gating requires extensive training, is time-consuming in daily practice, and faces a significant hurdle in analyzing data from next-generation cytometry platforms. To address these challenges, several computational approaches involving machine learning and artificial intelligence algorithms have been proposed to automate or aid the assessment of MRD. However, the immunophenotypic variability between patients and the relatively low proportions of residual leukemic cells in AML challenge most algorithms and require innovative approaches. This review provides an overview of recent efforts in using computational methods for immunophenotypic AML-MRD assessment. We first explain the technical and conceptual background of the different algorithms that have been explored. Next, we discuss their strengths and limitations in the disease-specific context of AML. Finally, we highlight how computational approaches offer a unique opportunity to standardize or even outperform current manual gating analyses, and ultimately, improve the treatment of AML patients.