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

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.

Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models.

Background: The proportion of residual leukemic blasts after chemotherapy assessed by multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and overall survival in acute myeloid leukemia (AML). This measurable residual disease (MRD) is used in clinical trials to stratify patients for more or less intensive consolidation therapy. However, an objective and reproducible analysis method to assess MRD status from flow cytometry data is lacking, yet is highly anticipated for broader implementation of MRD testing.

Impact of hemodilution on flow cytometry based measurable residual disease assessment in acute myeloid leukemia.

Measurable residual disease (MRD) measured in the bone marrow (BM) of acute myeloid leukemia (AML) patients after induction chemotherapy is an established prognostic factor. Hemodilution, stemming from peripheral blood (PB) mixing within BM during aspiration, can yield false-negative MRD results. We prospectively examined hemodilution by measuring MRD in BM aspirates obtained from three consecutive 2 mL pulls, along with PB samples.