Artificial Intelligence Accelerates the Interpretation of Measurable Residual B Lymphoblastic Leukemia by Flow Cytometry

Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155,884 cells per case, on average (87% cellularity reduction), while preserving small MRD populations (median 100% retention for MRD <1%) and allowing for true %MRD estimates using a correction factor. A deep neural network (DNN) cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score = 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve, AUC = 0.98) or B-lymphoid cells (AUC = 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (SD = 0.57); with 100% positive agreement with conventional analysis (for MRD ≥ 0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance, and has the potential of facilitating BALL-MRD analysis by more clinical laboratories.