Diagnosis of chronic B-cell lymphoproliferative disease in peripheral blood = how machine learning may help to the interpretation of flow cytometry data.

Flow cytometry (FCM) has become a method of choice for immunologic characterization of chronic lymphoproliferative disease (CLPD). To reduce the potential subjectivities of FCM data interpretation, we developed a machine learning random forest algorithm (RF) allowing unsupervised analysis. This assay relies on 16 parameters obtained from our FCM screening panel, routinely used in the exploration of peripheral blood (PB) samples (mean fluorescence intensity values (MFI) of CD19, CD45, CD5, CD20, CD200, CD23, HLA-DR, CD10 in CD19-gated B cells, ratio of kappa/Lambda, and different ratios of MFI B-cells/T-cells [CD20, CD200, CD23]).