Objective: Flow cytometry (FC) is a widely acknowledged technology in diagnosis of acute myeloid leukemia (AML) and has been indispensable in determining progression of the disease. Although FC plays a key role as a post-therapy prognosticator and evaluator of therapeutic efficacy, the manual analysis of cytometry data is a barrier to optimization of reproducibility and objectivity. This study investigates the utility of our recently introduced non-parametric Bayesian framework in accurately predicting the direction of change in disease progression in AML patients using FC data. Methods: The highly flexible non-parametric Bayesian model based on the infinite mixture of infinite Gaussian mixtures is used for jointly modeling data from multiple FC samples to automatically identify functionally distinct cell populations and their local realizations. Phenotype vectors are obtained by characterizing each sample by the proportions of recovered cell populations, which are in turn used to predict the direction of change in disease progression for each patient. Results: We used 200 diseased and non-diseased immunophenotypic panels for training and tested the system with 36 additional AML cases collected at multiple time points. The proposed framework identified the change in direction of disease progression with accuracies of 90% (9 out of 10) for relapsing cases and 100% (26 out of 26) for the remaining cases. Conclusions: We believe that these promising results are an important first step towards the development of automated predictive systems for disease monitoring and continuous response evaluation. Significance: Automated measurement and monitoring of therapeutic response is critical not only for objective evaluation of disease status prognosis but also for timely assessment of treatment strategies.


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flow cytometry, AML, Dirichlet process, nonparametric Bayesian, minimal residual disease

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