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A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data
Abstract
Objectives: Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model’s performance.

Methods: Using data from 531 patients who underwent evaluation for cytopenias and/ or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification.

Results: High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties.

Conclusions: Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.
Figures
An overall schematic diagram of the machine learning (ML) framework.
An overall schematic diagram of the machine learning (ML) framework.
Performance of the machine learning (ML) model for classification of acute leukemias and distinction from nonneoplastic cytopenias.
Performance of the machine learning (ML) model for classification of acute leukemias and distinction from nonneoplastic cytopenias.
Keywords
Machine learning | Flow cytometry | Acute promyelocytic leukemia | Acute myeloid leukemia | B-cell lymphoblastic leukemia/lymphoma
Publication Date
2021/10/13
Journal
American Journal of Clinical Pathology
American Journal of Clinical Pathology 2021
DOI
10.1093/ajcp/aqab148
Publisher
Oxford University Press