Acute myeloid leukemia with inv(16)(p13.1q22), abnormal eosinophils, and absence of peripheral blood and bone marrow blasts

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

The core concepts of core binding factor acute myeloid leukemia: Current considerations for prognosis and treatment

Core binding factor acute myeloid leukemia (CBF AML), defined by t(8;21) or inv(16), is a subset of favorable risk AML. Despite its association with a high complete remission rate after induction and relatively good prognosis overall compared with other subtypes of AML, relapse risk after induction chemotherapy remains high. Optimizing treatment planning to promote recurrence free survival and increase the likelihood of survival after relapse is imperative to improving outcomes. Recent areas of research have included evaluation of the role of gemtuzumab in induction and consolidation, the relative benefit of increased cycles of high dose cytarabine in consolidation, the utility of hypomethylating agents and kinase inhibitors, and the most appropriate timing of stem cell transplant. Surveillance with measurable residual disease testing is increasingly being utilized for monitoring disease in remission, and ongoing investigation seeks to determine how to use this tool for early identification of patients who would benefit from proceeding to transplant. In this review, we outline the current therapeutic approach from diagnosis to relapse while highlighting the active areas of investigation in each stage of treatment.