Ally Feras, Chen Xueyan
Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA.
Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Cancers (Basel). 2024 Nov 17;16(22):3855. doi: 10.3390/cancers16223855.
With recent technological advances and significant progress in understanding the pathogenesis of acute myeloid leukemia (AML), the updated fifth edition WHO Classification (WHO-HAEM5) and the newly introduced International Consensus Classification (ICC), as well as the European LeukemiaNet (ELN) recommendations in 2022, require the integration of immunophenotypic, cytogenetic, and molecular data, alongside clinical and morphologic findings, for accurate diagnosis, prognostication, and guiding therapeutic strategies in AML. Flow cytometry offers rapid and sensitive immunophenotyping through a multiparametric approach and is a pivotal laboratory tool for the classification of AML, identification of therapeutic targets, and monitoring of measurable residual disease (MRD) post therapy. The association of immunophenotypic features and recurrent genetic abnormalities has been recognized and applied in informing further diagnostic evaluation and immediate therapeutic decision-making. Recently, the evolving role of machine learning models in assisting flow cytometric data analysis for the automated diagnosis and prediction of underlying genetic alterations has been illustrated.
随着近期技术的进步以及在急性髓系白血病（AML）发病机制理解方面取得的重大进展，更新后的第五版世界卫生组织分类（WHO-HAEM5）、新引入的国际共识分类（ICC）以及2022年欧洲白血病网（ELN）的建议，都要求将免疫表型、细胞遗传学和分子数据与临床及形态学结果相结合，以准确诊断、预测AML并指导治疗策略。流式细胞术通过多参数方法提供快速且灵敏的免疫表型分析，是AML分类、治疗靶点识别以及治疗后可测量残留病（MRD）监测的关键实验室工具。免疫表型特征与复发性基因异常之间的关联已得到认可，并应用于进一步的诊断评估和即时治疗决策。最近，机器学习模型在协助流式细胞术数据分析以自动诊断和预测潜在基因改变方面的作用也得到了体现。
