文章：

急性髓系白血病：流式细胞术的诊断与评估

Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry

原文发布日期：17 November 2024

DOI: 10.3390/cancers16223855

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

随着近期技术进展及对急性髓系白血病（AML）发病机制认识的深化，世界卫生组织第五版分类（WHO-HAEM5）更新版、新推出的国际共识分类（ICC）以及2022年欧洲白血病网（ELN）指南均要求整合免疫表型、细胞遗传学与分子生物学数据，并结合临床与形态学发现，以实现AML的精准诊断、预后评估及治疗策略指导。流式细胞术通过多参数分析方法提供快速、灵敏的免疫表型检测，已成为AML分型、治疗靶点识别及治疗后可测量残留病（MRD）监测的关键实验室工具。免疫表型特征与重现性遗传异常之间的关联性已得到确认，并应用于指导进一步诊断评估与即时治疗决策。近期研究表明，机器学习模型在辅助流式细胞数据分析以实现自动化诊断及预测潜在遗传变异方面正发挥日益重要的作用。

原文链接：

Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry