目前用于诊断急性髓系白血病 (AML) 的血液和骨髓样本的流式细胞术分析在很大程度上依赖于处理和分析步骤中的人工干预，这给结果诊断带来了很大的主观性，并且需要训练有素的人员。此外，通过细胞遗传学和靶向测序同时进行分子表征可能需要数天时间，从而延误患者的诊断和治疗。基于注意力的多实例学习模型（ABMILM）是深度学习模型，它可以做出准确的预测并生成有关单个事件/细胞样本分类的可解释的见解；然而，这些模型尚未应用于流式细胞术数据。在这项研究中，我们开发了一个使用 ABMILM 的计算管道，用于仅基于流式细胞术数据自动诊断 AML 病例。对 1,820 个流式细胞术样本的分析表明，该管道可以准确诊断急性白血病 [AUROC 0.961]，并准确地区分 AML 与 B 和 T 淋巴细胞白血病 [AUROC 0.965]。用于预测 AML 中 9 种细胞遗传学异常和 32 种致病变异的模型提供了准确的预测，特别是对于 t(15;17)(PML::RARA) [AUROC 0.929]、t(8;21)(RUNX1::RUNX1T1) [AUROC 0.814] 和 NPM1 变体 [AUROC 0.807]。最后，我们展示了这些模型如何生成可解释的见解，了解哪些流式细胞术事件和标记物提供最佳的诊断效用，为血液病理学家提供数据可视化工具以改进数据解释，以及流式细胞术标记物表达与细胞遗传学/分子之间的新颖生物学关联AML 的变体。我们的研究首次说明了使用基于深度学习的流式细胞术数据分析进行自动化 AML 诊断和分子表征的可行性。版权所有 © 2023。由 Elsevier Inc. 出版。

Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both 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 which 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 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML versus 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.Copyright © 2023. Published by Elsevier Inc.