Lygizou Elpiniki Maria, Reiter Michael, Maurer-Granofszky Margarita, Dworzak Michael, Grosu Radu
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10781595.
Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this malignancy is immunophenotyping based on Multiparameter Flow Cytometry (FCM). However, this approach is manual, and thus time-consuming and subjective. To alleviate this situation, we propose in this paper the FCM-Former, a machine learning, self-attention based FCM-diagnostic tool, automating the immunophenotyping assessment in Childhood Acute Leukemia. The FCM-Former is trained in a supervised manner, by directly using flow cytometric data. Our FCM-Former achieves an accuracy of 96.5% assigning lineage to each sample among 960 cases of either acute B-cell, T-cell lymphoblastic, and acute myeloid leukemia (B-ALL, T-ALL, AML). To the best of our knowledge, the FCM-Former is the first work that automates the immunophenotyping assessment with FCM data in diagnosing pediatric Acute Leukemia.
急性白血病是儿童和青少年中最常见的血液系统恶性肿瘤。对这种恶性肿瘤进行诊断评估的一个关键方法是基于多参数流式细胞术（FCM）的免疫表型分析。然而，这种方法是人工操作的，因此既耗时又主观。为了缓解这种情况，我们在本文中提出了FCM-Former，这是一种基于机器学习和自注意力机制的FCM诊断工具，可自动进行儿童急性白血病的免疫表型评估。FCM-Former通过直接使用流式细胞术数据以监督方式进行训练。我们的FCM-Former在960例急性B细胞、T细胞淋巴细胞白血病和急性髓细胞白血病（B-ALL、T-ALL、AML）病例中，为每个样本指定谱系的准确率达到了96.5%。据我们所知，FCM-Former是第一项利用FCM数据自动进行免疫表型评估以诊断儿童急性白血病的研究。
