Hoffmann Joerg, Thrun Michael C, Röhnert Maximilian A, von Bonin Malte, Oelschlägel Uta, Neubauer Andreas, Ultsch Alfred, Brendel Cornelia
Department of Hematology, Oncology and Immunology, Philipps University Marburg, University Hospital Giessen and Marburg, Marburg, Germany.
Databionics, Mathematics and Computer Science, Philipps University Marburg, Marburg, Germany.
Cytometry A. 2023 Apr;103(4):304-312. doi: 10.1002/cyto.a.24686. Epub 2022 Sep 12.
Minimal residual disease (MRD) detection is a strong predictor for survival and relapse in acute myeloid leukemia (AML). MRD can be either determined by molecular assessment strategies or via multiparameter flow cytometry. The degree of bone marrow (BM) dilution with peripheral blood (PB) increases with aspiration volume causing consecutive underestimation of the residual AML blast amount. In order to prevent false-negative MRD results, we developed Cinderella, a simple automated method for one-tube simultaneous measurement of hemodilution in BM samples and MRD level. The explainable artificial intelligence (XAI) Cinderella was trained and validated with the digital raw data of a flow cytometric "8-color" AML-MRD antibody panel in 126 BM and 23 PB samples from 35 patients. Cinderella predicted PB dilution with high accordance compared to the results of the Holdrinet formula (Pearson's correlation coefficient r = 0.94, R = 0.89, p < 0.001). Unlike conventional neuronal networks Cinderella calculated the distributions of 12 different cell populations that were assigned to true hematopoietic counterparts as a human in the loop (HIL) approach. Besides characteristic BM cells such as myelocytes and myeloid progenitor cells the XAI identified discriminating populations, which were not specific for BM or PB (e.g., T cell/NK cell subpopulations and CD45 negative cells) and considered their frequency differences. Thus, Cinderella represents a HIL-XAI algorithm capable to calculate the degree of hemodilution in BM samples with an AML MRD immunophenotype panel. It is explicable, transparent, and paves a simple way to prevent false negative MRD reports.
微小残留病（MRD）检测是急性髓系白血病（AML）生存和复发的有力预测指标。MRD可通过分子评估策略或多参数流式细胞术来确定。随着抽吸量的增加，外周血（PB）对骨髓（BM）的稀释程度会增加，从而导致对残留AML原始细胞数量的连续低估。为了防止MRD结果出现假阴性，我们开发了“灰姑娘”（Cinderella）方法，这是一种简单的自动化方法，可在一管中同时测量BM样本中的血液稀释度和MRD水平。利用来自35例患者的126份BM样本和23份PB样本中流式细胞术“8色”AML-MRD抗体组合的数字原始数据，对可解释人工智能（XAI）“灰姑娘”方法进行了训练和验证。与Holdrinet公式的结果相比，“灰姑娘”方法对PB稀释度的预测具有高度一致性（皮尔逊相关系数r = 0.94，R = 0.89，p < 0.001）。与传统神经网络不同，“灰姑娘”方法计算了12种不同细胞群的分布，这些细胞群被指定为真实造血对应物，采用了人工介入（HIL）方法。除了特征性的BM细胞，如髓细胞和髓系祖细胞外，XAI还识别出了具有鉴别意义的细胞群，这些细胞群对BM或PB不具有特异性（例如，T细胞/NK细胞亚群和CD45阴性细胞），并考虑了它们的频率差异。因此，“灰姑娘”方法代表了一种HIL-XAI算法，能够通过AML MRD免疫表型组合计算BM样本中的血液稀释度。它是可解释的、透明的，为防止MRD报告出现假阴性提供了一种简单的方法。
