Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents. A strong prognostic factor in ALL is given by the Minimal Residual Disease (MRD), which is a measure for the number of leukemic cells persistent in a patient. Manual MRD assessment from Multiparameter Flow Cytometry (FCM) data after treatment is time-consuming and subjective. In this work, we present an automated method to compute the MRD value directly from FCM data. We present a novel neural network approach based on the transformer architecture that learns to directly identify blast cells in a sample. We train our method in a supervised manner and evaluate it on publicly available ALL FCM data from three different clinical centers. Our method reaches a median f1 score of ~0.93 when tested on 200 B-ALL samples.

翻译：急性淋巴素性白血病(ALL)是儿童和青少年最常见的血解恶性恶性肿瘤。最小残留疾病(MRD)给出了全方位的强烈预测因素,这是衡量病人中耐久性白细胞数量的尺度。多参数流体测量数据(FCM)在治疗后进行的人工MRD评估既费时又主观。在这项工作中,我们提出了一个自动方法,直接根据FCM数据计算MRD值。我们提出了一个基于变压器结构的新型神经网络方法,该变压器结构学习在样本中直接识别爆裂细胞。我们以监督的方式培训我们的方法,并根据三个不同临床中心公开提供的全氯氟化碳数据进行评估。我们的方法在对200个B-AL样本进行测试时达到每秒0.93的中位f1分。