Acute Myeloid Leukemia (AML) is one of the most aggressive types of hematological neoplasm. To support the specialists' decision about the appropriate therapy, patients with AML receive a prognostic of outcomes according to their cytogenetic and molecular characteristics, often divided into three risk categories: favorable, intermediate, and adverse. However, the current risk classification has known problems, such as the heterogeneity between patients of the same risk group and no clear definition of the intermediate risk category. Moreover, as most patients with AML receive an intermediate-risk classification, specialists often demand other tests and analyses, leading to delayed treatment and worsening of the patient's clinical condition. This paper presents the data analysis and an explainable machine-learning model to support the decision about the most appropriate therapy protocol according to the patient's survival prediction. In addition to the prediction model being explainable, the results obtained are promising and indicate that it is possible to use it to support the specialists' decisions safely. Most importantly, the findings offered in this study have the potential to open new avenues of research toward better treatments and prognostic markers.

翻译：急性髓系白血病（AML）是最具侵袭性的血液系统肿瘤之一。为辅助专科医生制定合适的治疗方案，AML患者需根据其细胞遗传学和分子学特征进行预后评估，通常分为三个风险等级：预后良好、预后中等和预后不良。然而，当前的风险分类存在已知缺陷，例如同一风险组患者间存在异质性，且中等风险类别缺乏明确定义。此外，由于大多数AML患者被划分为中等风险，专科医生常需进行额外检测与分析，导致治疗延迟及患者临床状况恶化。本文提出一种数据分析方法与可解释的机器学习模型，通过预测患者生存期来支持最适治疗方案决策。该预测模型不仅具有可解释性，且实验结果令人鼓舞，表明其可安全地辅助专科医生决策。更重要的是，本研究的发现有望为探索更优治疗方案及预后标志物开辟新研究方向。