Artificial Intelligence Reveals the Predictions of Hematological Indexes in Children with Acute Leukemia

Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by examining nutritional markers, key leukemia indicators, and granulocytes in patients' blood. Using a machine learning algorithm and ten indices, 826 pediatric patients with ALL and 255 children with AML were analyzed, comparing them with a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an Area Under the Curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.儿童白血病是一类高发的儿童恶性肿瘤，其中急性淋巴细胞白血病（acute lymphoblastic leukemia, ALL）与急性髓系白血病（acute myeloid leukemia, AML）为其主要病理类型。及时治疗已显著提升急性白血病患儿的总体生存率。本研究旨在通过检测患儿血液中的营养标志物、核心白血病相关指标及粒细胞，开发一款针对儿童血液系统恶性肿瘤的早期综合预测模型。本研究采用机器学习算法与十项评估指标，对826例急性淋巴细胞白血病患儿、255例急性髓系白血病患儿进行分析，并与200例健康儿童组成的对照组开展对照研究。研究结果显示存在显著组间差异：男孩的相关指标水平高于女孩，且白血病患儿与健康儿童的多数生化指标存在显著统计学差异。本研究构建的随机森林模型，对白血病亚型的预测曲线下面积（Area Under the Curve, AUC）可达0.950，对急性髓系白血病的预测AUC可达0.909。本研究为儿童血液恶性肿瘤的早期筛查提供了一款高效诊断工具，同时凸显了人工智能在现代医疗领域的应用潜力。