PDF(Pubmed)
Abstract：
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 analyzing nutritional biomarkers, key leukemia indicators, and granulocytes in their blood. Using a machine learning algorithm and ten indices, the blood samples of 826 children with ALL and 255 children with AML were compared to 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.
摘要：
儿童白血病是儿科癌症的一种常见形式，急性淋巴细胞白血病（ALL）和急性髓细胞性白血病（AML）是主要表现。及时治疗可显著提高急性白血病患儿的生存率。这项研究旨在通过分析营养生物标志物来开发儿童血液系统恶性肿瘤的早期和综合预测因子。白血病的关键指标，血液中的粒细胞.使用机器学习算法和十个索引，将826例ALL儿童和255例AML儿童的血液样本与200例健康儿童的对照组进行比较.这项研究揭示了显著的差异，包括与女孩相比，男孩的指标更高，白血病患者和健康儿童之间大多数生化指标的显着差异。采用随机森林模型导致用于预测白血病亚型的曲线下面积(AUC)为0.950,用于预测AML的AUC为0.909。这项研究为儿童血癌的早期筛查提供了一种有效的诊断工具，并强调了人工智能在现代医疗保健中的潜力。
