Gurumurthy Gerard, Gurumurthy Juditha, Gurumurthy Samantha
School of Medicine, University of Manchester, Manchester, UK.
School of Cancer and Pharmaceutical Sciences, King's College London, London, UK.
Pediatr Res. 2025 Feb;97(2):524-531. doi: 10.1038/s41390-024-03494-9. Epub 2024 Aug 31.
Machine Learning (ML) has demonstrated potential in enhancing care in adult oncology. However, its application in paediatric haematological malignancies is still emerging, necessitating a comprehensive review of its capabilities and limitations in this area.
A literature search was conducted through Ovid. Studies included focused on ML models in paediatric patients with haematological malignancies. Studies were categorised into thematic groups for analysis.
Twenty studies, primarily on leukaemia, were included in this review. Studies were organised into thematic categories such as prognoses, treatment responses and toxicity predictions. Prognostic studies showed AUC scores between 0.685 and 0.929, indicating moderate-high predictive accuracy. Treatment response studies demonstrated AUC scores between 0.840 and 0.875, reflecting moderate accuracy. Toxicity prediction studies reported high accuracy with AUC scores from 0.870 to 0.927. Only five studies (25%) performed external validation. Significant heterogeneity was noted in ML tasks, reporting formats, and effect measures across studies, highlighting a lack of standardised reporting and challenges in data comparability.
The clinical applicability of these ML models remains limited by the lack of external validation and methodological heterogeneity. Addressing these challenges through standardised reporting and rigorous external validation is needed to translate ML from a promising research tool into a reliable clinical practice component.
Key message: Machine Learning (ML) significantly enhances predictive models in paediatric haematological cancers, offering new avenues for personalised treatment strategies. Future research should focus on developing ML models that can integrate with real-time clinical workflows. Addition to literature: Provides a comprehensive overview of current ML applications and trends. It identifies limitations to its applicability, including the limited diversity in datasets, which may affect the generalisability of ML models across different populations.
Encourages standardisation and external validation in ML studies, aiming to improve patient outcomes through precision medicine in paediatric haematological oncology.
机器学习（ML）已显示出在改善成人肿瘤护理方面的潜力。然而，其在儿科血液恶性肿瘤中的应用仍在不断发展，因此有必要全面审视其在该领域的能力和局限性。
通过Ovid进行文献检索。纳入的研究聚焦于儿科血液恶性肿瘤患者的ML模型。研究被分类为不同主题组进行分析。
本综述纳入了20项主要针对白血病的研究。研究被组织成诸如预后、治疗反应和毒性预测等主题类别。预后研究显示曲线下面积（AUC）分数在0.685至0.929之间，表明预测准确性为中高。治疗反应研究显示AUC分数在0.840至0.875之间，反映出中等准确性。毒性预测研究报告的准确性较高，AUC分数在0.870至0.927之间。只有五项研究（25%）进行了外部验证。在各项研究的ML任务、报告格式和效应测量方面存在显著异质性，凸显出缺乏标准化报告以及数据可比性方面的挑战。
这些ML模型的临床适用性仍受限于缺乏外部验证和方法学异质性。需要通过标准化报告和严格的外部验证来应对这些挑战，以便将ML从一个有前景的研究工具转化为可靠的临床实践组成部分。
关键信息：机器学习（ML）显著增强了儿科血液癌症的预测模型，为个性化治疗策略提供了新途径。未来研究应专注于开发能够与实时临床工作流程整合的ML模型。文献补充：全面概述了当前ML应用及趋势。指出了其适用性的局限性，包括数据集多样性有限，这可能会影响ML模型在不同人群中的通用性。
鼓励ML研究中的标准化和外部验证，旨在通过儿科血液肿瘤学中的精准医学改善患者预后。
