基于机器学习的生存预测模型,用于晚期霍奇金淋巴瘤的无进展和整体生存
Rasmus Rask Kragh Jørgensen1,2, Fanny Bergström3, Sandra Eloranta3
1Department of Hematology, Clinical Cancer Research Centre, Aalborg University Hospital, Aalborg, Denmark.
JCO clinical cancer informatics
|April 12, 2024
概括
一个新的机器学习 (ML) 模型显示,与传统方法相比,在晚期霍奇金淋巴瘤 (aHL) 患者的生存预测得到了改进. 虽然它表现优于国际预测得分 (IPS),但它对A-HIPI预测指数的优势是有限的.
科学领域:
- 血液学 血液学 血液学
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
背景情况:
- 晚期霍奇金淋巴瘤 (aHL) 患者的预后传统上依赖于国际预后得分 (IPS).
- 现有的模型可能无法完全捕捉到预测患者结果的复杂性.
- 需要更准确的预后工具来指导aHL的治疗决策.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测aHL患者的整体生存率 (OS) 和无进展生存率 (PFS).
- 将ML模型的预测性能与IPS和aHL国际预测指数 (A-HIPI) 等已建立的预测得分进行比较.
主要方法:
- 使用堆叠组合方法开发了一个机器学习模型,结合了多个生存预测模型.
- 该模型是使用来自丹麦国家淋巴瘤登记册 (发展队列) 的患者数据进行训练的.
- 内部验证使用嵌套交叉验证,外部验证使用来自瑞典和挪威注册表 (验证队列) 的数据进行.
- 通过将ML模型与IPS-3,IPS-7和A-HIPI进行比较,使用一致性指数 (C指数) 和时间变化的AUC来评估性能.
主要成果:
- 在开发和验证队列中,ML模型显示了与IPS-3和IPS-7相比,OS和PFS的优异预测性能.
- 在发展队列中,ML模型在OS方面实现了比A-HIPI (0.768) 更高的C指数 (0.789).
- 对于PFS,ML模型在两个队列中始终达到最高的C指数 (0.665发展,0.691验证).
- 在诊断后的前5年内,ML模型和A-HIPI的时间变化的AUC始终高于IPS模型.
结论:
- 基于ML的预后模型在预测aHL的结果方面比传统的IPS模型有了显著的改进.
- ML模型的预测性能与A-HIPI预后指数相比只有有限的改善.
- 这种ML方法代表了高级霍奇金淋巴瘤风险分层的有希望的进步.
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