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多发性骨髓瘤的POD24的预后价值:基于传统统计和机器学习的综合分析

Quane Zhang^1,2, Yifan Wang^1,2, Qiuni Chen^1,2

¹Department of Hematology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.

|October 28, 2025

概括

在24个月内进展 (POD24) 显著影响多发性骨髓瘤生存率. 机器学习,特别是使用SHAP分析的ANN,将POD24确定为死亡率的关键预测因素,证实了其预后价值.

科学领域:

血液学血液学血液学
机器学习在医学中的应用
癌症预后癌症预后

背景情况:

24个月内的进展 (POD24) 是多发性骨髓瘤的关键不良预后因素.
POD24对整体存活时间 (OS) 的预后影响需要使用先进的分析方法进行进一步的研究.
机器学习方法为复杂的生存数据提供了新的见解.

研究的目的:

评估POD24与多发性骨髓瘤患者的整体存活率 (OS) 之间的关联.
应用机器学习算法来增强死亡风险预测.
确定多发性骨髓瘤生存结果的关键预测因素.

主要方法:

对155名多发性骨髓瘤患者的临床数据进行了回顾性分析.
使用卡普兰-梅尔曲线和考克斯回归方法,比较POD24和非POD24组之间的生存结果.
评估十个机器学习算法,包括人工神经网络 (ANN),用于生存预测.
使用SHAP (夏普利添加式解释) 分析来分析模型的可解释性.

主要成果:

经历POD24的患者的生存状况明显较差 (p < 0.001).
人工神经网络 (ANN) 在经过测试的机器学习模型中表现出最高的预测性能.
在SHAP分析中,POD24被确定为该队列中死亡率最有影响的预测因素.
强力图表明,非POD24状态与预测的死亡风险降低有关.

结论:

POD24是与多发性骨髓瘤生存结果相关的重要因素,通过传统和机器学习方法验证.
机器学习模型,特别是ANN,在预测多发性骨髓瘤死亡风险方面表现有前途.
该研究强调POD24对死亡风险分层的有用性,并强调ANN-SHAP对透明模型解释的价值.

关键词:

人工神经网络的人工神经网络机器学习是机器学习.多发性骨髓瘤是一种多发性骨髓瘤.总体生存率总体生存率 POD24 POD24 在线播放这就是 SHAP SHAP 的意思.

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