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对中国骨髓瘤独特化疗方案的疗效分析和生存预测

Ruizhen Wang^1,2, Zhen Bao¹, Fengrong Chen³

¹Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China.

Frontiers in physiology

|March 2, 2026

概括

一种新的化疗疗法改善了中国骨髓瘤患者的生存率. 机器学习模型,特别是随机森林和额外树木,可以准确地预测个性化治疗计划的整体生存率.

科学领域:

整形外科整形外科整形外科
在瘤学瘤学.
医疗信息学医疗信息学

背景情况:

骨髓瘤是一种主要的骨恶性瘤,对年轻患者产生重大影响.
标准治疗包括新辅助化疗和手术,但结果各不相同.
预测整体生存率 (OS) 仍然具有挑战性,需要先进的分析工具.

研究的目的:

评估一种特定的新辅助化疗方案在中国骨髓瘤患者中的有效性.
确定影响整体生存时间 (OS) 的关键因素.
为了比较6个机器学习 (ML) 模型对OS的预测性能.

主要方法:

对接受标准化新辅助化疗和手术的390名骨肉瘤患者 (2009-2019) 的回顾性分析.
使用卡普兰-梅尔曲线,日志等级测试和考克斯比例危险模型进行生存分析.
评估六个ML模型 (RF,AdaBoost,CatBoost,Extra Trees,XGBoost,LightGBM) 的五倍交叉验证和决策曲线分析 (DCA).

主要成果:

队列 (平均年龄19岁) 的3年和5年生存率分别为76.00%和65.00%.
瘤类型,手术方法,复发,死亡率和血清生物标志物 (LDH,ALP,PLT,WBC,RBC) 显著影响了OS.
随机森林 (RF) 和额外树木实现了最高的预测准确性 (AUC=0.960),优于其他ML模型,DCA显示的临床净效益优越.

结论:

评估的化疗方案显示,在骨髓瘤中,存活率更高.
预后评估应纳入各种临床和病理指标.
机器学习模型,特别是RF和ExtraTrees,为骨髓瘤的个性化生存预测和治疗策略提供了强大的能力.

关键词:

以数据为中心的AI.有效性有效性.骨质肉瘤是骨质肉瘤的一种疾病.现实世界的数据数据.生存预测的预测.

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