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预测接受各种治疗的小细胞肺癌患者的生存率:一种机器学习方法

Ziran Zhao¹, Xi Cheng², Yibo Gao¹

¹Thoracic Surgery Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Translational lung cancer research

|April 18, 2025

概括

这项研究评估了治疗对小细胞肺癌 (SCLC) 存活率的影响,发现手术对早期阶段有益,放射治疗对第三阶段有益. 机器学习模型确定了关键的生存预测因素.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

小细胞肺癌 (SCLC) 是一种高度转移的疾病,具有显著的全球死亡率.
由于缺乏既定的标准护理,需要研究有效的治疗策略.
准确的生存预测对于管理SCLC患者至关重要.

研究的目的:

评估不同治疗方式对SCLC患者在不同阶段的存活率的影响.
开发和验证基于机器学习的工具,用于预测SCLC的整体存活率 (OS).
确定SCLC患者生存的关键预测因素.

主要方法:

开发了四种生存预测模型:Cox比例危险 (Cox PH) 回归,生存树 (ST),随机生存森林 (RSF) 和梯度增强生存分析 (GBSA).
患者数据分为训练 (70%) 和测试 (30%) 组,具有10倍的交叉验证和50次代模型训练.
模型性能使用Harrell的C指数 (C指数) 和Brier分数 (BS) 进行评估,并进行内部验证.

主要成果:

与其他基于平均C指数和Brier分数的模型相比,Cox PH回归显示出更高的性能.
多变量分析确定了远程转移,瘤阶段和治疗方法 (放射治疗,化疗,手术) 作为显著的生存预测因素.
分层分析表明,手术在早期SCLC (第二阶段) 中有好处,放射治疗在第三阶段的疗效,化疗在各阶段都显示出一致的好处.

结论:

治疗SCLC的决定,包括手术,化疗和放射治疗,应根据特定的癌症阶段和患者特征进行量身定制.
手术显示为早期SCLC的治疗选择有前途.
需要进一步的研究来优化晚期SCLC的治疗策略.

关键词:

小细胞肺癌 (SCLC) 是一种癌症.机器学习是机器学习.幸存率幸存率生存率

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