基于DeepSurv模型的食道癌症预测模型的开发和验证:一个队列研究
Zhenzhen Xu1, Chengzhen Xu2, Fangzhen Ge2
1Department of Anesthesiology, Peking University First Hospital, Beijing, China.
Science progress
|September 17, 2025
概括
这项研究开发了一个DeepSurv机器学习模型来预测食道癌生存率. 该模型提供个性化的治疗建议,改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 机器学习模型越来越多地用于生存分析.
- 由于数据异质性,现有的模型往往具有低于最佳的预测性能.
- 预测食道癌存活率需要准确的模型来进行个性化治疗.
研究的目的:
- 开发一种机器学习模型,用于预测食道癌患者的生存率.
- 为个性化食道癌治疗策略提供数据驱动的建议.
- 评估 DeepSurv 模型在食道癌存活率分析中的预测性能.
主要方法:
- 一项回顾性队列研究,使用来自SEER数据库的5276名食道癌患者.
- 数据分为70%的培训和30%的测试集.
- 使用DeepSurv算法开发了一个生存分析模型.
- 使用一致性指数 (CI) 评估模型性能.
主要成果:
- DeepSurv模型实现了高预测性能,CI值为0.84 (训练) 和0.83 (测试).
- 种族,诊断年份和婚姻状况被确定为显著的预后风险因素 (p < 0.05).
- 在手术和非手术组之间观察到治疗疗效的显著差异 (p < 0.001).
结论:
- 开发的DeepSurv模型准确地预测了食道癌患者的生存率.
- 该模型为选择个性化治疗方法提供了有价值的见解.
- 机器学习,特别是DeepSurv,在改善食道癌患者护理方面表现有前途.
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