评估患者报告的基于结果的生存建模的尺寸性减少在患有头癌的患者中
Eric Ababio Anyimadu1, Yaohua Wang1, Amy C Moreno2
1The University of Iowa, Iowa City, IA.
JCO clinical cancer informatics
|October 15, 2025
概括
减小尺寸技术通过整合患者报告的结果 (PROs) 来增强头癌生存模型. 这提高了整体存活 (OS) 和无进展存活 (PFS) 的预测.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 患者报告的结局 (PROs) 提供了关键的洞察力,了解头癌 (HNC) 治疗期间的症状严重程度.
- PRO数据的高维度带来了诸如过拟合和生存建模中的计算复杂性等挑战.
- 整合PRO可以提高HNC患者的个性化护理和治疗策略.
研究的目的:
- 通过整合患者报告的结果 (PROs) 来改善头癌 (HNC) 的生存建模.
- 探索缩小维度技术的实用性,用于将PRO数据转化并纳入生存模型.
- 提高生存模型的预测性能,以提高HNC的总生存率 (OS) 和无进展生存率 (PFS).
主要方法:
- 追溯分析了923名HNC患者的临床和PRO数据.
- 应用减小维度技术:主要组件分析 (PCA),自动编码器 (AEs) 和患者集群.
- 将减少的PRO表示与临床数据集成到Cox比例危险模型中,以预测OS和PFS.
主要成果:
- 结合PCA和AE的Cox模型显示,OS和PFS单独的临床数据的性能优于OS和PFS.
- 基于PCA的模型实现了最高的一致性指数 (C指数),OS为0.74,PFS为0.64.
- 时间依赖的AUC和Brier分数证实了使用缩小维度的模型的增强预测准确性和校准.
结论:
- 缩小尺寸的技术有效地整合了PRO数据,以改善HNC的生存预测.
- 这些方法提供了更多个性化治疗策略的潜力,通过对患者的结果提供更深入的见解.
- 这些发现强调了先进数据处理对优化癌症生存模型的价值.
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