人口分布在现实世界和虚拟幻象人口之间匹配
Dhrubajyoti Ghosh1, Fakrul Tushar2, Lavsen Dahal2
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.
Medical physics
|February 26, 2026
概括
新的框架DISTINCT将虚拟和真实患者数据对齐,用于成像试验. 这确保了在不同人群中进行准确的性能评估,提高了虚拟成像试验的可靠性.
科学领域:
- 医疗成像医学成像
- 生物统计学 生物统计学
- 临床试验 临床试验
背景情况:
- 虚拟成像试验 (VIT) 为传统的临床试验提供了经济有效的替代方案.
- 虚拟群体和真实群体之间的人口差异可能会对成像性能评估产生偏见.
- 未解决的偏见限制了VIT研究结果对不同患者群体的翻译相关性.
研究的目的:
- 介绍DISTINCT (共变目标对齐的分布式子样本),这是一个统计框架,用于将临床数据集中的人口学子样本与虚拟队列对齐.
- 实现虚拟成像试验群体与现实世界临床数据之间的强大比较.
主要方法:
- 在国家肺部查试验 (NLST) 和虚拟数据集 (VLST) 中应用了DISTINCT.
- 通过使用多维数据库,共同对准连续 (年龄,BMI) 和分类 (性别,种族,种族) 变量.
- 使用瓦瑟斯坦和科尔摩戈罗夫-斯米尔诺夫距离评估人口相似性.
- 在对齐的子样本上使用ROC分析评估肺癌风险预测性能和稳定性.
主要成果:
- DISTINCT确定了9974名与VLST人群相匹配的最大人口统计学一致的NLST子样本.
- 肺癌风险评分的曲线下面积 (AUC) 估计稳定在6000名参与者左右.
- 分层分析突出了人口特异性的AUC变化,强调了对共变量调整的需要.
结论:
- DISTINCT提供了一种统计严格且可扩展的方法,用于在真实和虚拟成像队列之间对准共变量.
- 该框架适用于各种成像模式,疾病和变化因素.
- DISTINCT促进了公平和代表性的绩效评估,促进了VIT在研究和协议优化中的整合.
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