基于尿液挥发性有机化合物 (VOC),通过高维分类来诊断前列腺癌
George Ekow Quaye1, Wen-Yee Lee2, Elizabeth Noriega Landa2
1Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX, USA.
Journal of applied statistics
|December 4, 2024
概括
尿液中的挥发性有机化合物 (VOC) 在早期前列腺癌 (PCa) 检测方面表现有前途. 使用VOC的规范化后勤回归模型实现了0.748的AUC,超过了PSA测试.
科学领域:
- 在瘤学瘤学.
- 生物标志物发现发现
- 计算生物学 计算生物学
背景情况:
- 早期发现前列腺癌 (PCa) 对于治疗成功至关重要.
- 目前的方法如PSA测试和DRE具有诊断局限性.
- 尿液中的挥发性有机化合物 (VOC) 正在成为潜在的PCa生物标志物.
研究的目的:
- 开发和评估使用尿路VOCs的前列腺癌诊断模型.
- 为应对众多挥发性有机化合物带来的高维分类挑战.
- 将基于VOC的模型的诊断性能与PSA测试等传统方法进行比较.
主要方法:
- 从337名PCa阳性和233名PCa阴性患者收集了尿样.
- 采用高维分类技术来分析VOC数据.
- 利用规范后勤回归,特别是SCAD惩罚,用于模型开发.
主要成果:
- 规范后勤回归模型与SCAD惩罚显示了强的性能.
- 最终基于VOC的模型实现了ROC曲线下的面积 (AUC) 为0.748.
- 这显著超过了从PSA测试中获得的0.540的AUC.
结论:
- 尿液中的VOCs代表了前列腺癌查的一个有希望的,非侵入性的生物标志物.
- 调整后勤回归,特别是SCAD惩罚,对于分析高维的VOC数据是有效的.
- 与当前标准相比,基于VOC的诊断为PCa检测提供了一个潜在的更准确和临床可行的方法.
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