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通过呼气诊断肺癌:一项全面的研究
Elina Gashimova1, Azamat Temerdashev2, Dmitry Perunov3
1Kuban State University, Stavropol'skaya St. 149, Krasnodar, 350040, Russia. elina.gashimova@yandex.ru.
呼气分析显示肺癌诊断有前途,但吸烟和并发症等非疾病因素显著影响结果. 可靠的模型需要考虑这些变量,以避免错误分类.
科学领域:
- 临床诊断 临床诊断 临床诊断
- 分析化学是一种分析化学.
- 生物标志物 生物标志物
背景情况:
- 呼气分析为肺癌诊断提供了一种非侵入性的方法.
- 准确的诊断模型必须考虑与疾病状况无关的混因素.
研究的目的:
- 评估各种因素对呼气中挥发性有机化合物 (VOC) 概况的影响.
- 开发和评估使用VOCs的肺癌诊断模型.
主要方法:
- 气色谱-质谱 (GC-MS) 分析了来自646个人的呼吸样本 (肺癌,其他癌症,非癌症肺病,健康对照).
- 评估的因素包括年龄,性别,吸烟,食物消费,并发症和癌症特异性参数.
- 人工神经网络 (ANN) 和梯度增强决策树 (GBDT) 用于模型创建.
主要成果:
- 吸烟状况和食物消费显著改变了VOC档案.
- 糖尿病和肥胖等并发病影响了特定的VOC水平和比率.
- 诊断模型实现了不同的灵敏度和特异性,健康个体和患者的联合分类达到76.6%的灵敏度和68.2%的特异性.
结论:
- 呼出的气息中的VOC概况受到与疾病无关的因素的影响.
- 肺癌的诊断模型必须整合这些混变量以提高准确性.
- 当前的模型可能会将其他疾病错误地归类为肺癌,如果不考虑混因素.
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