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应用呼出气气相色谱-质谱分析研究混杂因素对肺癌诊断效果的影响。

Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath.

机构信息

Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia.

Research Institute - Regional Clinical Hospital No. 1 named after Prof. SV Ochapovsky, Krasnodar, Russia.

出版信息

Biomark Med. 2021 Aug;15(11):821-829. doi: 10.2217/bmm-2020-0828. Epub 2021 Jul 5.

Abstract

The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.

摘要

本研究旨在评估挥发性有机化合物(VOCs)区分肺癌患者和健康志愿者呼气样本的能力,并评估吸烟状况和性别对参数的影响。使用气相色谱-质谱联用仪分析了 40 例肺癌患者和 40 例健康个体的呼气样本。研究了其他因素对呼气 VOCs 谱的影响。一些与疾病状态相关的参数受其他因素的影响。排除这些参数可以创建一个具有 83%敏感性和 81%特异性的逻辑回归诊断模型。在呼气 VOCs 谱中,必须考虑其他因素的影响,以避免产生误导性结果。

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