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Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

机译:其他肺病和健康人呼吸呼吸呼吸呼吸挥发性有机化合物的目标分析

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The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene ( p -values 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.
机译:本研究的目的是探讨呼吸分析能力,以区分肺癌(LC)患者与其他呼吸系统疾病和健康人的患者。人口样品由51例确诊的LC,38例病理计算断层扫描患者(CT)发现未被诊断为LC,53例健康对照组成。通过VOC的固相微萃取(SPME)和随后的气相色谱 - 质谱(GC-MS)分析来定量19次挥发性有机化合物(VOC)的浓度在研究参与者的呼出气孔中定量。 Kruskal-Wallis和Mann-Whitney测试用于识别亚组之间的显着差异。机器学习方法用于确定该方法的判别力。在LC患者和健康对照中发现了几种化合物在显着不同。鉴定出强烈的缔组织2-丙醇,1-丙醇,甲苯,乙苯和苯乙烯(P-Values <0.001-0.006)。当环境空气浓度从呼吸浓度减去环境空气浓度时,这些关联仍然显着。发现VOC水平受到环境空气浓度的影响和含有吸烟状态的影响。随机森林机器学习算法达到了88.5%患者的正确分类(曲线0.94下的面积)。然而,没有使用的方法在LC患者和异常计算机断层扫描(CT)调查结果中取得了足够的歧视。生物标志物套,主要由外源单芳族化合物和1-和2-丙醇,充分区分的LC患者免受健康对照。这些化合物的呼吸浓度可能反映患者生理和生化状态的改变,并且可以用作调查这些状态或呼吸分析中患者相关因素的正常化探讨。

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