石棉
医学
无症状的
间皮瘤
肺癌
逻辑回归
内科学
气体分析呼吸
接收机工作特性
胃肠病学
病理
解剖
冶金
材料科学
作者
Kevin Lamote,Matthijs Vynck,Olivier Thas,Joris Van Cleemput,Kristiaan Nackaerts,Jan P. van Meerbeeck
出处
期刊:The European respiratory journal
[European Respiratory Society]
日期:2017-12-01
卷期号:50 (6): 1700919-1700919
被引量:48
标识
DOI:10.1183/13993003.00919-2017
摘要
Malignant pleural mesothelioma (MPM) is predominantly caused by asbestos exposure and has a poor prognosis. Breath contains volatile organic compounds (VOCs) and can be explored as an early detection tool. Previously, we used multicapillary column/ion mobility spectrometry (MCC/IMS) to discriminate between patients with MPM and asymptomatic high-risk persons with a high rate of accuracy. Here, we aim to validate these findings in different control groups.Breath and background samples were obtained from 52 patients with MPM, 52 healthy controls without asbestos exposure (HC), 59 asymptomatic former asbestos workers (AEx), 41 patients with benign asbestos-related diseases (ARD), 70 patients with benign non-asbestos-related lung diseases (BLD) and 56 patients with lung cancer (LC).After background correction, logistic lasso regression and receiver operating characteristic (ROC) analysis, the MPM group was discriminated from the HC, AEx, ARD, BLD and LC groups with 65%, 88%, 82%, 80% and 72% accuracy, respectively. Combining AEx and ARD patients resulted in 94% sensitivity and 96% negative predictive value (NPV). The most important VOCs selected were P1, P3, P7, P9, P21 and P26.We discriminated MPM patients from at-risk subjects with great accuracy. The high sensitivity and NPV allow breath analysis to be used as a screening tool for ruling out MPM.
科研通智能强力驱动
Strongly Powered by AbleSci AI