使用自制的电子鼻装置,通过呼气分析检测肺癌及其分期。

IF 3.9 3区 医学 Q1 PATHOLOGY Expert Review of Molecular Diagnostics Pub Date : 2024-04-01 Epub Date: 2024-02-19 DOI:10.1080/14737159.2024.2316755
Binson V A, Philip Mathew, Sania Thomas, Luke Mathew
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引用次数: 0

摘要

背景:呼吸组学是一个新兴领域,主要用于监测和诊断肺部疾病,尤其是肺癌。本研究旨在利用代谢组学方法在人体排出的空气中建立呼吸指纹,以快速识别肺癌及其分期:研究设计和开发了一个电子鼻(e-nose)系统,其中包含五个金属氧化物半导体(MOS)气体传感器、一个微控制器和机器学习算法。这项研究的志愿者包括 114 名肺癌患者和 147 名健康对照者,目的是了解电子鼻系统检测肺癌及其分期的临床潜力:在训练阶段,XGBoost 分类器模型在区分肺癌和对照组时的准确率为 91.67%。在验证阶段,XGBoost 分类器模型正确识别了 42 个肺癌患者样本中的 35 个样本和 51 个健康对照样本中的 44 个样本,总体灵敏度为 83.33%,特异度为 86.27%:这些结果表明,呼出气体挥发性有机化合物分析方法可作为一种新的肺癌检测诊断工具。基于电子鼻的诊断方法具有取样简便、无痛苦、成本低廉等优点,未来将成为一种很好的诊断方法。
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Detection of lung cancer and stages via breath analysis using a self-made electronic nose device.

Background: Breathomics is an emerging area focusing on monitoring and diagnosing pulmonary diseases, especially lung cancer. This research aims to employ metabolomic methods to create a breathprint in human-expelled air to rapidly identify lung cancer and its stages.

Research design and methods: An electronic nose (e-nose) system with five metal oxide semiconductor (MOS) gas sensors, a microcontroller, and machine learning algorithms was designed and developed for this application. The volunteers in this study include 114 patients with lung cancer and 147 healthy controls to understand the clinical potential of the e-nose system to detect lung cancer and its stages.

Results: In the training phase, in discriminating lung cancer from controls, the XGBoost classifier model with 10-fold cross-validation gave an accuracy of 91.67%. In the validation phase, the XGBoost classifier model correctly identified 35 out of 42 patients with lung cancer samples and 44 out of 51 healthy control samples providing an overall sensitivity of 83.33% and specificity of 86.27%.

Conclusions: These results indicate that the exhaled breath VOC analysis method may be developed as a new diagnostic tool for lung cancer detection. The advantages of e-nose based diagnostics, such as an easy and painless method of sampling, and low-cost procedures, will make it an excellent diagnostic method in the future.

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来源期刊
CiteScore
6.60
自引率
0.00%
发文量
71
审稿时长
1 months
期刊介绍: Expert Review of Molecular Diagnostics (ISSN 1473-7159) publishes expert reviews of the latest advancements in the field of molecular diagnostics including the detection and monitoring of the molecular causes of disease that are being translated into groundbreaking diagnostic and prognostic technologies to be used in the clinical diagnostic setting. Each issue of Expert Review of Molecular Diagnostics contains leading reviews on current and emerging topics relating to molecular diagnostics, subject to a rigorous peer review process; editorials discussing contentious issues in the field; diagnostic profiles featuring independent, expert evaluations of diagnostic tests; meeting reports of recent molecular diagnostics conferences and key paper evaluations featuring assessments of significant, recently published articles from specialists in molecular diagnostic therapy. Expert Review of Molecular Diagnostics provides the forum for reporting the critical advances being made in this ever-expanding field, as well as the major challenges ahead in their clinical implementation. The journal delivers this information in concise, at-a-glance article formats: invaluable to a time-constrained community.
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