呼吸组学:它能否成为非小细胞肺癌早期诊断的经济实惠的新工具?一项针对 60 名患者的探索性研究。

0 CARDIAC & CARDIOVASCULAR SYSTEMS
Debora Brascia, Giulia De Iaco, Teodora Panza, Francesca Signore, Graziana Carleo, Wenzhe Zang, Ruchi Sharma, Pamela Riahi, Jared Scott, Xudong Fan, Giuseppe Marulli
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引用次数: 0

摘要

目的:呼吸分析,特别是挥发性有机化合物 (VOC) 的模式分析,已显示出区分肺癌 (LC) 患者和健康人 (HC) 的潜力。然而,目前的技术依赖于复杂、昂贵和低通量的分析平台,这些平台提供离线响应,因此不适合大规模筛查。目前已开发出一种新型便携式设备,可实现快速和现场 LC 诊断,其可靠性正在接受测试:方法:从组织学证实的非小细胞肺癌(NSCLC)患者和健康对照者处收集呼吸样本,使用 Tedlar 袋和连接到单向吹口的 Nafion 过滤器。然后使用自主研发的微型便携式气相色谱仪对这些样本进行分析。该装置由热解吸管、热注入器、分离柱、光离子化检测器以及泵、阀和氦气筒等其他配件组成。利用化学计量学和机器学习技术对所得色谱图进行分析:30 名 NSCLC 患者和 30 名 HC 患者参加了研究。在训练集(20 名 NSCLC 患者和 20 名 HC 患者)和测试集(10 名 NSCLC 患者和 10 名 HC 患者)之后,发现基于三种 VOCs 识别 NSCLC 患者的总体特异性为 83.3%,灵敏度为 86.7%,准确率为 85.0%:这些结果是为现场肺癌快速筛查创造一种低成本、用户友好且易于使用的工具而迈出的重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breathomics: may it become an affordable, new tool for early diagnosis of non-small-cell lung cancer? An exploratory study on a cohort of 60 patients.

Objectives: Analysis of breath, specifically the patterns of volatile organic compounds (VOCs), has shown the potential to distinguish between patients with lung cancer (LC) and healthy individuals (HC). However, the current technology relies on complex, expensive and low throughput analytical platforms, which provide an offline response, making it unsuitable for mass screening. A new portable device has been developed to enable fast and on-site LC diagnosis, and its reliability is being tested.

Methods: Breath samples were collected from patients with histologically proven non-small-cell lung cancer (NSCLC) and healthy controls using Tedlar bags and a Nafion filter attached to a one-way mouthpiece. These samples were then analysed using an automated micro portable gas chromatography device that was developed in-house. The device consisted of a thermal desorption tube, thermal injector, separation column, photoionization detector, as well as other accessories such as pumps, valves and a helium cartridge. The resulting chromatograms were analysed using both chemometrics and machine learning techniques.

Results: Thirty NSCLC patients and 30 HC entered the study. After a training set (20 NSCLC and 20 HC) and a testing set (10 NSCLC and 10 HC), an overall specificity of 83.3%, a sensitivity of 86.7% and an accuracy of 85.0% to identify NSCLC patients were found based on 3 VOCs.

Conclusions: These results are a significant step towards creating a low-cost, user-friendly and accessible tool for rapid on-site LC screening.

Clinical registration number: ClinicalTrials.gov Identifier: NCT06034730.

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