呼气电子鼻分析肺癌检测:一项多中心前瞻性外部验证研究。

IF 56.7 1区 医学 Q1 ONCOLOGY
A I G Buma, M B Muntinghe-Wagenaar, V van der Noort, R de Vries, M M F Schuurbiers, P J Sterk, S P M Schipper, J Meurs, S M Cristescu, T J N Hiltermann, M M van den Heuvel
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引用次数: 0

摘要

背景:电子鼻(eNose)呼气分析显示出准确和及时诊断肺癌的潜力,但缺乏前瞻性的外部验证研究。我们的研究主要旨在前瞻性和外部验证已发表的用于COPD患者肺癌检测的eNose模型,并评估其与专门针对目标人群的新eNose模型在更普通门诊人群中的诊断性能。患者和方法:这项多中心前瞻性外部验证研究纳入了临床和/或放射学上怀疑肺癌的成年人,他们从荷兰两个地点的胸部肿瘤门诊诊所招募。使用云连接的eNose (SpiroNose®)收集呼吸资料。基于ROC-AUC、特异性、阳性预测值(PPV)和阴性预测值(NPV),对原始和新的eNose模型的诊断性能进行了评估,目标灵敏度为95%。对于新的eNose模型,使用了一个训练和验证队列。结果:在2019年3月至2023年11月期间,纳入了364名参与者。原始eNose模型在COPD患者中检测到肺癌的ROC-AUC为0.92 (95% CI: 0.85-0.99) (n=98/116;84%)和0.80 (95% CI: 0.75-0.85) (n=216/364;59%)。在95%的敏感性下,特异性、PPV和NPV分别为72%和51%,95%和74%,72%和88%。在验证队列中,新的eNose模型在所有参与者中识别出肺癌(n=72/121;60%), ROC-AUC为0.83 (95% CI: 0.75-0.91),敏感性94%,特异性63%,PPV为79%,NPV为89%。值得注意的是,准确的检测在肿瘤特征、疾病分期、诊断中心和临床特征之间是一致的。结论:本多中心前瞻性外部验证研究证实,呼气呼气分析能够在胸科肿瘤科门诊准确检测肺癌,而不受肿瘤特征、疾病分期、诊断中心和临床特征的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lung cancer detection by electronic nose analysis of exhaled breath: a multicentre prospective external validation study.

Background: Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in chronic obstructive pulmonary disease (COPD) patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.

Patients and methods: This multicentre prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in the Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose models was assessed in various population subsets based on receiver operating characteristic-area under the curve (ROC-AUC), specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training cohort and a validation cohort were used.

Results: Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with an ROC-AUC of 0.92 [95% confidence interval (CI) 0.85-0.99] in COPD patients (n = 98/116; 84%) and 0.80 (95% CI 0.75-0.85) in all participants (n = 216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n = 72/121; 60%) with an ROC-AUC of 0.83 (95% CI 0.75-0.91), sensitivity of 94%, specificity of 63%, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centres, and clinical characteristics.

Conclusion: This multicentre prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic centre, and clinical characteristics.

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来源期刊
Annals of Oncology
Annals of Oncology 医学-肿瘤学
CiteScore
63.90
自引率
1.00%
发文量
3712
审稿时长
2-3 weeks
期刊介绍: Annals of Oncology, the official journal of the European Society for Medical Oncology and the Japanese Society of Medical Oncology, offers rapid and efficient peer-reviewed publications on innovative cancer treatments and translational research in oncology and precision medicine. The journal primarily focuses on areas such as systemic anticancer therapy, with a specific emphasis on molecular targeted agents and new immune therapies. We also welcome randomized trials, including negative results, as well as top-level guidelines. Additionally, we encourage submissions in emerging fields that are crucial to personalized medicine, such as molecular pathology, bioinformatics, modern statistics, and biotechnologies. Manuscripts related to radiotherapy, surgery, and pediatrics will be considered if they demonstrate a clear interaction with any of the aforementioned fields or if they present groundbreaking findings. Our international editorial board comprises renowned experts who are leaders in their respective fields. Through Annals of Oncology, we strive to provide the most effective communication on the dynamic and ever-evolving global oncology landscape.
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