Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands.
Medical School Twente, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands.
Chest. 2023 Mar;163(3):697-706. doi: 10.1016/j.chest.2022.09.042. Epub 2022 Oct 13.
Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies.
This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer?
In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data.
A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86.
Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer.
The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.
尽管呼气挥发性有机化合物分析有可能用于诊断肺癌，但尚未实现临床应用，部分原因是缺乏验证研究。
本研究解决了两个问题。首先，我们能否基于呼气模式同时训练和验证一个预测模型，以区分非小细胞肺癌患者和非肺癌患者？其次，将临床变量添加到呼气数据中是否可以改善肺癌的诊断？
在这项多中心研究中，非小细胞肺癌患者和对照患者通过手持电子鼻设备 aeoNose 进行了 5 分钟的潮式呼吸。使用训练队列来开发基于呼吸数据的预测模型，并使用盲法队列进行验证。进行多变量逻辑回归分析，其中包括呼吸数据和临床变量，应用于验证数据的肺癌概率公式和截断值。
共 376 名患者组成训练集，199 名患者组成验证集。完整的训练模型（包括训练集中的呼气数据和临床参数）在多变量逻辑回归分析中结合在一起，保持肺癌概率的截断值为 16%，得出的结果为：敏感性为 95%，特异性为 51%，阴性预测值为 94%；受试者工作特征曲线下面积为 0.87。验证队列中预测模型的性能表现出相应的结果，敏感性为 95%，特异性为 49%，阴性预测值为 94%，受试者工作特征曲线下面积为 0.86。
在一项多中心、多设备的验证研究中，将呼气数据和临床变量相结合，可以以非侵入性的方式充分区分肺癌患者和非肺癌患者。这项研究为在诊断肺癌的日常实践中实施呼气分析铺平了道路。
荷兰试验注册处；编号：NL7025；网址：https://trialregister.nl/trial/7025。
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