Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study.

BACKGROUND: 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. RESEARCH QUESTION: 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? STUDY DESIGN AND METHODS: 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. RESULTS: 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. INTERPRETATION: 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. CLINICAL TRIAL REGISTRATION: The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.

Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters.

INTRODUCTION: Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis. METHODS: Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis. RESULTS: NSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69-0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81-0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79-0.89). CONCLUSIONS: Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer.

Data analysis of electronic nose technology in lung cancer: generating prediction models by means of Aethena.

INTRODUCTION: Only 15% of lung cancer cases present with potentially curable disease. Therefore, there is much interest in a fast, non-invasive tool to detect lung cancer earlier. Exhaled breath analysis using electronic nose technology measures volatile organic compounds (VOCs) in exhaled breath that are associated with lung cancer. METHODS: The diagnostic accuracy of the Aeonose™ is currently being studied in a multi-centre, prospective study in 210 subjects suspected for lung cancer, where approximately half will have a confirmed diagnosis and the other half will have a rejected diagnosis of lung cancer. We will also include 100-150 healthy control subjects. The eNose Company (provider of the Aeonose™) uses a software program, called Aethena, comprising pre-processing, data compression and neural networks to handle big data analyses. Each individual exhaled breath measurement comprises a data matrix with thousands of conductivity values. This is followed by data compression using a Tucker3-like algorithm, resulting in a vector. Subsequently, model selection takes place after entering vectors with different presets in an artificial neural network to train and evaluate the results. Next, a 'judge model' is formed, which is a combination of models for optimizing performance. Finally, two types of cross-validation, being 'leave-10%-out' cross-validation and 'bagging', are used when recalculating the judge models. These judge models are subsequently used to classify new, blind measurements. DISCUSSION: Data analysis in eNose technology is principally based on generating prediction models that need to be validated internally and externally for eventual use in clinical practice. This paper describes the analysis of big data, captured by eNose technology in lung cancer. This is done by means of generating prediction models with Aethena, a data analysis program specifically developed for analysing VOC data.

Quality of life and adherence to inhaled corticosteroids and tiotropium in COPD are related.

BACKGROUND: Poor adherence to inhaled medications in COPD patients seems to be associated with an increased risk of death and hospitalization. Knowing the determinants of nonadherence to inhaled medications is important for creating interventions to improve adherence. OBJECTIVES: To identify disease-specific and health-related quality of life (HRQoL) factors, associated with adherence to inhaled corticosteroids (ICS) and tiotropium in COPD patients. METHODS: Adherence of 795 patients was recorded over 3 years and was deemed optimal at >75%-≤125%, suboptimal at ≥50%-<75%, and poor at <50% (underuse) or >125% (overuse). Health-related quality of life was measured with the Clinical COPD Questionnaire and the EuroQol-5D questionnaire. RESULTS: Patients with a higher forced expiratory volume in 1 second (FEV1)/vital capacity (VC) (odds ratio [OR] =1.03) and ≥1 hospitalizations in the year prior to inclusion in this study (OR =2.67) had an increased risk of suboptimal adherence to ICS instead of optimal adherence. An increased risk of underuse was predicted by a higher FEV1/VC (OR =1.05). Predictors for the risk of overuse were a lower FEV1 (OR =0.49), higher scores on Clinical COPD Questionnaire-question 3 (anxiety for dyspnea) (OR =1.26), and current smoking (OR =1.73). Regarding tiotropium, predictors for suboptimal use were a higher FEV1/VC (OR =1.03) and the inability to perform usual activities as asked by the EuroQol-5D questionnaire (OR =3.09). A higher FEV1/VC also was a predictor for the risk of underuse compared to optimal adherence (OR =1.03). The risk of overuse increased again with higher scores on Clinical COPD Questionnaire-question 3 (OR =1.46). CONCLUSION: Several disease-specific and quality of life factors are related to ICS and tiotropium adherence, but a clear profile of a nonadherent patient cannot yet be outlined. Overusers of ICS and tiotropium experience more anxiety.