Sharina Kort

Background: Lung cancer remains a leading cause of cancer-related deaths. Early-stage detection is pivotal for curative interventions, yet most cases are diagnosed at advanced stages. Noninvasive point-of-care diagnostic strategies are needed to reduce lung cancer mortality rates. We assess the potential of electronic nose (eNose) devices as a tool for lung cancer detection.Study design and methods: All online available databases were searched. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we reviewed studies using eNose forin vivoexhaled air analysis in lung cancer patients. Included patients had pathologically confirmed lung cancer, both non-small cell lung cancer and small cell lung cancer, in various stages. Primary outcomes were sensitivity, specificity, negative predictive value and area under the curve. Secondary outcomes evaluated eNose performance in distinguishing non-small cell lung cancer subtypes and small cell lung cancer. Additionally, a subgroup analysis was conducted to evaluate the efficacy of various commonly used sensors in this differentiation. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used for risk-of-bias assessment.Results: We included 35 studies, comprising 4483 patients. Pooled sensitivity and specificity were 0.90 (95% CI 0.87–0.93) and 0.89 (95% CI 0.85–0.93), respectively. The median negative predictive value was 0.93 and the median area under the curve was 0.91. Both the likelihood of bias and concerns regarding applicability were minimal.Interpretation: eNose devices hold promise as noninvasive, point-of-care tools for lung cancer diagnosis. Owing to the high sensitivity and negative predictive values and great ease of use, they could be valuable additions to the diagnostic toolbox. Further research and validation efforts are critical to maximise their impact on early lung cancer detection.

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. Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose significantly improves the diagnostic accuracy to detect the presence or absence of lung cancerhttp://bit.ly/38ps6fH

Introduction: Lung cancer remains a leading cause of cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s), reflecting pathological processes, might detect lung cancer at an early stage, possibly leading to improved outcomes. Combining breath patterns with clinical parameters may improve the accuracy to diagnose lung cancer. Methods: In a multi-center study 144 subjects diagnosed with non-small cell lung cancer (NSCLC) and 146 healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). The diagnostic accuracy, presented as Area under the Curve (AUC) of the Aeonose™ sec was compared with the diagnostic accuracy when combined with clinical parameters in a multivariate logistic regression analysis. Results: Confirmed NSCLC patients (67.1 (9.0) years; 57.6% male) were compared with controls without NSCLC (62.1 (7.1) years; 40.4% male). The AUC of the absolute Aeonose™ value obtained by a trained neural network was 0.76 (95% CI: 0.71-0.82). Adding age, number of pack years, and presence of COPD to this absolute value of the Aeonose™ from the neural network resulted in an improved performance with an AUC of 0.86 (95% CI: 0.81-0.90). By choosing an appropriate threshold value in the ROC-diagram of the multivariate model, we observed a sensitivity of 95.7%, a specificity of 59.7%, and a positive and negative predictive value of 69.5% and 92.5%, respectively. Conclusion: Adding readily available clinical information to the absolute obtained value of exhaled-breath analysis with the Aeonose™ improves the diagnostic accuracy to detect the presence or absence of lung cancer.

Introduction: Lung cancer is a leading cause of cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s) may detect lung cancer at an early stage, possibly leading to better outcomes. Artificial neural networks (ANN) have proven to be accurate tools to diagnose lung cancer by distinguishing between breath profiles of healthy and sick individuals. Adding readily available clinical information to the ANN may improve the diagnostic accuracy. Methods: Subjects with non-small cell lung cancer (NSCLC) and healthy controls breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Diagnostic accuracy, presented as Area under the Curve (AUC) was studied in a prospective, multi-center study in 282 individuals of whom 140 had confirmed NSCLC. We compared a 6-element breath-vector (Aeonose-only) with a 10-element vector (Aeonose plus age, pack years, COPD-presence, and gender). Results: Confirmed NSCLC patients (67.1 (9.0) years; 57.6% male) were compared with non-NSCLC controls (62.1 (7.1) years; 40.4% male). The AUC based on the Aeonose-only classification was 0.75 (95% CI: 0.69-0.81). Adding age, number of pack years, presence of COPD, and gender to the ANN resulted in an improved performance with an AUC of 0.84 (95% CI: 0.79-0.88). By choosing an appropriate threshold value in the ROC-diagram of the multivariate model, we observed a sensitivity of 92.9%, a specificity of 53.3%, and a positive and negative predictive value of 66.3% and 88.4%, respectively. Conclusion: The diagnostic accuracy to predict presence or absence of lung cancer can be improved significantly by adding readily available clinical information to the ANN.

Introduction: Lung cancer occupies the first place in cancer-related deaths. 15% of lung cancer cases present timely with localized, potentially curable disease. Therefore, a sensitive, non-invasive tool to detect lung cancer earlier is desired. Exhaled breath analysis by electronic nose technology measures volatile organic compounds (VOC9s), which reflect pathological processes in the body. We aimed to determine the diagnostic accuracy of exhaled breath analysis by the Aeonose™ in non-small cell lung cancer (NSCLC). Methods: Subjects diagnosed with NSCLC and healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, The Netherlands) during 5 minutes. 3 Aeonoses were used, producing repeatable and interchangeable results. The diagnostic accuracy was prospectively studied in a multicentre study in 107 subjects. 60 Had a histopathologically confirmed diagnosis of NSCLC. Their exhaled breath prints were compared with 47 subjects without lung cancer. Data compression and artificial neural networks were used for the analysis of VOC data. Results: The subjects had a mean age of 64.4±8.8 years and 53 (50%) were male. The optimal threshold to rule out NSCLC resulted in a sensitivity of 90% (54/60) with a specificity of 74% (35/47), positive predictive value of 82% (54/66), negative predictive value of 85% (35/41) and an area under the curve of 0.80. Conclusion: The data suggest that electronic nose technology with the Aeonose™ can have a substantial added value in the non-invasive diagnostic workup in NSCLC. Data collection is still ongoing. With more subjects added (N=350 expected), the model can in all likelihood be further improved to increase sensitivity and negative predictive value, so no patient is missed.

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 Kirsten Koehorst-ter Huurne,1 Sharina Kort,1 Job van der Palen,1,3 Wendy JC van Beurden,1 Kris LL Movig,2 Paul van der Valk,1 Marjolein Brusse-Keizer1 1Department of Pulmonary Medicine, 2Department of Clinical Pharmacy, Medisch Spectrum Twente, 3Department of Research Methodology, Measurement, and Data Analysis, University of Twente, Enschede, the Netherlands 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 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. Keywords: chronic obstructive pulmonary disease, adherence, inhalation medication, quality of life