Combining exhaled-breath analysis data with clinical parameters to improve the diagnosis of lung cancer

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

The value of chest computer tomography and cervical mediastinoscopy in the preoperative assessment of patients with malignant pleural mesothelioma

BACKGROUND: Patients with localized malignant pleural mesothelioma (MPM) can be considered for surgical resection with or without additional treatment. For this approach it is imperative to select patients without mediastinal lymph node involvement. In this study cervical mediastinoscopy (CM) is compared with computer tomography (CT) scanning for its diagnostic accuracy in assessing mediastinal lymph nodes during preoperative workup. METHODS: Computer tomography scans of the chest and CM were performed in 43 patients with proven unilateral MPM. The CT scans were reviewed by one radiologist and two chest physicians. At CM the lymph node samples were taken from stations Naruke 2, 3, 4, and 7. Computer tomography and CM results were compared with final histopathologic findings obtained at thoracotomy or, if this was not performed, at CM. RESULTS: Computer tomography scanning revealed pathologic enlarged lymph nodes with a shortest diameter of at least 10 mm in 17 of 43 patients (39%). There was histopathologic evidence of lymph node metastases at CM in 11 of these patients (26%). This resulted in a sensitivity of 60% and 80%, a specificity of 71% and 100%, and a diagnostic accuracy of 67% and 93% for CT and CM, respectively. CONCLUSIONS: Cervical mediastinoscopy is a valuable diagnostic procedure for patients with MPM who are considered candidates for surgical-based therapy. Results of CM are more reliable than those obtained by CT scanning. Our data confirm results of previous studies reporting that mediastinal lymph node involvement is a frequent event in MP

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

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/702