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.

Oxygen depletion during and after mTHPC-mediated photodynamic therapy in RIF1 and H-MESO1 tumors.

During photodynamic therapy (PDT), low oxygenation levels, induced both by oxygen consumption and by vascular occlusion, can lead to an inefficient photochemical reaction that may compromise the efficacy of PDT. In the present studies, tumor oxygenation was measured before, during and after meta-tetrahydroxyphenylchlorin (mTHPC)-mediated PDT of murine RIF1 tumors and human mesothelioma xenografts (H-MESO1). Tumor pO2 was measured in real time with Eppendorf polarography, and the extent of relative hypoxia at specific times was measured by immunohistochemical staining. Significant decreases in median pO2 values, as well as an increase in the number of values below 2.5 mmHg, were seen during and after PDT in RIF1 tumors, although there was a large intertumoral variation. Tumor pO2 values did not change significantly in H-MESO1 tumors. Staining with antibodies against the hypoxia marker EF3 showed significant increases in relative hypoxia after PDT in both tumor types compared with separate groups of untreated controls. Our results are consistent with PDT-induced oxygen depletion (reduced pO2) leading to an increase in relative hypoxia in RIF1 tumors. Extensive necrosis in the H-MESO1 tumors may have prevented the detection of PDT-induced hypoxia using the Eppendorf polarographic needle, whereas immunohistochemistry did reveal increases in relative hypoxia.