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

Sharina Kort; Marjolein Brusse-Keizer; Jan Willem Gerritsen; Hugo Schouwink; Emanuel Citgez; Frans de Jongh; Jan van der Maten; Suzy Samii; Marco van den Bogart; Job van der Palen

doi:10.1183/23120541.00221-2019

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

ERJ Open Res. 2020 Mar 16;6(1):00221-2019. doi: 10.1183/23120541.00221-2019. eCollection 2020 Jan.

Authors

Affiliations

¹ Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands.
² Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands.
³ The eNose Company, Zutphen, the Netherlands.
⁴ Dept of Pulmonary Medicine, Medisch Centrum Leeuwarden, Leeuwarden, the Netherlands.
⁵ Dept of Pulmonary Medicine, Deventer Ziekenhuis, Deventer, the Netherlands.
⁶ Dept of Pulmonary Medicine, Bernhoven Uden, Uden, the Netherlands.
⁷ Dept of Research Methodology, Measurement, and Data Analysis, University of Twente, Enschede, the Netherlands.

Abstract

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.