Exhaled Breath Analysis for Lung Cancer Detection Using Ion Mobility Spectrometry

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

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Hybrid Analytical Platform Based on Field-Asymmetric Ion Mobility Spectrometry, Infrared Sensing, and Luminescence-Based Oxygen Sensing for Exhaled Breath Analysis

Sensors ◽

10.3390/s19122653 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2653

Author(s):

L. Tamina Hagemann ◽

Stefan Repp ◽

Boris Mizaikoff

Keyword(s):

Ion Mobility Spectrometry ◽

Ion Mobility ◽

Small Animal ◽

Breath Analysis ◽

Prototype System ◽

Exhaled Breath ◽

Analytical Technique ◽

Exhaled Breath Analysis ◽

Wide Range ◽

Online Measurements

The reliable online analysis of volatile compounds in exhaled breath remains a challenge, as a plethora of molecules occur in different concentration ranges (i.e., ppt to %) and need to be detected against an extremely complex background matrix. Although this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three different detector types based on truly orthogonal measurement principles as an alternative solution: Field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). By carefully aligning the experimental needs and measurement protocols of all three methods, they were successfully integrated into a single compact analytical platform suitable for online measurements. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2), and acetone as a model volatile organic compound (VOC) commonly present in breath. All three target analytes could be detected within their respectively breath-relevant concentration range, i.e., CO2 and O2 at 3-5 % and at ~19.6 %, respectively, while acetone could be detected with LOQs as low as 165-405 ppt. Orthogonality of the three methods operating in concert was clearly proven, which is essential to cover a possibly wide range of detectable analytes. Finally, the remaining challenges toward the implementation of the developed hybrid FAIMS-FTIR-LS system for exhaled breath analysis for metabolic studies in small animal intensive care units are discussed.

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Cancerous glucose metabolism in lung cancer—evidence from exhaled breath analysis

Journal of Breath Research ◽

10.1088/1752-7155/10/2/026012 ◽

2016 ◽

Vol 10 (2) ◽

pp. 026012 ◽

Cited By ~ 15

Author(s):

Tali Feinberg ◽

Layah Alkoby-Meshulam ◽

Jens Herbig ◽

John C Cancilla ◽

Jose S Torrecilla ◽

...

Keyword(s):

Lung Cancer ◽

Glucose Metabolism ◽

Breath Analysis ◽

Exhaled Breath ◽

Exhaled Breath Analysis

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MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Chemosensors ◽

10.3390/chemosensors9080209 ◽

2021 ◽

Vol 9 (8) ◽

pp. 209

Author(s):

Davide Marzorati ◽

Luca Mainardi ◽

Giulia Sedda ◽

Roberto Gasparri ◽

Lorenzo Spaggiari ◽

...

Keyword(s):

Lung Cancer ◽

Survival Rate ◽

Mortality Rate ◽

Early Diagnosis ◽

Gas Sensor ◽

Electronic Nose ◽

Gas Sensors ◽

Breath Analysis ◽

Exhaled Breath ◽

Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

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IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

Journal of Integrative Bioinformatics ◽

10.1515/jib-2007-75 ◽

2007 ◽

Vol 4 (3) ◽

pp. 186-197 ◽

Cited By ~ 17

Author(s):

Jan Baumbach ◽

Alexander Bunkowski ◽

Sita Lange ◽

Timm Oberwahrenbrock ◽

Nils Kleinbölting ◽

...

Keyword(s):

Lung Cancer ◽

Ion Mobility Spectrometry ◽

Ion Mobility ◽

Software System ◽

Medical Software ◽

Cancer Types ◽

The One ◽

Lung Cancer Detection ◽

Processing Steps ◽

Fold Cross Validation

Abstract IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS) data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world.After reviewing the IMS technology, we first describe the software architecture of IMS2 and then the integrated classification module, including necessary pre-processing steps and different classification methods. The Lung Hospital Hemer (Germany) provided IMS data of 35 patients suffering from lung cancer and 72 samples of healthy persons. IMS2 correctly classifies 99% of the samples, evaluated using 10-fold cross-validation.

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