Data Pre-Processing of Infrared Spectral Breathprints for Lung Cancer Detection

Larracy, Robyn; Phinyomark, Angkoon; Scheme, Erik

doi:10.1109/embc46164.2021.9629690

Cited by 1 publication

(1 citation statement)

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“…The spectral breathprint features were derived directly from the CRDS absorption profiles through pre-processing and feature extraction methods. More details about spectral data pre-processing can be found in our preliminary study [24]. Due to the high variability in breathprints observed across subjects, in this study, the crafted features emphasized the shape of the spectra rather than individual absorption values.…”

Section: Spectral Breathprintsmentioning

confidence: 99%

Infrared cavity ring-down spectroscopy for detecting non-small cell lung cancer in exhaled breath

2022

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Early diagnosis of lung cancer greatly improves the likelihood of survival and remission, but limitations in existing technologies like low-dose computed tomography have prevented the implementation of widespread screening programs. Breath-based solutions that seek disease biomarkers in exhaled volatile organic compound (VOC) profiles show promise as affordable, accessible and non-invasive alternatives to traditional imaging. In this pilot work, we present a lung cancer detection framework using cavity ring-down spectroscopy (CRDS), an effective and practical laser absorption spectroscopy technique that has the ability to advance breath screening into clinical reality. The main aims of this work were to 1) test the utility of infrared CRDS breath profiles for discriminating non-small cell lung cancer (NSCLC) patients from controls, 2) compare models with VOCs as predictors to those with patterns from the CRDS spectra (breathprints) as predictors, and 3) present a robust approach for identifying relevant disease biomarkers. First, based on a proposed learning curve technique that estimated the limits of a model's performance at multiple sample sizes (10-158), the CRDS-based models developed in this work were found to achieve classification performance comparable or superior to like mass spectroscopy and sensor-based systems. Second, using 158 collected samples (62 NSCLC subjects and 96 controls), the accuracy range for the VOC-based model was 65.19%-85.44% (51.61%-66.13% sensitivity and 73.96%-97.92% specificity), depending on the employed cross-validation technique. The model based on breathprint predictors generally performed better, with accuracy ranging from 71.52%-86.08% (58.06%-82.26% sensitivity and 80.21%-88.54% specificity). Lastly, using a protocol based on consensus feature selection, three VOCs (isopropanol, dimethyl sulfide, and butyric acid) and two breathprint features (from a local binary pattern transformation of the spectra) were identified as possible NSCLC biomarkers. This research demonstrates the potential of infrared CRDS breath profiles and the developed early-stage classification techniques for NSCLC biomarker detection and screening.

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Section: Spectral Breathprintsmentioning

confidence: 99%