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TRUVACE RECORD VERSION
record: TRV-2026-0616
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-01T06:08:00.367985Z
status: published
lens: g_space
sector: health
headline: Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study
dek: Scalable, non-invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We evaluated Inflammacheck, a point-of-care device utilizing exhaled breath condensate (EBC) H 2 O 2 and physiological parameters with machine learning for non-invasive lung cancer detection in a real-world screening population. Exhaled Hydrogen Peroxide for Early Lung Cancer Detection (ExPeL) study participants, from the UK Targeted Lung Health Check (TLHC) programme, included i…
gain_title: A point-of-care exhaled breath condensate device combined with physiological parameters and ensemble machine learning identified early-stage lung cancer in a real-world screening cohort with 85.7% accuracy and 100% specificity on held-out test data.
problem_title: (none)
trace_subject: (none)
gain_reading: A point-of-care exhaled breath condensate device combined with physiological parameters and ensemble machine learning identified early-stage lung cancer in a real-world screening cohort with 85.7% accuracy and 100% specificity on held-out test data.
gain_evidence: The voting ensemble model achieved: Accuracy 85.7%, Sensitivity 80%, Specificity 100%, Precision (PPV) 100%, ROC-AUC 0.90 and MCC 0.73 | Inflammacheck effectively distinguishes early-stage lung cancer via a rapid, non-invasive breath test, findings which are highly relevant for primary care and screening triage
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers tested Inflammacheck, a point-of-care device that measures hydrogen peroxide in exhaled breath condensate plus physiological signals, combined with machine learning, in 34 participants from a UK lung health check programme where 83% of cancers were stage I-II. Multivariate analyses separated cancer and control groups, and a voting ensemble achieved 85.7% accuracy and 0.90 ROC-AUC on held-out data.

The result matters because scalable non-invasive triage tools are needed for primary care where symptoms are non-specific and prevalence is low. Whether the 100% specificity and high accuracy persist beyond this small, early-stage-enriched cohort, and how metabolomic signatures integrate with the point-of-care sensor, remains to be validated in larger prospective screening workflows.
limitation: Findings are based on a small cohort of 34 participants with valid EBC data, limiting generalizability to broader screening populations.
tag: Evidence-backed gain
key_points: ExPeL study enrolled participants from the UK Targeted Lung Health Check programme including suspected lung cancer cases and low-risk ever-smoker controls. | Device measured H2O2, end-tidal CO2, humidity, temperature, and exhalation flow rate with multivariate PCA, LDA and Mahalanobis analysis showing group separation. | Untargeted LC-MS metabolomics identified 2132 features with four key metabolites yielding AUC 0.969 for cancer discrimination.
rundown: The study collected exhaled breath condensate via Inflammacheck in the ExPeL cohort drawn from the UK Targeted Lung Health Check programme. In addition to H2O2, the device captured end-tidal CO2, humidity, temperature and flow rate, and analyses noted greater dispersion in cancer patients reflecting physiological heterogeneity missed by univariate analysis.

Supervised models were trained on SMOTE-balanced data and evaluated on held-out test sets, with the voting ensemble reporting no false positives. Parallel untargeted metabolomics was performed to identify discriminatory molecular features beyond the sensor parameters.
sources:
- peer_reviewed | Clinical and Translational Science | https://doi.org/10.1111/cts.70687 | 2026-08-01
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