Development and Multicenter Validation of a Breathomics-Based Triage Tool for Lung Cancer: A Prospective Study of 5,214 Participants.

BACKGROUND

The inherent false-positive rate of CT imaging necessitates efficient, noninvasive triage tools to distinguish lung cancer from benign mimics, thereby streamlining early detection and mitigating unnecessary invasive procedures.

RESEARCH QUESTION: Can a machine learning-derived, molecularly resolved breathomics prediction model effectively triage patients with radiologically detected pulmonary abnormalities in a real-world symptomatic cohort?

STUDY DESIGN AND

METHODS

In this large-scale, prospective, multicenter diagnostic accuracy study, we enrolled 5,214 symptomatic patients with radiological lung abnormalities at two campuses. Participants were allocated into a discovery cohort (n = 4,669) and a geographically independent external validation cohort (n = 545). Exhaled volatile organic compounds were analyzed by high-throughput proton-transfer-reaction time-of-flight mass spectrometry. A machine learning model integrating a specific volatile organic compound signature with clinical factors was developed, locked, and validated blind in the external cohort.

RESULTS

The integrated model achieved an area under the receiver operating characteristic curve (AUC) of 0.891 (95% CI, 0.864-0.915) in the independent internal testing set. In the independent external validation, the model maintained robust performance (AUC, 0.850; 95% CI, 0.805-0.890). In this validation cohort, applying the prespecified "rule-out" threshold, the model achieved a sensitivity of 93.1% (95% CI, 90.5%-95.4%), with an overall specificity of 55.9% and negative predictive value of 71.0%. Importantly, in the intended-use pulmonary medicine subgroup (n = 155), the sensitivity was 91.4%, with specificity and negative predictive value reaching 55.8% and 89.0%, respectively. Subgroup analyses confirmed consistent efficacy across diverse clinical scenarios, notably maintaining robust accuracy in detecting early-stage disease (AUC, 0.849).

INTERPRETATION: This study represents the largest prospective validation of a mass-spectrometry-based breath test to date. The validated prediction model holds potential to serve as a robust, noninvasive triage tool. Its integration into the diagnostic pathway shows promise for enhancing efficiency and reducing unnecessary biopsies, particularly in respiratory outpatient settings.