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Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
Published on: January 23, 2011
Development and Multicenter Validation of a Breathomics-Based Triage Tool for Lung Cancer: A Prospective Study of
Jianwen Qin1, Meixiu Sun2, Xin Li1
1Department of Respiratory and Critical Care Medicine and Department of Thoracic Surgery, Chest Hospital, Tianjin University, Tianjin, China.
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.
