An observational study in Pulmonary Nodules, Multiple, Pulmonary Nodules, Solitary and Lung Cancer, sponsored by ChromX Health. Recruiting at 15 sites in China. Open to participants aged 18 Years to 80 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-12-24.
Sponsored by ChromX Health · Observational
The goal of this observational study is to develop an advanced expiratory algorithm model utilizing exhaled breath volatile organic compound (VOC) markers. This model aims to accurately differentiate benign from malignant nodules in individuals harboring pulmonary nodules. The primary objectives it strives to accomplish are:
By utilizing this comprehensive approach, the study hopes to contribute significantly to early detection and accurate classification of pulmonary nodules, ultimately leading to improved patient care and treatment outcomes.
This is a prospective, cross-sectional, and observational cohort study aiming at recruiting 3000 participants with pulmonary nodules ranging from 5 to 30 mm in diameter. Prior to invasive surgery, exhaled breath samples will be collected from these participants and analyzed using Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system. Following the acquisition of μGC-PID results, a comprehensive evaluation of the diagnostic performance of VOC biomakers distinguishing between benign and malignant pulmonary nodules will be conducted, leveraging histopathological findings, CT examination data, and clinical data.
People who have pulmonary nodules with a large diameter between 5 mm and 30 mm on a CT scan within six months.
Exclusion Criteria:
Pre-surgery adult patients with pulmonary nodule found by CT scan.
Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
Detection of volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules.
The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with pathologic diagnosis and CT/LDCT data, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
Time frame: 3 years
The diagnostic effectiveness of an AI model to identify specific types of malignant nodules, including lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer.
The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with pathologic diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
Time frame: 3 years
Establish an exhaled breath VOC model for predicting EGFR mutations in malignant pulmonary nodules.
Establish an exhaled breath VOC model for predicting EGFR mutations in pathologically confirmed malignant pulmonary nodules. And evaluate the prediction accuracy by comparing the results of EGFR gene testing.
Time frame: 3 years
Plan to share: No
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