Differentiation of Benign and Malignant Pulmonary Nodules by Volatile Organic Compounds in Human Exhaled Breath
NCT Number: NCT06518655
Status: RECRUITING
Study Type: OBSERVATIONAL
Conditions: Pulmonary Nodules, Multiple|Pulmonary Nodules, Solitary|Lung Cancer
Interventions: OTHER: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
Sponsor: ChromX Health
Collaborators: The First Affiliated Hospital of Guangzhou Medical University|First People's Hospital of Foshan|Sichuan Cancer Hospital and Research Institute
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 3000
Study Design: Observational Model: |Time Perspective: p
Start Date: 2024-06-30
Completion Date: 2027-06-30
Locations: First People's Hospital of Foshan, Foshan, Guangdong, 528000, China|The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510140, China|Sichuan Cancer Hospital, Chengdu, Sichuan, 610042, China

Summary:
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:

1. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules.
2. To evaluate the diagnostic effectiveness of an AI model that employs exhaled breath VOC biomakers to identify specific types of malignant nodules, including lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer.
3. To explore and identify key characteristic VOCs combinations that are associated with EGFR site mutations in malignant nodules, further modeling and evaluating the classification performance.

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.

Primary Outcome Measures: 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)., 3 years
Secondary Outcome Measures: 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)., 3 years
Other Outcome Measures: 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., 3 years