Identification Of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers In Human Exhaled Breath

Study Overview

Identification of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath

The goal of this observational study is to develop an advanced expiratory algorithm model utilizing exhaled breath volatile organic compound (VOC) marker molecules. This model aims to accurately diagnose mutiple pulmonary diseases. 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 diagnose several common pulmonary diseases. 2. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.

This is a prospective, cross-sectional, observational cohort study aimed at recruiting 10,000 participants with multiple pulmonary disease, including lung cancer, lung infection, chronic obstructive pulmonary disease (COPD), bronchitis, pulmonary fibrosis, pulmonary embolism, pulmonary arterial hypertension, tuberculosis, lung abscess, emphysema, radioactive lung injury, cystic fibrosis of the lung, Bronchial Asthma, Bronchiectasis, interstitial lung disease (ILD), preserved ratio impaired spirometry (PRISm) etc . Exhaled breath samples from these participants will be collected and analyzed using Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system. Upon obtaining the μGC-PID results, a comprehensive evaluation of the diagnostic capabilities of exhaled breath samples in differentiating various pulmonary diseases will be performed, leveraging clinical diagnostic results, CT examination data, and clinical data.

Lung Cancer
Lung Infection
COPD
Bronchitis
Pulmonary Fibrosis
Pulmonary Embolism
Pulmonary Arterial Hypertension
Pulmonary Tuberculosis
Pulmonary Abscess
Emphysema
Lung Injury
Cystic Fibrosis of the Lung
Bronchial Asthma
Bronchiectasis
Interstitial Lung Disease
Preserved Ratio Impaired Spirometry

OTHER: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system

MLD001

Study Record Dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Registration Dates	Results Reporting Dates	Study Record Updates
First Submitted 2024-07-18	Results First Submitted N/A	Last Update Submitted that met QC Criteria 2025-03-23
First Submitted that Met QC Criteria 2024-07-25	Results First Submitted that Met QC Criteria N/A	Last Update Posted (ACTUAL) 2025-03-26
First Posted (ACTUAL) 2024-07-30	Results First Posted N/A	Last Verified 2025-03

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

Design Details

Primary Purpose:
N/A

Allocation:
N/A

Interventional Model:
N/A

Masking:
N/A

Arms and Interventions

Participant Group/Arm	Intervention/Treatment
: pulmonary disease Individuals with abnormalities in lung CT imaging and clinically diagnosed with lung cancer, lung infection, chronic obstructive pulmonary disease (COPD), bronchitis, pulmonary fibrosis, pulmonary embolism, pulmonary arterial hypertension, tuberculosis, l	OTHER: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
: normal individual Individuals with no abnormalities detected in lung CT imaging.	OTHER: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID

Participant Group/Arm

Intervention/Treatment

: pulmonary disease

Individuals with abnormalities in lung CT imaging and clinically diagnosed with lung cancer, lung infection, chronic obstructive pulmonary disease (COPD), bronchitis, pulmonary fibrosis, pulmonary embolism, pulmonary arterial hypertension, tuberculosis, l

OTHER: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system

Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID

: normal individual

Individuals with no abnormalities detected in lung CT imaging.

OTHER: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system

Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID

Primary Outcome Measures	Measure Description	Time Frame
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases.	The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with clinical diagnosis and CT/LDCT diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).	2 years

Secondary Outcome Measures	Measure Description	Time Frame
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases.	The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with clinical diagnosis and CT/LDCT diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).	2 years

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Name: Hengrui Liang, MD

Phone Number: +86 15625064712

Email: hengrui_liang@163.com

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person’s general health condition or prior treatments.

Ages Eligible for Study:
ALL

Sexes Eligible for Study:
18 Years

Accepts Healthy Volunteers:
1

Males or females, age must be 18 years old or above.
Patients must meet the CT imaging diagnostic criteria for different lung diseases, and patients must be able to provide electronic versions of CT image data.
Patients must have a clear clinical diagnosis.
All participants must sign a written informed consent form.

Pregnant women.
Individuals with a history of cancer other than lung disease.
Individuals who have undergone organ transplants or non-autologous (allogeneic) bone marrow or stem cell transplants.
Individuals with other severe organic diseases or mental illnesses.
Individuals with metabolic diseases such as diabetes, hyperlipidemia, etc.
Any other condition that researchers deem unsuitable for participation in this clinical trial.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

The First Affiliated Hospital of Guangzhou Medical University
First People's Hospital of Foshan
Sichuan Cancer Hospital and Research Institute
Liwan District Central Hospital
Shanghai Chest Hospital
Peking Union Medical College Hospital
Guangzhou Development Zone Hospital
Huangpu District Hongshan Street Community Health Service Center
Huangpu District Chinese Medicine Hospital
Fifth Affiliated Hospital of Guangzhou Medical University
Huangpu District Jiufo Street Community Health Service Center
Huangpu District Xinlong Town Central Hospital
Huangpu District Yonghe Street Community Health Service Center
Huangpu District Lianhe Street Second Community Health Service Center

Investigators

STUDY_CHAIR: Jianxing He, MD, The First Affiliated Hospital of Guangzhou Medical University

Publications

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020 Jun;8(6):585-596. doi: 10.1016/S2213-2600(20)30105-3.
Ratiu IA, Ligor T, Bocos-Bintintan V, Mayhew CA, Buszewski B. Volatile Organic Compounds in Exhaled Breath as Fingerprints of Lung Cancer, Asthma and COPD. J Clin Med. 2020 Dec 24;10(1):32. doi: 10.3390/jcm10010032.
van de Kant KD, van der Sande LJ, Jobsis Q, van Schayck OC, Dompeling E. Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review. Respir Res. 2012 Dec 21;13(1):117. doi: 10.1186/1465-9921-13-117.
Wang J, Janson C, Gislason T, Gunnbjornsdottir M, Jogi R, Orru H, Norback D. Volatile organic compounds (VOC) in homes associated with asthma and lung function among adults in Northern Europe. Environ Pollut. 2023 Mar 15;321:121103. doi: 10.1016/j.envpol.2023.121103. Epub 2023 Jan 21.
V A B, Subramoniam M, Mathew L. Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose. Expert Rev Mol Diagn. 2021 Nov;21(11):1223-1233. doi: 10.1080/14737159.2021.1971079. Epub 2021 Aug 27.

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