Lung CancerNCT06528418
Identification of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath
An observational study of Lung Cancer and Lung Infection, run by ChromX Health at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer, Lung Infection, COPD
- What is being tested?
- Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- 30 Jun 2024to 30 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients with abnormal lung CT images within the past six months, 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 .
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510140
Hengrui Liang, MD, +86 15625064712, hengrui_liang@163.com
About this study
Official title: Exploration and Study on the Identification of Various 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:
- To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose several common pulmonary diseases.
- To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.
Read the detailed description
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.
- Conditions
- Lung Cancer, Lung Infection, COPD, Bronchitis, Pulmonary Fibrosis, Pulmonary Embolism
- Being tested
- Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
- Keywords
- Pulmonary Disease, Volatile Organic Compounds, Human Exhaled Breath, micro Gas Chromatography-photoionisation, detector (μGC-PID) system
- Medical subject headings
- Lung Neoplasms, Pulmonary Disease, Chronic Obstructive, Bronchitis, Pulmonary Fibrosis, Pulmonary Embolism, Pulmonary Arterial Hypertension, Tuberculosis, Pulmonary, Lung Abscess
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) systemOther
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
| Study group | Role | What they receive |
|---|---|---|
| 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, lung abscess, emphysema, radioactive lung injury, cystic fibrosis of the lung, Bronchial Asthma, Bronchiectasis, interstitial lung disease (ILD), preserved ratio impaired spirometry (PRISm) etc . Receives: Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system | |
| normal individual | Individuals with no abnormalities detected in lung CT imaging. Receives: Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 2 years
Secondary outcomes (1)
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).
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jul 2024
- Study start30 Jun 2024
- Primary completion30 Dec 2026 (estimated)
- Study completion30 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- ChromX Health
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- 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
- Responsible party
- Sponsor
- Investigators
- Jianxing He, MD, Study chair, The First Affiliated Hospital of Guangzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06528418
- Sponsor's protocol ID
- MLD001
- Status last verified
- Mar 2025
- First submitted
- 18 Jul 2024
- Last updated
- 26 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 32526187Ratiu 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.
PubMed 33374433van 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.
PubMed 23259710Wang 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.
PubMed 36690293V 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.
PubMed 34415806
Study at a glance
From the registry record, last updated by the sponsor on 26 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Exploration and Study on the Identification of Various Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath
- ClinicalTrials.gov ID
- NCT06528418
- Acronym / sponsor ID
- MLD001
- Conditions
- Lung Cancer, Lung Infection, COPD, Bronchitis, Pulmonary Fibrosis, Pulmonary Embolism
- Keywords
- Pulmonary Disease, Volatile Organic Compounds, Human Exhaled Breath, micro Gas Chromatography-photoionisation, detector (μGC-PID) system
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- ChromX HealthIndustry (pharma, biotech, devices)
- Collaborators
- 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
- Study start
- 30 Jun 2024 (actual)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 30 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose several common pulmonary diseases.
- To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| pulmonary disease | – | Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) systemIndividuals 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, lung abscess, emphysema, radioactive lung injury, cystic fibrosis of the lung, Bronchial Asthma, Bronchiectasis, interstitial lung disease (ILD), preserved ratio impaired spirometry (PRISm) etc . |
| normal individual | – | Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) systemIndividuals with no abnormalities detected in lung CT imaging. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with abnormal lung CT images within the past six months, 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 .
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) systemExhaled 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 | Other | normal individual, pulmonary disease |
Primary outcome
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases.
Time frame: 2 years
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).
1 secondary outcome
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases.
Time frame: 2 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Mar 2025.
- Background: PubMed 32526187GBD 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.
- Background: PubMed 33374433Ratiu 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.
- Background: PubMed 23259710van 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.
- Background: PubMed 36690293Wang 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.
- Background: PubMed 34415806V 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jianxing He, MD study chair
The First Affiliated Hospital of Guangzhou Medical University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Lung Infection, run by ChromX Health at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 26 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Hengrui Liang, MD (+86 15625064712, hengrui_liang@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases. (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06528418, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Mar 2025.