Pulmonary Nodules, MultipleNCT06518655
Differentiation of Benign and Malignant Pulmonary Nodules by Volatile Organic Compounds in Human Exhaled Breath
An observational study of Pulmonary Nodules, Multiple and Pulmonary Nodules, Solitary, run by ChromX Health at 15 sites 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) markers. This model aims to accurately differentiate benign from malignant nodules in individuals harboring pulmonary nodules. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Pulmonary Nodules, Multiple, Pulmonary Nodules, Solitary, Lung Cancer
- What is being tested?
- Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
- How many people
- 3,000 participants
- Where
- 15 sitesChina
- 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 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: People who have pulmonary nodules with a large diameter between 5 mm and 30 mm on a CT scan within six months.
Who may be able to join
- 18-80 years old;
- Pulmonary nodules were detected through low-dose spiral CT, chest CT conventional scan, or high-resolution thin-layer CT examination, with a maximum diameter of 5-30 mm, including solid nodules and ground glass nodules;
- Patients require pulmonary nodule resection to define the type of nodule pathology;
- The Patients have not yet used any drugs for tumor treatment;
- Patients and/or family members are able to understand the research protocol and are willing to participate in this study, providing written informed consent.
Who cannot join
- The maximum diameter of pulmonary nodules is greater than 30 mm;
- Patients are unable to determine the pathological diagnosis of pulmonary nodules after surgical resection or biopsy;
- Patients with recurrent lung cancer;
- Patients who have undergone lung transplantation or lobectomy;
- Individuals who currently or have a history of malignant tumors;
- Patients in the acute phase of inflammation or in need of intensive care in the above selected disease groups;
Show all 12Show fewer
- Individuals with severe liver and kidney dysfunction;
- Mental illness patients (such as severe dementia, schizophrenia, severe depression, manic depressive psychosis, etc.);
- Confirmed HIV patients;
- Pregnant or lactating women;
- Patients or family members are unable to understand the conditions and objectives of this study.
- The patient is unwilling or unable to personally sign the informed consent form.
Trial locations and contacts
15 sites in 1 country. Contact the study team or a site near you to ask about taking part.
15 sites in 1 country
China15 sites, 15 recruiting
- Recruiting
Peking Union Medical College Hospital
Beijing, Beijing Municipality
Qian Wang, MD
- Recruiting
Sichuan Cancer Hospital
Chengdu, Sichuan, 610042
Bo Tian, MD, +86 18980053101
- Recruiting
First People's Hospital of Foshan
Foshan, Guangdong, 528000
Zhuxing Chen
- Recruiting
The First Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510140
Hengrui Liang, MD, +86 15625064712, hengrui_liang@163.com
- Recruiting
Liwan District Central Hospital
Guangzhou, Guangdong, 510175
Hengrui Liang, MD
- Recruiting
Guangzhou Development Zone Hospital
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
Huangpu District Chinese Medicine Hospital
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
Huangpu District Hongshan Street Community Health Service Center
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
Huangpu District Jiufo Street Community Health Service Center
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
Huangpu District Lianhe Street Second Community Health Service Center
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
Huangpu District Xinlong Town Central Hospital
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
Huangpu District Yonghe Street Community Health Service Center
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
The Fifth Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong
Hengrui Liang, MD
- Recruiting
Shanghai Chest Hospital
Shanghai, Shanghai Municipality
Yanwei Zhang, MD
- Recruiting
Renmin Hospital of Wuhan University
Wuhan, Hubei
Huiqing Lin, MD
About this study
Official title: Exploratory Study on the Identification of Benign and Malignant Pulmonary Nodules Using Volatile Organic Compounds 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) markers. This model aims to accurately differentiate benign from malignant nodules in individuals harboring pulmonary nodules. 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 distinguishing benign and malignant pulmonary nodules.
- 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.
- 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.
Read the detailed description
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.
- Being tested
- Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
- Keywords
- Pulmonary Nodules, Lung Cancer, Volatile Organic Compounds, Human Exhaled Breath
- Medical subject headings
- Multiple Pulmonary Nodules, Solitary Pulmonary Nodule, Lung Neoplasms
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
Detection of volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
| Study group | Role | What they receive |
|---|---|---|
| Pulmonary Nodules | Pre-surgery adult patients with pulmonary nodule found by CT scan. 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 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).
Measured: 3 years
Secondary outcomes (1)
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).
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 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 (20)Papers the sponsor has linked to this study.
- Other trials for Pulmonary Nodules, MultipleStudies 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
- 3,000 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, Renmin Hospital of Wuhan University
- Responsible party
- Sponsor
- Investigators
- Jianxing He, MD, Study chair, The First Affiliated Hospital of Guangzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06518655
- Sponsor's protocol ID
- LCLN01
- Status last verified
- Dec 2025
- First submitted
- 19 Jul 2024
- Last updated
- 24 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
PubMed 33538338Xia C, Dong X, Li H, Cao M, Sun D, He S, Yang F, Yan X, Zhang S, Li N, Chen W. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl). 2022 Feb 9;135(5):584-590. doi: 10.1097/CM9.0000000000002108.
PubMed 35143424Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, Stein KD, Alteri R, Jemal A. Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016 Jul;66(4):271-89. doi: 10.3322/caac.21349. Epub 2016 Jun 2.
PubMed 27253694National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
PubMed 21714641Shlomi D, Abud M, Liran O, Bar J, Gai-Mor N, Ilouze M, Onn A, Ben-Nun A, Haick H, Peled N. Detection of Lung Cancer and EGFR Mutation by Electronic Nose System. J Thorac Oncol. 2017 Oct;12(10):1544-1551. doi: 10.1016/j.jtho.2017.06.073. Epub 2017 Jul 12.
PubMed 28709937van de Goor R, van Hooren M, Dingemans AM, Kremer B, Kross K. Training and Validating a Portable Electronic Nose for Lung Cancer Screening. J Thorac Oncol. 2018 May;13(5):676-681. doi: 10.1016/j.jtho.2018.01.024. Epub 2018 Feb 6.
PubMed 29425703Hanna GB, Boshier PR, Markar SR, Romano A. Accuracy and Methodologic Challenges of Volatile Organic Compound-Based Exhaled Breath Tests for Cancer Diagnosis: A Systematic Review and Meta-analysis. JAMA Oncol. 2019 Jan 1;5(1):e182815. doi: 10.1001/jamaoncol.2018.2815. Epub 2019 Jan 10.
PubMed 30128487Horvath I, Lazar Z, Gyulai N, Kollai M, Losonczy G. Exhaled biomarkers in lung cancer. Eur Respir J. 2009 Jul;34(1):261-75. doi: 10.1183/09031936.00142508.
PubMed 19567608Mitsui T, Kondo T. Inadequacy of theoretical basis of breath methylated alkane contour for assessing oxidative stress. Clin Chim Acta. 2003 Jul 1;333(1):91; author reply 93-4. doi: 10.1016/s0009-8981(03)00173-6. No abstract available.
PubMed 12809740Stone BG, Besse TJ, Duane WC, Evans CD, DeMaster EG. Effect of regulating cholesterol biosynthesis on breath isoprene excretion in men. Lipids. 1993 Aug;28(8):705-8. doi: 10.1007/BF02535990.
PubMed 8377584Nakhleh MK, Amal H, Jeries R, Broza YY, Aboud M, Gharra A, Ivgi H, Khatib S, Badarneh S, Har-Shai L, Glass-Marmor L, Lejbkowicz I, Miller A, Badarny S, Winer R, Finberg J, Cohen-Kaminsky S, Perros F, Montani D, Girerd B, Garcia G, Simonneau G, Nakhoul F, Baram S, Salim R, Hakim M, Gruber M, Ronen O, Marshak T, Doweck I, Nativ O, Bahouth Z, Shi DY, Zhang W, Hua QL, Pan YY, Tao L, Liu H, Karban A, Koifman E, Rainis T, Skapars R, Sivins A, Ancans G, Liepniece-Karele I, Kikuste I, Lasina I, Tolmanis I, Johnson D, Millstone SZ, Fulton J, Wells JW, Wilf LH, Humbert M, Leja M, Peled N, Haick H. Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules. ACS Nano. 2017 Jan 24;11(1):112-125. doi: 10.1021/acsnano.6b04930. Epub 2016 Dec 21.
PubMed 28000444Zhou J, Huang ZA, Kumar U, Chen DDY. Review of recent developments in determining volatile organic compounds in exhaled breath as biomarkers for lung cancer diagnosis. Anal Chim Acta. 2017 Dec 15;996:1-9. doi: 10.1016/j.aca.2017.09.021. Epub 2017 Sep 13.
PubMed 29137702Arasaradnam RP, Wicaksono A, O'Brien H, Kocher HM, Covington JA, Crnogorac-Jurcevic T. Noninvasive Diagnosis of Pancreatic Cancer Through Detection of Volatile Organic Compounds in Urine. Gastroenterology. 2018 Feb;154(3):485-487.e1. doi: 10.1053/j.gastro.2017.09.054. Epub 2017 Nov 10. No abstract available.
PubMed 29129714Chan DK, Zakko L, Visrodia KH, Leggett CL, Lutzke LS, Clemens MA, Allen JD, Anderson MA, Wang KK. Breath Testing for Barrett's Esophagus Using Exhaled Volatile Organic Compound Profiling With an Electronic Nose Device. Gastroenterology. 2017 Jan;152(1):24-26. doi: 10.1053/j.gastro.2016.11.001. Epub 2016 Nov 5. No abstract available.
PubMed 27825962Gordon SM, Szidon JP, Krotoszynski BK, Gibbons RD, O'Neill HJ. Volatile organic compounds in exhaled air from patients with lung cancer. Clin Chem. 1985 Aug;31(8):1278-82.
PubMed 4017231
Study at a glance
From the registry record, last updated by the sponsor on 24 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Exploratory Study on the Identification of Benign and Malignant Pulmonary Nodules Using Volatile Organic Compounds in Human Exhaled Breath
- ClinicalTrials.gov ID
- NCT06518655
- Acronym / sponsor ID
- LCLN01
- Keywords
- Pulmonary Nodules, Lung Cancer, Volatile Organic Compounds, Human Exhaled Breath
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 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, Renmin Hospital of Wuhan University
- Study start
- 30 Jun 2024 (actual)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 24 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) markers. This model aims to accurately differentiate benign from malignant nodules in individuals harboring pulmonary nodules. 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 distinguishing benign and malignant pulmonary nodules.
- 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.
- 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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pulmonary Nodules | – | Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) systemPre-surgery adult patients with pulmonary nodule found by CT scan. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- People who have pulmonary nodules with a large diameter between 5 mm and 30 mm on a CT scan within six months.
Key inclusion criteria (5 listed)
- 18-80 years old;
- Pulmonary nodules were detected through low-dose spiral CT, chest CT conventional scan, or high-resolution thin-layer CT examination, with a maximum diameter of 5-30 mm, including solid nodules and ground glass nodules;
- Patients require pulmonary nodule resection to define the type of nodule pathology;
- The Patients have not yet used any drugs for tumor treatment;
- Patients and/or family members are able to understand the research protocol and are willing to participate in this study, providing written informed consent.
Key exclusion criteria (12 listed)
- The maximum diameter of pulmonary nodules is greater than 30 mm;
- Patients are unable to determine the pathological diagnosis of pulmonary nodules after surgical resection or biopsy;
- Patients with recurrent lung cancer;
- Patients who have undergone lung transplantation or lobectomy;
- Individuals who currently or have a history of malignant tumors;
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) systemDetection of volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID | Other | Pulmonary Nodules |
Primary outcome
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules.
Time frame: 3 years
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).
1 secondary outcome
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.
Time frame: 3 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 24 Dec 2025.
- Background: PubMed 33538338Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
- Background: PubMed 35143424Xia C, Dong X, Li H, Cao M, Sun D, He S, Yang F, Yan X, Zhang S, Li N, Chen W. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl). 2022 Feb 9;135(5):584-590. doi: 10.1097/CM9.0000000000002108.
- Background: PubMed 27253694Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, Stein KD, Alteri R, Jemal A. Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016 Jul;66(4):271-89. doi: 10.3322/caac.21349. Epub 2016 Jun 2.
- Background: PubMed 21714641National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
- Background: PubMed 28709937Shlomi D, Abud M, Liran O, Bar J, Gai-Mor N, Ilouze M, Onn A, Ben-Nun A, Haick H, Peled N. Detection of Lung Cancer and EGFR Mutation by Electronic Nose System. J Thorac Oncol. 2017 Oct;12(10):1544-1551. doi: 10.1016/j.jtho.2017.06.073. Epub 2017 Jul 12.
- Background: PubMed 29425703van de Goor R, van Hooren M, Dingemans AM, Kremer B, Kross K. Training and Validating a Portable Electronic Nose for Lung Cancer Screening. J Thorac Oncol. 2018 May;13(5):676-681. doi: 10.1016/j.jtho.2018.01.024. Epub 2018 Feb 6.
- Background: PubMed 30128487Hanna GB, Boshier PR, Markar SR, Romano A. Accuracy and Methodologic Challenges of Volatile Organic Compound-Based Exhaled Breath Tests for Cancer Diagnosis: A Systematic Review and Meta-analysis. JAMA Oncol. 2019 Jan 1;5(1):e182815. doi: 10.1001/jamaoncol.2018.2815. Epub 2019 Jan 10.
- Background: PubMed 19567608Horvath I, Lazar Z, Gyulai N, Kollai M, Losonczy G. Exhaled biomarkers in lung cancer. Eur Respir J. 2009 Jul;34(1):261-75. doi: 10.1183/09031936.00142508.
- Background: PubMed 12809740Mitsui T, Kondo T. Inadequacy of theoretical basis of breath methylated alkane contour for assessing oxidative stress. Clin Chim Acta. 2003 Jul 1;333(1):91; author reply 93-4. doi: 10.1016/s0009-8981(03)00173-6. No abstract available.
- Background: PubMed 8377584Stone BG, Besse TJ, Duane WC, Evans CD, DeMaster EG. Effect of regulating cholesterol biosynthesis on breath isoprene excretion in men. Lipids. 1993 Aug;28(8):705-8. doi: 10.1007/BF02535990.
- Background: PubMed 28000444Nakhleh MK, Amal H, Jeries R, Broza YY, Aboud M, Gharra A, Ivgi H, Khatib S, Badarneh S, Har-Shai L, Glass-Marmor L, Lejbkowicz I, Miller A, Badarny S, Winer R, Finberg J, Cohen-Kaminsky S, Perros F, Montani D, Girerd B, Garcia G, Simonneau G, Nakhoul F, Baram S, Salim R, Hakim M, Gruber M, Ronen O, Marshak T, Doweck I, Nativ O, Bahouth Z, Shi DY, Zhang W, Hua QL, Pan YY, Tao L, Liu H, Karban A, Koifman E, Rainis T, Skapars R, Sivins A, Ancans G, Liepniece-Karele I, Kikuste I, Lasina I, Tolmanis I, Johnson D, Millstone SZ, Fulton J, Wells JW, Wilf LH, Humbert M, Leja M, Peled N, Haick H. Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules. ACS Nano. 2017 Jan 24;11(1):112-125. doi: 10.1021/acsnano.6b04930. Epub 2016 Dec 21.
- Background: PubMed 29137702Zhou J, Huang ZA, Kumar U, Chen DDY. Review of recent developments in determining volatile organic compounds in exhaled breath as biomarkers for lung cancer diagnosis. Anal Chim Acta. 2017 Dec 15;996:1-9. doi: 10.1016/j.aca.2017.09.021. Epub 2017 Sep 13.
- 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
15 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Nodules, Multiple and Pulmonary Nodules, Solitary, run by ChromX Health at 15 sites 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 5 inclusion and 12 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 24 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 15 sites 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 distinguishing benign and malignant pulmonary nodules. (time frame: 3 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 NCT06518655, which also lists 20 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 24 Dec 2025.