Clinical trial · Observational
Differentiation of Benign and Malignant Pulmonary Nodules by Volatile Organic Compounds in Human Exhaled Breath
Exploratory Study on the Identification of Benign and Malignant Pulmonary Nodules Using Volatile Organic Compounds in Human Exhaled Breath
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
| Pulmonary Nodules, Multiple | — | UNRESOLVED | — |
| Pulmonary Nodules, Solitary | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Pulmonary Nodules
- description
- Pre-surgery adult patients with pulmonary nodule found by CT scan.
- interventionNames
- Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
Primary outcomes (1)
- measure
- The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules.
- timeFrame
- 3 years
- description
- 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).
Secondary outcomes (1)
- measure
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: * 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. Exclusion Criteria: * 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; * 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.
References
Publications (20)
- BACKGROUNDSung 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. PMID 33538338
- BACKGROUNDXia 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. PMID 35143424
- BACKGROUNDMiller 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. PMID 27253694
- BACKGROUNDNational 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. PMID 21714641
- BACKGROUNDShlomi 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. PMID 28709937
- BACKGROUNDvan 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. PMID 29425703
- BACKGROUNDHanna 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.