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Recruiting NCT06518655

Differentiation of Benign and Malignant Pulmonary Nodules by Volatile Organic Compounds in Human Exhaled Breath

— Observational Pulmonary Nodules, Multiple Pulmonary Nodules, Solitary Lung Cancer

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system.
Who it may be relevant to
Registry conditions: Pulmonary Nodules, Multiple, Pulmonary Nodules, Solitary, Lung Cancer. Basic parameters: 18 years — 80 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

Exploratory Study on the Identification of Benign and Malignant Pulmonary Nodules Using Volatile Organic Compounds in Human Exhaled Breath

Overview

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.

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.

Interventions

  • Other Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
    Detection of volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID

Primary outcome measures

  • 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]
Secondary outcome measures (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. [Time frame: 3 years]

Eligibility criteria

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Cohort

Study locations

China · 15 centers
  • Peking Union Medical College Hospital — Beijing
  • First People's Hospital of Foshan — Foshan
  • The First Affiliated Hospital of Guangzhou Medical University — Guangzhou
  • Liwan District Central Hospital — Guangzhou
  • Guangzhou Development Zone Hospital — Guangzhou
  • Huangpu District Chinese Medicine Hospital — Guangzhou
  • Huangpu District Hongshan Street Community Health Service Center — Guangzhou
  • Huangpu District Jiufo Street Community Health Service Center — Guangzhou
  • … and 7 more centers

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. PMID 33538338
  • Xia 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
  • Miller 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
  • National 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
  • Shlomi 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
  • van 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
  • Hanna 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. PMID 30128487
  • Horvath 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. PMID 19567608

Identifiers

NCT: NCT06518655 · LCLN01

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗