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

December 18, 2025 updated by: ChromX Health

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:

  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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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 Type

Observational

Enrollment (Estimated)

3000

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

Study Locations

  • China
    • Beijing Municipality
      • Beijing, Beijing Municipality, China
        • Recruiting
        • Peking Union Medical College Hospital
        • Contact:
          • Qian Wang, MD
    • Guangdong
      • Foshan, Guangdong, China, 528000
        • Recruiting
        • First People's Hospital of Foshan
        • Contact:
          • Zhuxing Chen
      • Guangzhou, Guangdong, China
        • Recruiting
        • The Fifth Affiliated Hospital of Guangzhou Medical University
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China, 510140
        • Recruiting
        • The First Affiliated Hospital of Guangzhou Medical University
        • Contact:
      • Guangzhou, Guangdong, China, 510175
        • Recruiting
        • Liwan District Central Hospital
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China
        • Recruiting
        • Guangzhou Development Zone Hospital
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China
        • Recruiting
        • Huangpu District Chinese Medicine Hospital
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China
        • Recruiting
        • Huangpu District Hongshan Street Community Health Service Center
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China
        • Recruiting
        • Huangpu District Jiufo Street Community Health Service Center
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China
        • Recruiting
        • Huangpu District Lianhe Street Second Community Health Service Center
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China
        • Recruiting
        • Huangpu District Xinlong Town Central Hospital
        • Contact:
          • Hengrui Liang, MD
      • Guangzhou, Guangdong, China
        • Recruiting
        • Huangpu District Yonghe Street Community Health Service Center
        • Contact:
          • Hengrui Liang, MD
    • Hubei
      • Wuhan, Hubei, China
        • Recruiting
        • Renmin Hospital of Wuhan University
        • Contact:
          • Huiqing Lin, MD
    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, China
        • Recruiting
        • Shanghai Chest Hospital
        • Contact:
          • Yanwei Zhang, MD
    • Sichuan
      • Chengdu, Sichuan, China, 610042
        • Recruiting
        • Sichuan Cancer Hospital
        • Contact:
          • Bo Tian, MD
          • Phone Number: +86 18980053101

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.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability Sample

Study Population

People who have pulmonary nodules with a large diameter between 5 mm and 30 mm on a CT scan within six months.

Description

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.

Study Plan

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

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Pulmonary Nodules
Pre-surgery adult patients with pulmonary nodule found by CT scan.
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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).
3 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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
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).
3 years

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Establish an exhaled breath VOC model for predicting EGFR mutations in malignant pulmonary nodules.
Time Frame: 3 years
Establish an exhaled breath VOC model for predicting EGFR mutations in pathologically confirmed malignant pulmonary nodules. And evaluate the prediction accuracy by comparing the results of EGFR gene testing.
3 years

Collaborators and Investigators

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

Sponsor

ChromX Health

Collaborators

Peking Union Medical College Hospital
Shanghai Chest Hospital
The First Affiliated Hospital of Guangzhou Medical University
Renmin Hospital of Wuhan University
First People's Hospital of Foshan
Sichuan Cancer Hospital and Research Institute
Fifth Affiliated Hospital of Guangzhou Medical University
Liwan District Central Hospital
Guangzhou Development Zone Hospital
Huangpu District Hongshan Street Community Health Service Center
Huangpu District Chinese Medicine Hospital
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 and helpful links

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

General Publications

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 Major Dates

Study Start (Actual)

June 30, 2024

Primary Completion (Estimated)

December 30, 2026

Study Completion (Estimated)

June 30, 2027

Study Registration Dates

First Submitted

July 19, 2024

First Submitted That Met QC Criteria

July 19, 2024

First Posted (Actual)

July 24, 2024

Study Record Updates

Last Update Posted (Actual)

December 24, 2025

Last Update Submitted That Met QC Criteria

December 18, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • LCLN01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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