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

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. 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

• Pulmonary Nodules, Multiple
• Pulmonary Nodules, Solitary
• Lung Cancer

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

Sponsors & Collaborators

• The First Affiliated Hospital of Guangzhou Medical University

  collaborator OTHER

• First People's Hospital of Foshan

  collaborator OTHER

• Sichuan Cancer Hospital and Research Institute

  collaborator OTHER

• Liwan District Central Hospital

  collaborator UNKNOWN

• Shanghai Chest Hospital

  collaborator OTHER

• Peking Union Medical College Hospital

  collaborator OTHER

• Guangzhou Development Zone Hospital

  collaborator UNKNOWN

• Huangpu District Hongshan Street Community Health Service Center

  collaborator UNKNOWN

• Huangpu District Chinese Medicine Hospital

  collaborator UNKNOWN

• Fifth Affiliated Hospital of Guangzhou Medical University

  collaborator OTHER

• Huangpu District Jiufo Street Community Health Service Center

  collaborator UNKNOWN

• Huangpu District Xinlong Town Central Hospital

  collaborator UNKNOWN

• Huangpu District Yonghe Street Community Health Service Center

  collaborator UNKNOWN

• Huangpu District Lianhe Street Second Community Health Service Center

  collaborator UNKNOWN

• Renmin Hospital of Wuhan University

  collaborator OTHER

• ChromX Health

  lead INDUSTRY

Principal Investigators

• Jianxing He, MD · The First Affiliated Hospital of Guangzhou Medical University

Eligibility

Min Age

18 Years

Max Age

80 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-06-30

Primary Completion

2026-12-30

Completion

2027-06-30

Countries

• China

Study Locations