Lung Cancer, Pulmonary Nodules, Multiple, Pulmonary Nodules, Solitary
Conditions
Keywords
Pulmonary Nodules, Lung Cancer, Volatile Organic Compounds, Human Exhaled Breath
Brief 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.
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
Detection of volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
Sponsors
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
ChromX Health
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules. | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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. | 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). |
Other
| Measure | Time frame | Description |
|---|---|---|
| Establish an exhaled breath VOC model for predicting EGFR mutations in malignant pulmonary nodules. | 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. |
Countries
China
Contacts
Primary ContactHengrui Liang, MD
Outcome results
None listed