Clinical trial NCT06518655: Differentiation of Benign and Malignant Pulmonary Nodules by Volatile Organic Compounds in Human Exhaled Breath
Official title: Exploratory Study on the Identification of Benign and Malignant Pulmonary Nodules Using Volatile Organic Compounds in Human Exhaled Breath
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Pulmonary Nodules, Multiple
Pulmonary Nodules, Solitary
Lung Cancer
Keywords: Pulmonary Nodules
Lung Cancer
Volatile Organic Compounds
Human Exhaled Breath
Lead sponsor: ChromX Health
Sponsor class: INDUSTRY
Collaborators: name: The First Affiliated Hospital of Guangzhou Medical University
name: First People's Hospital of Foshan
name: Sichuan Cancer Hospital and Research Institute
name: Liwan District Central Hospital
name: Shanghai Chest Hospital
name: Peking Union Medical College Hospital
name: Guangzhou Development Zone Hospital
name: Huangpu District Hongshan Street Community Health Service Center
name: Huangpu District Chinese Medicine Hospital
name: Fifth Affiliated Hospital of Guangzhou Medical University
name: Huangpu District Jiufo Street Community Health Service Center
name: Huangpu District Xinlong Town Central Hospital
name: Huangpu District Yonghe Street Community Health Service Center
name: Huangpu District Lianhe Street Second Community Health Service Center
First posted: 2024-07-24
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

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.

Primary outcomes:
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
Secondary outcomes:
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.
timeFrame: 3 years
Other outcomes:
measure: Establish an exhaled breath VOC model for predicting EGFR mutations in malignant pulmonary nodules.
timeFrame: 3 years
Locations:
city: Peking
country: China
facility: Peking Union Medical College Hospital
state: Beijing
city: Foshan
country: China
facility: First People's Hospital of Foshan
state: Guangdong
city: Guangzhou
country: China
facility: The First Affiliated Hospital of Guangzhou Medical University
state: Guangdong
city: Guangzhou
country: China
facility: Liwan District Central Hospital
state: Guangdong
city: Guangzhou
country: China
facility: Guangzhou Development Zone Hospital
state: Guangdong
city: Guangzhou
country: China
facility: Huangpu District Chinese Medicine Hospital
state: Guangdong
city: Guangzhou
country: China
facility: Huangpu District Hongshan Street Community Health Service Center
state: Guangdong
city: Guangzhou
country: China
facility: Huangpu District Jiufo Street Community Health Service Center
state: Guangdong
city: Guangzhou
country: China
facility: Huangpu District Lianhe Street Second Community Health Service Center
state: Guangdong
city: Guangzhou
country: China
facility: Huangpu District Xinlong Town Central Hospital
state: Guangdong
city: Guangzhou
country: China
facility: Huangpu District Yonghe Street Community Health Service Center
state: Guangdong
city: Guangzhou
country: China
facility: The Fifth Affiliated Hospital of Guangzhou Medical University
state: Guangdong
city: Shanghai
country: China
facility: Shanghai Chest Hospital
state: Shanghai
city: Chengdu
country: China
facility: Sichuan Cancer Hospital
state: Sichuan
Study officials:
affiliation: The First Affiliated Hospital of Guangzhou Medical University
name: Jianxing He, MD
role: STUDY_CHAIR
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