Differentiation of Benign and Malignant Pulmonary Nodules by Volatile Organic Compounds in Human Exhaled Breath
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-80
SponsorChromX Health
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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;
Disqualifiers
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;
Trial population
People who have pulmonary nodules with a large diameter between 5 mm and 30 mm on a CT scan within six months.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system
Other interventionDetection of volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
Treatment groups
3,000 Participants
are divided into 1 treatment groupGroup A: Pulmonary Nodules1 intervention
Trial outcomes
Primary outcomes
1
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in distinguishing benign and malignant pulmonary nodules.
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).
Time frame
3 yearsSecondary outcomes
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.
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).
Time frame
3 yearsOther outcomes
1
Establish an exhaled breath VOC model for predicting EGFR mutations in malignant pulmonary nodules.
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.
Time frame
3 yearsSponsors and contacts
Click on the lead sponsor to view all of their trials.
ChromX Health
Lead sponsor
The First Affiliated Hospital of Guangzhou Medical University
Collaborator
First People's Hospital of Foshan
Collaborator
Sichuan Cancer Hospital and Research Institute
Collaborator
Liwan District Central Hospital
Collaborator
Shanghai Chest Hospital
Collaborator
Peking Union Medical College Hospital
Collaborator
Guangzhou Development Zone Hospital
Collaborator
Huangpu District Hongshan Street Community Health Service Center
Collaborator
Huangpu District Chinese Medicine Hospital
Collaborator
Fifth Affiliated Hospital of Guangzhou Medical University
Collaborator
Huangpu District Jiufo Street Community Health Service Center
Collaborator
Huangpu District Xinlong Town Central Hospital
Collaborator
Huangpu District Yonghe Street Community Health Service Center
Collaborator
Huangpu District Lianhe Street Second Community Health Service Center
Collaborator
Renmin Hospital of Wuhan University
Collaborator
Source ClinicalTrials.gov