Benign/Malignant Pulmonary Nodule Classification Based on High-throughput Whole-genome Methylation Sequencing(GM-seq)
NCT ID: NCT05415670
Last Updated: 2025-09-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
158 participants
OBSERVATIONAL
2023-07-01
2025-08-01
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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[Training set, N=80] Benign/Malignant Pulmonary Nodule
This is a prospective training-set cohort study. A stratified case-cohort design will be used to select patients with malignant pulmonary nodules and patients with benign pulmonary nodules for analysis. All participants will receive chest CT or low-dose computed tomography (LD-CT) scanning and detection of serum tumor markers, and receive Whole-genome methylation sequencing at baseline. GM-seq will perform methylation analysis to build a prediction model for benign and malignant classification.
Whole-genome Methylation Sequencing(GM-seq)
A Whole-genome Methylation detection method, which can analyze the genome-wide, single base resolution methylation of tissue / blood samples, and is used to develop a benign and malignant classification model for Pulmonary Nodule.
[Verification set, N=40] Benign/Malignant Pulmonary Nodule
This is a prospective validation-set cohort study. A stratified case-cohort design was used to select patients with malignant pulmonary nodules and patients with benign pulmonary nodules for analysis. All participants will verify the benign and malignant differentiation model based on GM-seq methylation analysis, and compare the results with histopathological benign and malignant results, so as to develop a clinical benign and malignant differentiation model.
Whole-genome Methylation Sequencing(GM-seq)
A Whole-genome Methylation detection method, which can analyze the genome-wide, single base resolution methylation of tissue / blood samples, and is used to develop a benign and malignant classification model for Pulmonary Nodule.
Interventions
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Whole-genome Methylation Sequencing(GM-seq)
A Whole-genome Methylation detection method, which can analyze the genome-wide, single base resolution methylation of tissue / blood samples, and is used to develop a benign and malignant classification model for Pulmonary Nodule.
Eligibility Criteria
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Inclusion Criteria
2. The nodules are single or multiple, suspected to be malignant, and have the indication of surgical resection;
3. Patient accept imaging evaluation without advanced lung tumors and metastases;
4. The location of the nodule in the lung is within the reach of lung biopsy under bronchoscope;
5. provide the collected clinical data needed by the research;
6. Patients have the ability to follow the planned schedule and actively cooperate to return to the hospital for regular clinical visits.
Exclusion Criteria
2. Contraindication of tracheoscopy;
3. Consider that the pulmonary nodules are metastatic tumors or unresectable advanced lung cancer;
4. Those who cannot tolerate resection of pulmonary nodules;
5. Accompanied by other malignant tumors;
6. In the judgment of the researcher, the patient also suffers from other serious diseases that may affect the accuracy of the test;
7. Those who cannot accept the use of contrast-enhanced magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT);
8. Any other illness, social / psychological problems, etc. are judged by the researcher to be unsuitable for participating in this study.
20 Years
75 Years
ALL
Yes
Sponsors
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Beijing Hospital
OTHER_GOV
Emergency General Hospital
OTHER
Geneplus-Beijing Co. Ltd.
INDUSTRY
Responsible Party
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Principal Investigators
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Wei Zhou, Doctor
Role: STUDY_CHAIR
Beijing Hospital
Yunzhi Zhou, Doctor
Role: STUDY_DIRECTOR
Emergency General Hospital
Locations
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Emergency general hospital
Beijing, Beijing Municipality, China
Beijing hospital
Beijing, Beijing Municipality, China
Countries
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Other Identifiers
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GM-Lung diagnosis
Identifier Type: -
Identifier Source: org_study_id
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