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Benign/Malignant Pulmonary Nodule Classification Based on High-throughput Whole-genome Methylation Sequencing(GM-seq)

Pulmonary Nodule, Solitary Clinical Trial

Official title:
Development a Pulmonary Nodules Diagnosis Classification Model for Benign/Malignant of Bronchoscopic Biopsy Specimens Based on High-throughput Whole-genome Methylation Sequencing(GM-seq)

Clinical Trial Summary

Lung cancer is the first cancer in China in terms of morbidity and mortality. The problem of early diagnosis/treatment has always been concerned. The popularization of chest CT (electronic computed tomography) screening makes it possible to detect lung cancer early. However, the diagnosis still needs pathological evidence. It is an ideal choice to obtain pathological evidence through bronchoscope and other minimally invasive means before surgical resection. However, the positive rate of tracheoscopy is still unsatisfactory, which is related to the difficulty of traditional pathological detection in detecting small specimens obtained by tracheoscopy. Liquid biopsy technology based on methylation detection has been used in early cancer screening, but its advantages have not been fully exploited due to the low content of ctDNA (circulating tumor DNA) in the current detection samples. Therefore, through prospective clinical research, the investigators plan to combine the methylation detection technology based on "Whole genome methylation sequencing(GM-seq)" with tracheoscopy, compare the traditional pathological methods with methylation detection on the bronchoscopic samples of lung nodule subjects suspected of early lung cancer, and take the postoperative pathology as the gold standard for judging benign and malignant, to confirm the feasibility and advantages of the new technology.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT05415670
Study type Observational
Source Geneplus-Beijing Co. Ltd.
Contact Wei Zhou, Doctor
Phone 13911295918
Email zhouwei3090@bjhmoh.cn
Status Recruiting
Phase
Start date July 1, 2023
Completion date June 1, 2024
View Details
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