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

Study Overview
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Brief Summary
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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.

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

Conditions
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Pulmonary Nodule, Solitary, Whole-genome Methylation Sequencing

Intervention / Treatment
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* Diagnostic Test: Whole-genome Methylation Sequencing(GM-seq)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Male or female, 20-75 year-old with pulmonary nodules 1-3cm in diameter confirmed by chest CT; The nodules are single or multiple, suspected to be malignant, and have the indication of surgical resection; Patient accept imaging evaluation without advanced lung tumors and metastases; The location of the nodule in the lung is within the reach of lung biopsy under bronchoscope; provide the collected clinical data needed by the research; Patients have the ability to follow the planned schedule and actively cooperate to return to the hospital for regular clinical visits. Exclusion criteria: Unwilling to accept the invasive examination and treatment of this study; Contraindication of tracheoscopy; Consider that the pulmonary nodules are metastatic tumors or unresectable advanced lung cancer; Those who cannot tolerate resection of pulmonary nodules; Accompanied by other malignant tumors; In the judgment of the researcher, the patient also suffers from other serious diseases that may affect the accuracy of the test; Those who cannot accept the use of contrast-enhanced magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT); Any other illness, social / psychological problems, etc. are judged by the researcher to be unsuitable for participating in this study.

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| [Training set, N=80] Benign/Malignant Pulmonary Nodule<br>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. | Diagnostic Test: Whole-genome Methylation Sequencing(GM-seq)<br>* 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.<br>|
| [Verification set, N=40] Benign/Malignant Pulmonary Nodule<br>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. | Diagnostic Test: Whole-genome Methylation Sequencing(GM-seq)<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area under the receiver operating characteristic curve (ROC) | Area under curve (AUC) of GM-seq data in discriminating malignant nodules from benign nodules. | 2 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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ctDNA, Pulmonary Nodule, Solitary, Whole-genome Methylation Sequencing(GM-seq), Machine learning