Establishing a Longitudinal Cohort Study of Lung Cancer Using Tissue and Peripheral Blood Metabolomics.

Recruiting

• Conditions: Lung Cancer; Lung; Lung Cancer (NSCLC); Metabolomics

• Registration Number: NCT06843707
• Lead Sponsor: The First Affiliated Hospital of Guangzhou Medical University

Brief Summary

This study will utilize tissue and peripheral blood samples for metabolomics analysis and establish a longitudinal metabolomics cohort at multiple critical treatment time points to comprehensively investigate the role of metabolomics in the diagnosis, prognosis, and therapeutic monitoring of lung cancer. By profiling metabolic alterations, this study aims to identify potential biomarkers for distinguishing benign and malignant lung nodules, predicting therapeutic efficacy, and assessing long-term prognosis. Key time points include initial screening for lung nodules, postoperative evaluation to predict treatment outcomes, and therapeutic monitoring to assess efficacy after medication or other interventions. Through these analyses, the study seeks to uncover underlying metabolic mechanisms and provide valuable insights into personalized lung cancer management.

Detailed Description

Not available

Study Timeline

• Study Start: 2024-01-01
• Status Verified: 2025-01
• Study First Submitted: 2025-01-12
• Study First Submitted QC: 2025-02-24
• Last Update Submitted: 2025-02-24
• Study First Posted: 2025-02-25
• Last Update Posted: 2025-02-25
• Primary Completion: 2026-12-31
• Study Completion: 2027-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 2500

Inclusion Criteria

1. Signing of the informed consent form;
2. Male or female, aged 18-75 years;
3. Patients with lung nodules confirmed by CT examination;
4. Good preoperative pulmonary function cooperation and complete reporting;
5. Preoperative chest single/dual phase CT scans without significant artefacts and with complete imaging;
6. The interval between preoperative pulmonary function and single/dual phase CT scans does not exceed one month.

Exclusion Criteria

1. Poor preoperative pulmonary function cooperation or missing reports;
2. Preoperative chest single/dual phase CT scans exhibit significant artefacts or image omission;
3. The interval between preoperative pulmonary function and single/dual phase CT scans exceeds one month;
4. Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.);
5. Coexisting with other severe functional impairments;
6. Patients with obstructive lesions such as airway or esophageal stenosis;

(8) Medication use before pulmonary function testing that does not meet the cessation guidelines; (9) Pulmonary function report quality graded D-F.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area Under the Curve	3 Years	AUC, or Area Under the Curve, is a commonly used metric in statistical and machine learning models, particularly for evaluating the performance of classification models. It refers to the area under the Receiver Operating Characteristic (ROC) curve, which plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. An AUC value ranges from 0 to 1, where: * 1 indicates a perfect model,; * 0.5 suggests a model no better than random guessing,; * \< 0.5 reflects a model performing worse than random.

Secondary Outcome Measures

Name	Time	Method
Differentially Expressed Metabolites	3 years	Differential metabolites, or differentially expressed metabolites (DEMs), refer to metabolites that show significant changes in abundance between different biological or experimental conditions, such as disease vs. healthy states, treated vs. untreated groups, or across time points in longitudinal studies. These metabolites are identified through quantitative metabolomics techniques, including mass spectrometry or nuclear magnetic resonance (NMR), and analyzed using statistical or bioinformatics tools to determine significance.

Trial Locations

Locations (1)

the First Affiliated of Guangzhou Medical University; 🇨🇳 Guangzhou, Guangdong, China