Clinical trial · Observational
Establishing a Longitudinal Cohort Study of Lung Cancer Using Tissue and Peripheral Blood Metabolomics.
Establishing a Longitudinal Cohort Study of Lung Cancer Using Tissue and Peripheral Blood Metabolomics to Explore Biomarkers and Therapeutic Mechanisms.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung | — | UNRESOLVED | — |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
| Lung Cancer (NSCLC) | Malignant Lung Neoplasm | CURATED_EXACT | 0.85 |
| Metabolomics | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Monitoring serum metabolites in lung cancer patients using tissue and peripheral blood samples. | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Area Under the Curve
- timeFrame
- 3 Years
- description
- 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 outcomes (1)
- measure
- Differentially Expressed Metabolites
- timeFrame
- 3 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available