Cancer, Lung, Lung Cancer, NSCLC
Conditions
Keywords
Lung cancer, Proteomics, Protein, NSCLC
Brief summary
This study will utilize tissue and peripheral blood samples for proteomics analysis and establish a longitudinal proteomics cohort at multiple critical treatment time points to explore the research value of proteomics in the diagnosis and treatment of lung cancer. The study includes key time points such as screening, postoperative efficacy prediction, and efficacy prediction after medication.
Interventions
OTHERUsing tissue and peripheral blood proteomics to distinguish the benign and malignant nature of lung cancer in patients, as well as to evaluate therapeutic efficacy and long-term prognosis during the t
Peripheral blood samples from enrolled participants will be drawn, or lesion tissues will be obtained through procedures such as biopsy or surgery, followed by quantitative proteomics analysis using mass spectrometry.
Sponsors
The First Affiliated Hospital of Guangzhou Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. In clinical studies, AUC is often used to assess diagnostic tests, where a higher AUC indicates better test accuracy in distinguishing between conditions (e.g., disease vs. no disease). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Differentially Expressed Proteins | 3 years | Differential proteins, or differentially expressed proteins (DEPs), refer to proteins that show significant changes in expression levels between different biological or experimental conditions, such as disease vs. healthy states, treated vs. untreated groups, or across time points in longitudinal studies. These proteins are identified through quantitative proteomics techniques, including mass spectrometry or label-free methods, and analyzed using statistical or bioinformatics tools to determine significance. |
Countries
China
Contacts
Primary ContactJianxing He, Professor
Outcome results
None listed