LcProt: Proteomics Longitudinal Cohort Study on Lung Cancer
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-75
SponsorThe First Affiliated Hospital of Guangzhou Medical University
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Signing of the informed consent form;
Male or female, aged 18-75 years;
Patients with lung nodules confirmed by CT examination;
Good preoperative pulmonary function cooperation and complete reporting;
Disqualifiers
Poor preoperative pulmonary function cooperation or missing reports;
Preoperative chest single/dual phase CT scans exhibit significant artefacts or image omission;
The interval between preoperative pulmonary function and single/dual phase CT scans exceeds one month;
Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.);
Trial population
Patients with lung nodules confirmed by CT examination.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Using 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
Other interventionPeripheral 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.
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
Area Under the Curve
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).
Time frame
3 yearsSecondary outcomes
1
Differentially Expressed Proteins
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.
Time frame
3 yearsSponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov