Study of Blood and Imaging Biomarkers to Improve Early Diagnosis of Lung Cancer in Patients with Lung Nodules

What this Trial Is About

Researchers are evaluating the use of artificial intelligence and machine learning-based radiomics to distinguish between benign and malignant lung nodules in patients who have previously been treated for cancer. The study focuses on incidental lung nodules detected during routine care, aiming to improve early diagnosis of lung cancer and differentiate between metastatic recurrence and new primary lung cancer. This observational study seeks to enhance current risk models by integrating imaging and blood biomarker data. Participants under active investigation or surveillance for incidental lung nodules will be included. The study involves collecting blood samples for high-dimensional immune phenotyping and analyzing imaging data to develop biomarkers that predict cancer risk. The study also explores combining blood and imaging biomarkers to improve malignancy prediction. This research builds on previous lung screening programs and aims to reduce unnecessary imaging and invasive procedures. During the study, participants will undergo regular CT scans and blood sampling to monitor lung nodules and immune profiles over time. Researchers will assess immune phenotypes and imaging characteristics to identify markers associated with cancer development or relapse. The primary and secondary outcomes will be evaluated over a 10-year period, with long-term follow-up to track changes in risk scores after tumor treatment or regression. This approach aims to support more personalized surveillance strategies for lung nodules.

Nodule IMmunophenotyping Biomarker for Lung Cancer Early Diagnosis Study