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Risk-Based Lung Cancer Detection in COPD Patients
Sponsor: Vejle Hospital
Summary
The goal of the interventional study is to evaluate whether risk-based stratification using the PLCOm2012 model and a machine learning (ML) model can identify patients with chronic obstructive pulmonary disease (COPD) who are at high risk of developing lung cancer and may benefit from low-dose computed tomography (LDCT) screening. The study population includes adults aged 50 years and older with COPD and a history of smoking attending an outpatient clinic. The main question it aims to answer are: \- What is the incidence of histopathologically confirmed lung cancer following risk-based stratification? Participants will: * Undergo lung cancer risk assessment using the PLCOm2012 model and an ML-based model based on clinical and laboratory data * Be referred for LDCT if classified as high-risk * Continue standard care if classified as low-risk * Be followed through electronic health records for up to six years to assess outcomes including lung cancer incidence, adherence to LDCT, time to imaging, healthcare utilization, costs, and safety
Official title: Early Lung Cancer Detection in High-Risk Patients With Risk-Based Machine Learning Models and Biomarkers
Key Details
Gender
All
Age Range
50 Years - Any
Study Type
INTERVENTIONAL
Enrollment
1000
Start Date
2026-06-01
Completion Date
2033-12
Last Updated
2026-09-30
Healthy Volunteers
No
Interventions
Risk-based prediction models
Patients will be stratified into high-risk and low-risk groups using both the Lung Cancer Risk Prediction Calculator for smokers (PLCOm2012) and an in-house developed machine learning model based on sex, age, smoking status, and laboratory data from routine blood sample analyses.
Locations (1)
Vejle Hospital, University Hospital of Southern Denmark
Vejle, Region Syddanmark, Denmark