Clinical trial · Interventional
Risk-Based Lung Cancer Detection in COPD Patients
Early Lung Cancer Detection in High-Risk Patients With Risk-Based Machine Learning Models and Biomarkers
- Source
- ClinicalTrials.gov
- Retrieved
- Oct 1, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20261001-000001
Summary
Brief summary (as posted)
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
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| COPD (Chronic Obstructive Pulmonary Disease) | — | UNRESOLVED | — |
| Lung Cancer (Diagnosis) | Malignant Lung Neoplasm | CURATED_EXACT | 0.85 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Risk-based prediction models | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- COPD patients undergoing lung cancer risk assessment
- description
- All enrolled patients with chronic obstructive pulmonary disease (COPD) will undergo lung cancer risk assessment using the PLCOm2012 model and an in-house developed machine learning model. Based on the model-derived risk score, patients will be stratified into high-risk and low-risk groups. Patients classified as high-risk will undergo further diagnostic evaluation for lung cancer according to the study protocol. Patients classified as low-risk will continue with standard of care management. Outcomes will be compared between risk groups to evaluate the feasibility and clinical utility of the model.
- interventionNames
- Other: Risk-based prediction models
Primary outcomes (1)
- measure
- Number of histopathologically confirmed lung cancers
- timeFrame
- From risk assessment to end of follow-up at 6 years
Secondary outcomes (10)
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 50 Years
Show eligibility criteria text
Inclusion Criteria: * Diagnosed with COPD * Age ≥ 50 years * Attendance at the outpatient Respiratory Medicine clinic, Vejle Hospital, University Hospital of Southern Denmark * Smoking history (current or former smoker) * Consent to translational research and biobank. Exclusion Criteria: * Previous diagnosis of lung cancer within the last five years. * Active treatment for cancer within the last 12 months except non-melanoma skin cancer and carcinoma in situ cervicis uteri. * Invasive methods to verify potential lung cancer not an option. * Inability to provide informed consent.
References
Publications (1)
- BACKGROUNDBang Henriksen M, Hansen TF, Jensen LH, Brasen CL, Borg M, Hilberg O, Lokke A. Lung cancer among outpatients with COPD: a 7-year cohort study. ERJ Open Res. 2024 Jul 22;10(4):00064-2024. doi: 10.1183/23120541.00064-2024. eCollection 2024 Jul. PMID 39040576