[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100658226":3},{"organization":4,"outcomesModule":7,"designInfo":35,"detailedDescription":41,"studyPopulation":11,"armGroups":42,"interventions":49,"overallOfficials":54,"centralContacts":59,"locations":65,"responsibleParty":81,"collaborators":11,"id":83,"slug":84,"hasResults":85,"nctId":86,"briefTitle":87,"officialTitle":88,"acronym":89,"eligibilityCriteria":90,"healthyVolunteers":85,"sex":91,"minAge":92,"maxAge":11,"enrollmentInfo":93,"targetDuration":11,"studyType":96,"phases":97,"briefSummary":98,"conditions":99,"keywords":102,"overallStatus":67,"whyStopped":11,"lastUpdateSubmitDate":108,"lastUpdatePostDateStruct":109,"startDateStruct":112,"completionDateStruct":114,"leadSponsor":116,"locationsCount":117},{"fullName":5,"class":6},"Vejle Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":11},[9],{"measure":10,"description":11,"timeFrame":12},"Number of histopathologically confirmed lung cancers",null,"From risk assessment to end of follow-up at 6 years",[14,17,19,21,23,25,27,29,31,33],{"measure":15,"description":11,"timeFrame":16},"Proportion of high-risk patients who undergo LDCT after referral","From risk assessment to LDCT completion at 1 year",{"measure":18,"description":11,"timeFrame":12},"Number of COPD patients requiring risk assessment and LDCT screening to detect one case of lung cancer",{"measure":20,"description":11,"timeFrame":16},"Time from risk assessment to completion of LDCT",{"measure":22,"description":11,"timeFrame":12},"Number of diagnostic procedures per patient stratified by risk group",{"measure":24,"description":11,"timeFrame":12},"Number of hospitalizations per patient stratified by risk group",{"measure":26,"description":11,"timeFrame":12},"Number of outpatient visits per patient stratified by risk group",{"measure":28,"description":11,"timeFrame":12},"Healthcare costs per detected lung cancer case stratified by risk group",{"measure":30,"description":11,"timeFrame":12},"Healthcare costs per patient stratified by risk group",{"measure":32,"description":11,"timeFrame":12},"Proportion of eligible COPD patients consenting to risk assessment",{"measure":34,"description":11,"timeFrame":16},"Proportion of eligible COPD patients completing LDCT",{"allocation":36,"interventionModel":37,"interventionModelDescription":11,"primaryPurpose":38,"observationalModel":11,"timePerspective":11,"maskingInfo":39},"NA","SINGLE_GROUP","DIAGNOSTIC",{"masking":40,"maskingDescription":11,"whoMasked":11},"NONE","Lung cancer is a leading cause of cancer-related mortality in Denmark, largely due to late-stage diagnosis. Early detection through low-dose computed tomography (LDCT) has been shown to reduce mortality in trials such as the National Lung Screening Trial and the NELSON trial, but implementation remains limited due to cost, capacity, and risk of false-positive findings. Risk-based approaches may improve screening efficiency by identifying individuals at highest risk.\n\nThis prospective, single-centre interventional study evaluates the feasibility and clinical implementation of lung cancer risk stratification in patients with chronic obstructive pulmonary disease (COPD). A total of 1,000 patients will be enrolled from an outpatient clinic. At baseline, participants will undergo risk assessment using the PLCOm2012 model and a machine learning (ML)-based model based on clinical and laboratory data.\n\nPatients identified as high-risk by either model will be referred for LDCT, while low-risk patients will continue standard care. If LDCT findings are suspicious, further diagnostic evaluation will be performed according to routine clinical practice. Participants will be followed through electronic health records for up to six years, with outcome assessment at 1, 2, and 6 years.\n\nThe primary outcome is the incidence of histopathologically confirmed lung cancer. Secondary outcomes include adherence to LDCT referral, time from risk assessment to LDCT, number needed to screen, healthcare utilization, healthcare costs, and safety, including adverse events related to diagnostic procedures.",[43],{"label":44,"type":45,"description":46,"interventionNames":47},"COPD patients undergoing lung cancer risk assessment","EXPERIMENTAL","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.",[48],"Other: Risk-based prediction models",[50],{"type":6,"name":51,"description":52,"armGroupLabels":53,"otherNames":11},"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.",[44],[55],{"name":56,"affiliation":57,"role":58},"Morten H. Borg, MD, PhD","Vejle Hospital, University Hospital of Southern Denmark","STUDY_DIRECTOR",[60],{"name":61,"role":62,"phone":63,"phoneExt":11,"email":64},"Cecilie M. Jacobsen, MScEng","CONTACT","+4579406644","cecilie.mondrup.jacobsen@rsyd.dk",[66],{"facility":57,"status":67,"city":68,"state":69,"zip":70,"country":71,"countryCode":72,"cosmosGeoPoint":73,"geoPoint":78,"contacts":79},"RECRUITING","Vejle","Region Syddanmark","7100","Denmark","DK",{"type":74,"coordinates":75},"Point",[76,77],9.5357,55.70927,{"lat":77,"lon":76},[80],{"name":61,"role":62,"phone":63,"phoneExt":11,"email":64},{"type":82,"investigatorFullName":11,"investigatorTitle":11,"investigatorAffiliation":11,"oldNameTitle":11,"oldOrganization":11},"SPONSOR","100658226","risk-based-lung-cancer-detection-in-copd-patients-100658226",false,"NCT07848815","Risk-Based Lung Cancer Detection in COPD Patients","Early Lung Cancer Detection in High-Risk Patients With Risk-Based Machine Learning Models and Biomarkers","ELUCID","Inclusion Criteria:\n\n* Diagnosed with COPD\n* Age ≥ 50 years\n* Attendance at the outpatient Respiratory Medicine clinic, Vejle Hospital, University Hospital of Southern Denmark\n* Smoking history (current or former smoker)\n* Consent to translational research and biobank.\n\nExclusion Criteria:\n\n* Previous diagnosis of lung cancer within the last five years.\n* Active treatment for cancer within the last 12 months except non-melanoma skin cancer and carcinoma in situ cervicis uteri.\n* Invasive methods to verify potential lung cancer not an option.\n* Inability to provide informed consent.","ALL","50 Years",{"count":94,"type":95},1000,"ESTIMATED","INTERVENTIONAL",[36],"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.\n\nThe main question it aims to answer are:\n\n\\- What is the incidence of histopathologically confirmed lung cancer following risk-based stratification?\n\nParticipants will:\n\n* Undergo lung cancer risk assessment using the PLCOm2012 model and an ML-based model based on clinical and laboratory data\n* Be referred for LDCT if classified as high-risk\n* Continue standard care if classified as low-risk\n* 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",[100,101],"Lung Cancer (Diagnosis)","COPD (Chronic Obstructive Pulmonary Disease)",[103,104,105,106,107],"Lung cancer","Chronic obstructive pulmonary disease","Machine learning","Prediction models","Biomarkers","2026-09-23",{"date":110,"type":111},"2026-09-30","ACTUAL",{"date":113,"type":111},"2026-06-01",{"date":115,"type":95},"2033-12",{"name":5,"class":6},1]