Lung Cancer (Diagnosis)BREATHENCT07552584
Blood Based Risk Evaluation With AI for Targeted Primary Health Care in Early Lung Cancer Detection
A study testing Risk stratification in people with Lung Cancer (Diagnosis), run by Vejle Hospital at 2 sites in Denmark.
Trial summary
The study is a prospective, non-randomized feasibility study evaluating blood sample and machine learning-based risk stratification for lung cancer in patients with COPD (chronic obstructive pulmonary disease). Patients with COPD will be recruited in general practice, where they will have a blood sample drawn. Read more about this study
- Who is it for?
- Ages 50 years and older
- Conditions the study is for
- Lung Cancer (Diagnosis)
- What is being tested?
- Risk stratification
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 2 sitesDenmark
- Study dates
- 26 May 2026to Apr 2034
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Diagnosed with COPD.
- => 50 years.
- Former or current smoker.
- Speaks and understands Danish.
- Able to give informed consent to participation.
Who cannot join
- Had a CT scan of the thorax within 6 months.
- Received active treatment for cancer within one year (except non-melanoma skin cancer and carcinoma in situ cervicis uteri).
- Diagnosed with cancer within one year (except non-melanoma skin cancer and carcinoma in situ cervicis uteri).
- Presents with symptoms giving suspicion of cancer (except non-melanoma skin cancer and carcinoma in situ cervicis uteri).
- In a condition not allowing diagnostic workup for or treatment of lung cancer.
- Does not have Eboks (electronic communication with Danish authorities).
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Denmark2 sites, 2 recruiting
- Recruiting
Lillebaelt Hospital Vejle, University Hospital of Southern Denmark
Vejle, 7100
Sara Witting Christensen WC Wen, MD, PhD, +45 79406511, sara.witting.christensen.wen@rsyd.dk
Lene Horsted, Study nurse, +45 79409946, breathe@rsyd.dk
- Recruiting
General practices, Vejle area
Vejle
Sara Witting Christensen Wen, MD, PhD, +45 79409946, breathe@rsyd.dk
About this study
The study is a prospective, non-randomized feasibility study evaluating blood sample and machine learning-based risk stratification for lung cancer in patients with COPD (chronic obstructive pulmonary disease).
Patients with COPD will be recruited in general practice, where they will have a blood sample drawn. All data will be analyzed by the machine learning model, and patients with increased risk of lung cancer will be referred for a low-dose CT scan of the chest.
Read more
The primary objective of the study is to evaluate the feasibility of AI and DNA methylation-based risk stratification for lung cancer in patients with COPD in a primary care setting.
The secondary objectives are to evaluate the safety of the risk stratification approach, the potential effects on quality of life and wellbeing, to gain insight into the patient and physician perspectives, and to estimate the health economic consequences.
Read the detailed description
Lung cancer causes the highest number of cancer-related deaths. Around 5000 people are diagnosed with lung cancer annually in Denmark, and people with chronic obstructive pulmonary disease (COPD) have a higher risk compared to the general population. Screening with low-dose computed tomography (LDCT) can reduce the mortality from lung cancer, but patient adherence and LDCT capacity represent considerable challenges.
The selection criteria commonly applied to LDCT screening programs center around age and tobacco consumption resulting in a large number of individuals eligible for screening. A more personalized approach could reduce the resources required for a lung cancer screening program. Smoking is the single greatest risk factor for developing lung cancer, but the damaging effect can vary between individuals. The methylation-level of the AHRR gene was found to be related to the risk of developing lung cancer. Artificial intelligence (AI) is another promising approach to risk evaluation, and a machine learning model based on clinical data and standard blood tests developed by Danish researchers can be used to predict the risk of lung cancer.
The present project aims to investigate the feasibility of blood sample and AI-based risk stratification for lung cancer in patients with COPD treated and followed in general practice.
A thousand patients with COPD will be enrolled by general practitioners located in the general Vejle area in the Region of Southern Denmark. Consenting patients will fill out basic clinical data in an online REDCap database, and then they will have the blood sample collected by a healthcare professional at the general practice clinic. The sample will be transported to the laboratory at Lillebaelt Hospital, Vejle, for analysis.
A collaborative group at Lillebaelt Hospital Vejle will perform the risk stratification including analyzing DNA methylation and running the AI algorithm. Patients with a score indicating increased risk of lung cancer will be referred for LDCT.
The project will evaluate both feasibility, safety, economy and the experiences of the participants and health care professionals.
- Conditions
- Lung Cancer (Diagnosis)
- Being tested
- Risk stratification
- Keywords
- Lung cancer, Biomarkers, Machine learning, Risk stratification, COPD
- Medical subject headings
- Lung Neoplasms, Disease, Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Risk stratificationOther
Patients with COPD will have their risk of lung cancer evaluated using a machine learning model incorporating clinical data and standard blood tests as well as a DNA methylation biomarker. If the risk of lung cancer is above the cut-off, the patient will be referred for a low-dose CT scan of the chest. Currently smoking patients will be referred for a smoking cessation program.
| Study group | Role | What they receive |
|---|---|---|
| Risk stratification | Experimental | Risk stratification for lung cancer using standard blood tests, machine learning and a DNA methylation analysis. Receives: Other: Risk stratification |
What is being measured
The primary outcome is the main question the study is designed to answer.
The fraction of patients consenting to participate in the study.
The fraction of patients consenting to participate in the study.
Measured: 2 years
Secondary outcomes (14)
Number of low-dose CT scans performed
The total number of low-dose CT scans performed in the study
Measured: 2 years
Number of correctly identified lung cancer cases
The number of correctly identified lung cancer cases when evaluated by the machine learning model, the DNA methylation biomarker, the PLCOm2012 model, and the USPSTF lung cancer screening criteria.
Measured: Up to 8 years
Number of lung cancer cases
The total number of lung cancer cases identified during the study and during 6 years of subsequent follow-up.
Measured: Up to 8 years
Stage distribution of lung cancer cases
The number of lung cancer cases identified within each stage from I-IV.
Measured: Up to 8 years
Number of patients with incidental findings on low-dose CT
The total number of patients with an incidental finding on the low-dose CT scan requiring treatment or further diagnostic procedures.
Measured: 2 years
Number of patients without malignant disease who undergo invasive diagnostic procedures
The number of patients who undergo invasive diagnostic procedures who do not have a lung cancer diagnosis at one and two years of follow-up.
Measured: Up to 4 years
Number of adverse events
The number of adverse events in the form of pneumothorax, bleeding, infection and hospital admission.
Measured: 2 years
Number of patients who initiate smoking cessation
The number and fraction of active smokers initiating and maintaining a smoking cessation program.
Measured: Up to 4 years
The fraction of participants who adhere to the study protocol
The fraction of participants who have the blood sample drawn, and when applicable, the fraction of referred participants who undergo low-dose CT.
Measured: 2 years
Differences in World Health Organization Five Well-being Index (WHO-5) score
Differences in World Health Organization Five Well-being Index (WHO-5) score between patients with and without increased risk of lung cancer after 1 month and 12 months.
The scale minimum is 0 and the maximum is 100. A higher score indicates a better outcome.
Measured: Up to 3 years
Differences in Anxiety Symptom Scale 2 (ASS-2) score
Differences in Anxiety Symptom Scale 2 (ASS-2) score between patients with and without increased risk of lung cancer after 1 month and 12 months.
The scale minimum is 0 and the maximum is 10. A lower score indicates a better outcome.
Measured: Up to 3 years
Differences in Major Depression Inventory 2 (MDI-2) score
Differences in Major Depression Inventory 2 (MDI-2) score between patients with and without increased risk of lung cancer after 1 month and 12 months.
The scale minimum is 0 and the maximum is 10. A lower score indicates a better outcome.
Measured: Up to 3 years
Differences in EQ-5D-5L (quality of life) score
Differences in EQ-5D-5L score between patients with and without increased risk of lung cancer after 1 month and 12 months.
Each of the five domains in the scale has a minimum of 1 and the maximum of 5. A lower score indicates a better outcome.
The visual analog scale has a minimum of 0 and a maximum of 100. A higher score indicates a better outcome.
Measured: Up to 3 years
Health economic consequences
The estimated health economic consequences of implementing AI and DNA methylation-based risk stratification in a primary healthcare setting including an estimation of the extra workload placed in the primary healthcare sector. A cost-utility analysis will calculate the incremental quality-adjusted life years (QALYs) gained by the program.
Measured: Up to 8 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Apr 2026
- Study start26 May 2026
- Primary completionApr 2028 (estimated)
- Study completionApr 2034 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung Cancer (Diagnosis)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Vejle Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ole Hilberg, MD, DMSc, Study chair, Department of Medicine, Lillebaelt Hospital Vejle, University Hospital of Southern DenmarkSara Witting Christensen Wen, MD, PhD, Principal investigator, Department of Biochemistry and Immunology, Lillebaelt Hospital Vejle, University Hospital of Southern Denmark
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07552584
- Sponsor's protocol ID
- BREATHE
- Other IDs
- 26/9896
- Acronym
- BREATHE
- Status last verified
- Jun 2026
- First submitted
- 20 Apr 2026
- Last updated
- 3 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Blood Based Risk Evaluation With AI for Targeted Primary Health Care in Early Lung Cancer Detection
- ClinicalTrials.gov ID
- NCT07552584
- Acronym / sponsor ID
- BREATHE / BREATHE
- Conditions
- Lung Cancer (Diagnosis)
- Keywords
- Lung cancer, Biomarkers, Machine learning, Risk stratification, COPD
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Vejle HospitalUniversity, hospital or other
- Study start
- 26 May 2026 (actual)
- Primary completion
- Apr 2028 (estimated)
- Study completion
- Apr 2034 (estimated)
- First posted
- 27 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The study is a prospective, non-randomized feasibility study evaluating blood sample and machine learning-based risk stratification for lung cancer in patients with COPD (chronic obstructive pulmonary disease).
Patients with COPD will be recruited in general practice, where they will have a blood sample drawn. All data will be analyzed by the machine learning model, and patients with increased risk of lung cancer will be referred for a low-dose CT scan of the chest.
The primary objective of the study is to evaluate the feasibility of AI and DNA methylation-based risk stratification for lung cancer in patients with COPD in a primary care setting.
The secondary objectives are to evaluate the safety of the risk stratification approach, the potential effects on quality of life and wellbeing, to gain insight into the patient and physician perspectives, and to estimate the health economic consequences.
Detailed description
Lung cancer causes the highest number of cancer-related deaths. Around 5000 people are diagnosed with lung cancer annually in Denmark, and people with chronic obstructive pulmonary disease (COPD) have a higher risk compared to the general population. Screening with low-dose computed tomography (LDCT) can reduce the mortality from lung cancer, but patient adherence and LDCT capacity represent considerable challenges.
The selection criteria commonly applied to LDCT screening programs center around age and tobacco consumption resulting in a large number of individuals eligible for screening. A more personalized approach could reduce the resources required for a lung cancer screening program. Smoking is the single greatest risk factor for developing lung cancer, but the damaging effect can vary between individuals. The methylation-level of the AHRR gene was found to be related to the risk of developing lung cancer. Artificial intelligence (AI) is another promising approach to risk evaluation, and a machine learning model based on clinical data and standard blood tests developed by Danish researchers can be used to predict the risk of lung cancer.
The present project aims to investigate the feasibility of blood sample and AI-based risk stratification for lung cancer in patients with COPD treated and followed in general practice.
A thousand patients with COPD will be enrolled by general practitioners located in the general Vejle area in the Region of Southern Denmark. Consenting patients will fill out basic clinical data in an online REDCap database, and then they will have the blood sample collected by a healthcare professional at the general practice clinic. The sample will be transported to the laboratory at Lillebaelt Hospital, Vejle, for analysis.
A collaborative group at Lillebaelt Hospital Vejle will perform the risk stratification including analyzing DNA methylation and running the AI algorithm. Patients with a score indicating increased risk of lung cancer will be referred for LDCT.
The project will evaluate both feasibility, safety, economy and the experiences of the participants and health care professionals.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Risk stratification | Experimental | Other: Risk stratificationRisk stratification for lung cancer using standard blood tests, machine learning and a DNA methylation analysis. |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Diagnosed with COPD.
- => 50 years.
- Former or current smoker.
- Speaks and understands Danish.
- Able to give informed consent to participation.
Key exclusion criteria (6 listed)
- Had a CT scan of the thorax within 6 months.
- Received active treatment for cancer within one year (except non-melanoma skin cancer and carcinoma in situ cervicis uteri).
- Diagnosed with cancer within one year (except non-melanoma skin cancer and carcinoma in situ cervicis uteri).
- Presents with symptoms giving suspicion of cancer (except non-melanoma skin cancer and carcinoma in situ cervicis uteri).
- In a condition not allowing diagnostic workup for or treatment of lung cancer.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Risk stratificationPatients with COPD will have their risk of lung cancer evaluated using a machine learning model incorporating clinical data and standard blood tests as well as a DNA methylation biomarker. If the risk of lung cancer is above the cut-off, the patient will be referred for a low-dose CT scan of the chest. Currently smoking patients will be referred for a smoking cessation program. | Other | Risk stratification |
Primary outcome
The fraction of patients consenting to participate in the study.
Time frame: 2 years
The fraction of patients consenting to participate in the study.
14 secondary outcomes
Number of low-dose CT scans performed
Time frame: 2 years
Number of correctly identified lung cancer cases
Time frame: Up to 8 years
Number of lung cancer cases
Time frame: Up to 8 years
Stage distribution of lung cancer cases
Time frame: Up to 8 years
Number of patients with incidental findings on low-dose CT
Time frame: 2 years
Number of patients without malignant disease who undergo invasive diagnostic procedures
Time frame: Up to 4 years
Number of adverse events
Time frame: 2 years
Number of patients who initiate smoking cessation
Time frame: Up to 4 years
The fraction of participants who adhere to the study protocol
Time frame: 2 years
Differences in World Health Organization Five Well-being Index (WHO-5) score
Time frame: Up to 3 years
Differences in Anxiety Symptom Scale 2 (ASS-2) score
Time frame: Up to 3 years
Differences in Major Depression Inventory 2 (MDI-2) score
Time frame: Up to 3 years
Differences in EQ-5D-5L (quality of life) score
Time frame: Up to 3 years
Health economic consequences
Time frame: Up to 8 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Jun 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ole Hilberg, MD, DMSc study chair
Department of Medicine, Lillebaelt Hospital Vejle, University Hospital of Southern Denmark
Sara Witting Christensen Wen, MD, PhD principal investigator
Department of Biochemistry and Immunology, Lillebaelt Hospital Vejle, University Hospital of Southern Denmark
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Risk stratification in people with Lung Cancer (Diagnosis), run by Vejle Hospital at 2 sites in Denmark.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 3 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Denmark. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sara Witting Christensen WC Wen, MD, PhD (+45 79406511, sara.witting.christensen.wen@rsyd.dk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The fraction of patients consenting to participate in the study. (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Jun 2026.