Blood Based Risk Evaluation With AI for Targeted Primary Health Care in Early Lung Cancer Detection
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Risk stratification.
- Who it may be relevant to
- Registry conditions: Lung Cancer (Diagnosis). Basic parameters: from 50 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Denmark
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Other Risk stratification
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.
Primary outcome measures
- The fraction of patients consenting to participate in the study. [Time frame: 2 years]
Secondary outcome measures (12)
- 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]
Eligibility criteria
Inclusion criteria
- Diagnosed with COPD.
- => 50 years.
- Former or current smoker.
- Speaks and understands Danish.
- Able to give informed consent to participation.
Exclusion criteria
- 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).
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- N/A
- Model
- Single group
- Masking
- Open label
- Primary purpose
- Other
Study locations
Denmark · 2 centers
- Lillebaelt Hospital Vejle, University Hospital of Southern Denmark — Vejle
- General practices, Vejle area — Vejle
Identifiers
NCT: NCT07552584 · BREATHE · 26/9896