Blood Based Risk Evaluation With AI for Targeted Primary Health Care in Early Lung Cancer Detection

Blood Based Risk Evaluation With AI for Targeted Primary Health Care in Early Lung Cancer Detection

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.

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.

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).