Lung Cancer (Diagnosis)
Conditions
Keywords
Lung cancer, Biomarkers, Machine learning, Risk stratification, COPD
Brief 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. 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
OTHERRisk 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.
Sponsors
Vejle Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
50 Years to No maximum
Healthy volunteers
No
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).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The fraction of patients consenting to participate in the study. | 2 years | The fraction of patients consenting to participate in the study. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Number of low-dose CT scans performed | 2 years | The total number of low-dose CT scans performed in the study |
| Number of correctly identified lung cancer cases | Up to 8 years | 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. |
| Number of lung cancer cases | Up to 8 years | The total number of lung cancer cases identified during the study and during 6 years of subsequent follow-up. |
| Stage distribution of lung cancer cases | Up to 8 years | The number of lung cancer cases identified within each stage from I-IV. |
| Number of patients with incidental findings on low-dose CT | 2 years | The total number of patients with an incidental finding on the low-dose CT scan requiring treatment or further diagnostic procedures. |
| Number of patients without malignant disease who undergo invasive diagnostic procedures | Up to 4 years | 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. |
| Number of adverse events | 2 years | The number of adverse events in the form of pneumothorax, bleeding, infection and hospital admission. |
| Number of patients who initiate smoking cessation | Up to 4 years | The number and fraction of active smokers initiating and maintaining a smoking cessation program. |
| The fraction of participants who adhere to the study protocol | 2 years | The fraction of participants who have the blood sample drawn, and when applicable, the fraction of referred participants who undergo low-dose CT. |
| Differences in World Health Organization Five Well-being Index (WHO-5) score | Up to 3 years | 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. |
| Differences in Anxiety Symptom Scale 2 (ASS-2) score | Up to 3 years | 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. |
| Differences in Major Depression Inventory 2 (MDI-2) score | Up to 3 years | 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. |
| Differences in EQ-5D-5L (quality of life) score | Up to 3 years | 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. |
| Health economic consequences | Up to 8 years | 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. |
Countries
Denmark
Contacts
CONTACTSara Witting Christensen WC Wen, MD, PhD
CONTACTLene Horsted, Study nurse
STUDY_CHAIROle Hilberg, MD, DMSc
Department of Medicine, Lillebaelt Hospital Vejle, University Hospital of Southern Denmark
PRINCIPAL_INVESTIGATORSara Witting Christensen Wen, MD, PhD
Department of Biochemistry and Immunology, Lillebaelt Hospital Vejle, University Hospital of Southern Denmark
Outcome results
None listed