Lung Cancer
Conditions
Brief summary
Lung cancer screening is currently not recommended in non-smokers due to paucity of evidence. Emerging evidence suggests that first-degree family history is a strong risk factor for lung cancer in Asian non-smokers. In Asia, lack of resource is a major challenge in successful implementation of lung cancer screening. Artificial intelligence (AI) is a promising tool to overcome this resource. In this study, we aim to study the clinical utility and demonstrate the feasibility of using an AI assisted programme for lung cancer screening in Asian non-smokers with a positive family history. This is a single-arm non-randomized lung cancer screening study. 3000 non-smokers, age 50 to 75 year old, with a first-degree family history of lung cancer, will be enrolled. Participants will undergo low does computed tomography (LDCT) of thorax and blood taking at enrolment. LDCT films will be interpreted by AI softwares for presence of lung nodules. Participants with lung nodules will be further investigated and followed up according to the risk of malignancy. The primary endpoint is the prevalence of early-staged lung cancer detected by first-round LDCT thorax in this population.
Interventions
DEVICELung-SIGHT
* The LDCT images will be interpreted by an artificial intelligence-based programme (Lung-SIGHT) for lung nodules. * b. In phase I, AI will serve as a first reader to screen LDCT scans. LDCT with lung nodules \>=5mm will be marked as abnormal, sent for reporting by board-certified radiologists and followed up in lung nodule clinic if the presence of lung nodules is confirmed. * c. In phase II, LDCT with lung nodules \>=5mm detected by AI will be categorized into different groups depending on risk of lung nodules and followed up with LDCT according to the risk. Subjects with high-risk nodules will be sent for reporting by board-certified radiologists and followed up in lung nodule clinic if the presence of high-risk nodules is confirmed. * Subjects with negative LDCT determined by AI programme (AI-) will undergo LDCT thorax and blood taking two years later (T1). Participants with normal second-round LDCT as determined by AI (AI-) or radiologists (AI+ Rad-) do not require follow up.
Sponsors
Chinese University of Hong Kong
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
50 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
Patients are eligible to be included in the study only if all of the following criteria apply: 1. Age 50-75 years old 2. Non-smoker (defined as less than 100 cigarettes in lifetime) 3. Having a first-degree family history of lung cancer 4. Physically fit for curative treatment if early-staged lung cancer is found 5. Able to provide written informed consent 6. Consent to follow up visits and follow up CT scan if indicated 7. Consent to blood taking for translational research
Exclusion criteria
Patients who meet any of the following
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung nodule (≥5mm) detection and monitoring compared to radiologist assessment | 2 years |
Secondary
| Measure | Time frame |
|---|---|
| Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung cancer detection | 2 years |
| Diagnostic utility of plasma-based biomarker for detection and risk assessment of early-staged lung cancer | 2 years |
| Rate of invasive workup and associated complications | 2 years |
| Stage distribution of lung cancer detected by LDCT screening | 2 years |
| Prevalence of lung cancer detected by second-round LDCT (T1) in patients with negative first-round LDCT | 2 years |
| Cost effectiveness of LDCT lung cancer screening using AI-assisted programme | 2 years |
Countries
Hong Kong
Contacts
CONTACTMolly SC LI, MBBS, MRCP
CONTACTCandy TANG, PC
Outcome results
None listed