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Trial Title:
Lung Cancer Screening by Artificial Intelligence Device
NCT ID:
NCT06295497
Condition:
Lung Cancer
Conditions: Official terms:
Lung Neoplasms
Study type:
Interventional
Study phase:
N/A
Overall status:
Not yet recruiting
Study design:
Allocation:
N/A
Intervention model:
Single Group Assignment
Primary purpose:
Screening
Masking:
None (Open Label)
Intervention:
Intervention type:
Device
Intervention name:
Lung-SIGHT
Description:
- The LDCT images will be interpreted by an artificial intelligence-based programme
(Lung-SIGHT) for lung nodules.
- LDCT with lung nodules ≥5mm detected by AI programme (AI+) will be sent for
reporting by board-certified radiologists.
- Participants with abnormal LDCT reported by radiologists (Rad+) will be followed up
at designated clinic. Management of lung nodules will follow the guidelines below
for two years.
- Participants with negative LDCT determined by AI programme (AI-) will undergo LDCT
thorax and blood taking two years later (T1). Patients with lung nodules detected by
AI programme (AI+) will be sent for radiologist reporting and those with abnormal CT
reported by radiologists (Rad+) will be followed up in lung nodule clinic as per
standard of care. Patients with normal second-round LDCT as determined by AI (AI-)
or radiologists (AI+ Rad-) do not require follow up.
Arm group label:
Artificial intelligence-based programme (Lung-SIGHT)
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. 1000 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.
Criteria for eligibility:
Criteria:
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 exclusion criteria at screening are not eligible
to be enrolled in this study:
1. History of malignancy
2. Smoking history (defined as more than 100 cigarettes in lifetime)
3. Clinical symptoms suspicious for lung cancer e.g. haemoptysis, chest pain, weight
loss
4. Medical comorbidities that preclude curative treatment (surgery) for lung cancer,
such as severe heart disease, acute or chronic respiratory failure, home oxygen
therapy, bleeding disorder
5. Pregnant ladies or ladies planning for conception
6. History of tuberculosis or interstitial lung disease
7. Pneumonia requiring antibiotic treatment within the last 12 weeks
8. CT thorax or chest performed within 2 years (including LDCT or CT coronary
angiogram)
9. Unable or unwilling to provide written informed consent
Gender:
All
Minimum age:
50 Years
Maximum age:
75 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Department of Clinical Oncology, Prince of Wales Hospital
Address:
City:
Hong Kong
Country:
Hong Kong
Contact:
Last name:
Molly SC LI, MBBS, MRCP
Phone:
3505 2166
Email:
molly@clo.cuhk.edu.hk
Contact backup:
Last name:
Candy TANG, PC
Phone:
2479 8366
Email:
candytang@cuhk.edu.hk
Start date:
December 1, 2024
Completion date:
December 31, 2027
Lead sponsor:
Agency:
Chinese University of Hong Kong
Agency class:
Other
Source:
Chinese University of Hong Kong
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06295497