Lung Cancer Screening in HIgh Risk nonsmokErs by Artificial inteLligence Device
A Prospective Study on Artificial Intelligence Guided Lung Cancer Screening for High-risk Never Smokers in Hong Kong
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Molly SC LI, MBBS, MRCP
- Phone Number: 3505 2166
- Email: molly@clo.cuhk.edu.hk
Study Contact Backup
- Name: Candy TANG, PC
- Phone Number: 2479 8366
- Email: candytang@cuhk.edu.hk
Study Locations
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Hong Kong, Hong Kong
- Recruiting
- Department of Clinical Oncology, Prince of Wales Hospital
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Contact:
- Molly SC LI, MBBS, MRCP
- Phone Number: 3505 2166
- Email: molly@clo.cuhk.edu.hk
-
Contact:
- Candy TANG, PC
- Phone Number: 2479 8366
- Email: candytang@cuhk.edu.hk
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
Patients are eligible to be included in the study only if all of the following criteria apply:
- Age 50-75 years old
- Non-smoker (defined as less than 100 cigarettes in lifetime)
- Having a first-degree family history of lung cancer
- Physically fit for curative treatment if early-staged lung cancer is found
- Able to provide written informed consent
- Consent to follow up visits and follow up CT scan if indicated
- 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:
- History of malignancy
- Smoking history (defined as more than 100 cigarettes in lifetime)
- Clinical symptoms suspicious for lung cancer e.g. haemoptysis, chest pain, weight loss
- 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
- Pregnant ladies or ladies planning for conception
- History of tuberculosis or interstitial lung disease
- Pneumonia requiring antibiotic treatment within the last 12 weeks
- CT thorax or chest performed within 2 years (including LDCT, PET-CT, MRI thorax or suspicious of lung cancer)
- Unable or unwilling to provide written informed consent
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Other: Artificial intelligence-based programme (Lung-SIGHT)
Artificial intelligence (AI) algorithms have been demonstrated to function well and complement radiologists as second or concurrent readers in pulmonary nodule detection.
AI Lung nodule detection and quantification solution are now widely used in the hospitals in the United Kingdom and at least eight other European countries.
The sensitivity of nodule detection by radiologists increased from 72% to 80% with the aid of the AI programme.
A clinical trial in Taiwan showed that using AI programme alone achieved an overall sensitivity of 95.6% in nodule detection, and superior performance in detecting nodule sized 4-5 mm comparing to radiologists.
Overall, application of AI in CT analysis and lung nodule detection may significantly reduce the cost and workload of radiologist.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
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Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung nodule (≥5mm) detection and monitoring compared to radiologist assessment
Time Frame: 2 years
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2 years
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
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Prevalence of lung cancer detected by second-round LDCT (T1) in patients with negative first-round LDCT
Time Frame: 2 years
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2 years
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Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung cancer detection
Time Frame: 2 years
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2 years
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Diagnostic utility of plasma-based biomarker for detection and risk assessment of early-staged lung cancer
Time Frame: 2 years
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2 years
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Rate of invasive workup and associated complications
Time Frame: 2 years
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2 years
|
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Stage distribution of lung cancer detected by LDCT screening
Time Frame: 2 years
|
2 years
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Cost effectiveness of LDCT lung cancer screening using AI-assisted programme
Time Frame: 2 years
|
2 years
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- LC-SHIELD
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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