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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Artificial intelligence-based programme (Lung-SIGHT) | Other | 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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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Lung-SIGHT | Device |
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| Measure | Description | 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 |
| Measure | Description | 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 |
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Inclusion Criteria:
Patients are eligible to be included in the study only if all of the following criteria apply:
Exclusion Criteria:
Patients who meet any of the following exclusion criteria at screening are not eligible to be enrolled in this study:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Molly SC LI, MBBS, MRCP | Contact | 3505 2166 | molly@clo.cuhk.edu.hk | |
| Candy TANG, PC | Contact | 2479 8366 | candytang@cuhk.edu.hk |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Clinical Oncology, Prince of Wales Hospital | Recruiting | Hong Kong | Hong Kong |
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| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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|
| 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 |
| D008171 |
| Lung Diseases |
| D012140 | Respiratory Tract Diseases |