Lung Cancer Screening by Artificial Intelligence Device

Not Applicable

Recruiting

• Conditions: Lung Cancer
• Interventions: Device: Lung-SIGHT

• Registration Number: NCT06295497
• Lead Sponsor: Chinese University of Hong Kong

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. 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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2024-02-18
• Study First Submitted QC: 2024-02-28
• Study First Posted: 2024-03-06
• Study Start: 2024-07-18
• Status Verified: 2024-12
• Last Update Submitted: 2024-12-09
• Last Update Posted: 2024-12-12
• Primary Completion: 2027-06-30
• Study Completion: 2027-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

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

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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

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Artificial intelligence-based programme (Lung-SIGHT)	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.

Primary Outcome Measures

Name	Time	Method
The prevalence of early-staged lung cancer detected by first-round LDCT thorax (T0) in a high-risk non-smoker population	2 years

Secondary Outcome Measures

Name	Time	Method
Sensitivity of AI-assisted programme in lung nodule detection and monitoring compared to radiologist assessment	2 years
Prevalence of lung cancer detected by second-round LDCT (T1) in patients with negative first-round LDCT	2 years
To determine the quality adjusted life years (QALYs) gained through screening	2 years
Diagnostic accuracy and discrimination ability of plasma-based fragmentomic assay in detection of lung cancer via assessment of sensitivity, specificity, positive predictive value and negative predictive value	2 years
Rate of invasive workup and incidence of associated complications.	2 years

Trial Locations

Locations (1)

Department of Clinical Oncology, Prince of Wales Hospital; 🇭🇰 Hong Kong, Hong Kong