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

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.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer
• Intervention / Treatment: Device: Lung-SIGHT

• Study Type: Interventional
• Enrollment (Estimated): 3000
• Phase: Not Applicable

Contacts and Locations

• 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

• Hong Kong
  • Hong Kong, Hong Kong
    • Recruiting
    • Department of Clinical Oncology, Prince of Wales Hospital
    • 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

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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, PET-CT, MRI thorax or suspicious of lung cancer)
9. 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

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

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.

Device: Lung-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.

What is the study measuring?

Primary Outcome Measures

Outcome 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 Outcome Measures

Outcome Measure	Time Frame
Prevalence of lung cancer detected by second-round LDCT (T1) in patients with negative first-round LDCT	2 years
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
Cost effectiveness of LDCT lung cancer screening using AI-assisted programme	2 years

Collaborators and Investigators

• Sponsor: Chinese University of Hong Kong

Study record dates

Study Major Dates

• Study Start (Actual): July 18, 2024
• Primary Completion (Estimated): September 1, 2028
• Study Completion (Estimated): December 1, 2028

Study Registration Dates

• First Submitted: February 18, 2024
• First Submitted That Met QC Criteria: February 28, 2024
• First Posted (Actual): March 6, 2024

Study Record Updates

• Last Update Posted (Actual): March 18, 2026
• Last Update Submitted That Met QC Criteria: March 16, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: LC-SHIELD

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No