Lung Cancer Screening in HIgh Risk nonsmokErs by Artificial inteLligence Device

NCT06295497 | Recruiting

• 1 other identifier: LC-SHIELD
• Study Type: interventional
• Target: 3,000
• Locations: 1 country
• Sites: 1

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. 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.

Conditions

Lung Cancer

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (6)

• 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

• Prevalence of lung cancer detected by second-round LDCT (T1) in patients with negative first-round LDCT

  2 years

Study Arms (1)

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.

Device: Lung-SIGHT

Interventions

Lung-SIGHT DEVICE

* 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.

Artificial intelligence-based programme (Lung-SIGHT)

Eligibility Criteria

• Age: 50 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Chinese University of Hong Kong: lead

Study Sites (1)

Department of Clinical Oncology, Prince of Wales Hospital

Hong Kong, Hong Kong

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Molly SC LI, MBBS, MRCP

CONTACT

3505 2166; molly@clo.cuhk.edu.hk

Candy TANG, PC

CONTACT

2479 8366; candytang@cuhk.edu.hk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: February 18, 2024
• First Posted: March 6, 2024
• Study Start: July 18, 2024
• Primary Completion (Estimated): September 1, 2028
• Study Completion (Estimated): December 1, 2028
• Last Updated: March 18, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)