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NCT Number: NCT06295497

Lung Cancer Screening in HIgh Risk nonsmokErs by Artificial inteLligence Device

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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Key information

Age range

50 year–75 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

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

    Time frame: 2 years

Secondary outcomes

  1. Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung cancer detection

    Time frame: 2 years

  2. Diagnostic utility of plasma-based biomarker for detection and risk assessment of early-staged lung cancer

    Time frame: 2 years

  3. Rate of invasive workup and associated complications

    Time frame: 2 years

  4. Stage distribution of lung cancer detected by LDCT screening

    Time frame: 2 years

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

    Time frame: 2 years

  6. Cost effectiveness of LDCT lung cancer screening using AI-assisted programme

    Time frame: 2 years

Study contacts

Contact information is provided by the study sponsor or research team.

Candy TANG, PC

CONTACT

[email protected]

2479 8366

Molly SC LI, MBBS, MRCP

CONTACT

[email protected]

3505 2166

Sponsors and collaborators

Lead sponsor

Chinese University of Hong Kong

Other

Registry information

Official study title

A Prospective Study on Artificial Intelligence Guided Lung Cancer Screening for High-risk Never Smokers in Hong Kong

Important dates

Study start
2024
Primary completion
2028
Study completion
2028
First posted
Mar 6, 2024
Registry last updated
Mar 18, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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