Lung CancerNCT06295497
Lung Cancer Screening in HIgh Risk nonsmokErs by Artificial inteLligence Device
A study testing Lung-SIGHT in people with Lung Cancer, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 75 years
- Conditions the study is for
- Lung Cancer
- What is being tested?
- Lung-SIGHT
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 3,000 participants
- Where
- 1 siteHong Kong
- Study dates
- 18 Jul 2024to 1 Dec 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Show all 10Show fewer
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Hong Kong1 site, 1 recruiting
- Recruiting
Department of Clinical Oncology, Prince of Wales Hospital
Hong Kong
Molly SC LI, MBBS, MRCP, 3505 2166, molly@clo.cuhk.edu.hk
Candy TANG, PC, 2479 8366, candytang@cuhk.edu.hk
About this study
Official title: 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.
- Conditions
- Lung Cancer
- Being tested
- Lung-SIGHT
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Lung-SIGHTDevice
- 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Lung-SIGHT |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung nodule (≥5mm) detection and monitoring compared to radiologist assessment
Measured: 2 years
Secondary outcomes (6)
Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung cancer detection
Measured: 2 years
Diagnostic utility of plasma-based biomarker for detection and risk assessment of early-staged lung cancer
Measured: 2 years
Rate of invasive workup and associated complications
Measured: 2 years
Stage distribution of lung cancer detected by LDCT screening
Measured: 2 years
Prevalence of lung cancer detected by second-round LDCT (T1) in patients with negative first-round LDCT
Measured: 2 years
Cost effectiveness of LDCT lung cancer screening using AI-assisted programme
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2024
- Study start18 Jul 2024
- Primary completion1 Sep 2028 (estimated)
- Study completion1 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Molly SC Li, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06295497
- Sponsor's protocol ID
- LC-SHIELD
- Status last verified
- Mar 2026
- First submitted
- 18 Feb 2024
- Last updated
- 18 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Study on Artificial Intelligence Guided Lung Cancer Screening for High-risk Never Smokers in Hong Kong
- ClinicalTrials.gov ID
- NCT06295497
- Acronym / sponsor ID
- LC-SHIELD
- Conditions
- Lung Cancer
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Study start
- 18 Jul 2024 (actual)
- Primary completion
- 1 Sep 2028 (estimated)
- Study completion
- 1 Dec 2028 (estimated)
- First posted
- 6 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial intelligence-based programme (Lung-SIGHT) | Other | Device: Lung-SIGHTArtificial 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. |
Who may qualify
- Ages
- 50 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (8 listed)
- 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
Key exclusion criteria (10 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Device | Artificial intelligence-based programme (Lung-SIGHT) |
Primary outcome
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
6 secondary outcomes
Sensitivity, specificity, positive predictive value and negative predictive value of AI-assisted programme in lung cancer detection
Time frame: 2 years
Diagnostic utility of plasma-based biomarker for detection and risk assessment of early-staged lung cancer
Time frame: 2 years
Rate of invasive workup and associated complications
Time frame: 2 years
Stage distribution of lung cancer detected by LDCT screening
Time frame: 2 years
Prevalence of lung cancer detected by second-round LDCT (T1) in patients with negative first-round LDCT
Time frame: 2 years
Cost effectiveness of LDCT lung cancer screening using AI-assisted programme
Time frame: 2 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Mar 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Lung-SIGHT in people with Lung Cancer, run by Chinese University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 18 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Molly SC LI, MBBS, MRCP (3505 2166, molly@clo.cuhk.edu.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Mar 2026.