Screening for Lung Cancer in Subjects With Family History of Lung Cancer

NCT ID: NCT05762731

Last Updated: 2023-03-10

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1520 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-02-01

Study Completion Date

2025-12-31

Brief Summary

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Lung cancer can be detected via screening of high-risk individuals, i.e current or ex-heavy smokers, with low-dose computer tomography (LDCT) of thorax. The National Lung Screening Trial in US and the NELSON trial in Europe demonstrated reduction in lung cancer mortality with LDCT screening for lung cancer. In Hong Kong, however, there is a prominence of female never-smokers with lung cancer. There is no identifiable risk factors for non-smokers with lung cancer except family history of lung cancer. The hypothesis is that lung cancer screening for subjects with family history of lung cancer, can detect early lung cancer.

Detailed Description

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The primary aim of this prospective study is to find out the rate of lung cancer detection in subjects who are first degree relatives of lung cancer patients. Secondary aims include studying the characteristics of screen-detected lung cancer. This is a multi-centered prospective cohort study. 1,520 subjects who are first degree relatives of lung cancer patients at four public hospitals in Hong Kong will be screened. Intervention Detailed questionnaires and LDCT Thorax will be performed. The primary outcome measure is the number of lung cancers detected by this study. The screening-detection rate of lung cancer in first-degree relatives of lung cancer patients will be estimated.

Conditions

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Lung Cancer

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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First degree relatives of lung cancer patients

Age 50-75, men or women, smokers or non-smokers. Being first degree relatives (siblings, children and parents) of lung cancer subjects. Having no known lung cancer before.

Low dose CT thorax

Intervention Type DIAGNOSTIC_TEST

A multi-detector row CT scanner with minimum section collimation of ≤1 mm and minimum number of data acquisition channels ≥ 16will be employed.

Control group

Non-lung cancer subjects who are not related any lung cancer patients

Low dose CT thorax

Intervention Type DIAGNOSTIC_TEST

Control subjects will also proceed to CT thorax, and outcome measures be compared to subjects

Interventions

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Low dose CT thorax

A multi-detector row CT scanner with minimum section collimation of ≤1 mm and minimum number of data acquisition channels ≥ 16will be employed.

Intervention Type DIAGNOSTIC_TEST

Low dose CT thorax

Control subjects will also proceed to CT thorax, and outcome measures be compared to subjects

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* Age 50-75, men or women, smokers or non-smokers
* Being first degree relatives (Siblings, children, and parents) of lung cancer subjects
* Having no known lung cancer before

Exclusion Criteria

* Non-Chinese
* Mentally incompetent to give informed consent
Minimum Eligible Age

50 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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The Queen Elizabeth Hospital

OTHER

Sponsor Role collaborator

Kwong Wah Hospital

OTHER

Sponsor Role collaborator

Hong Kong Sanatorium & Hospital

INDUSTRY

Sponsor Role collaborator

The University of Hong Kong

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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University of Hong Kong

Hong Kong, , Hong Kong

Site Status RECRUITING

Countries

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Hong Kong

Facility Contacts

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David CL Lam, MBBS, FRCP(Edin), FCCP, FACP

Role: primary

+852 2255 5814

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Related Links

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http://www3.ha.org.hk/cancereg/

Hong Kong Cancer Registry 2016 Hong Kong: Hospital Authority

Other Identifiers

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HKU_UW_19_444

Identifier Type: -

Identifier Source: org_study_id

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