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NCT Number: NCT07685028

LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol A

This research is being done to determine if an image-based deep learning model (Sybil) can accurately predict the likelihood of future lung cancer based on chest computed tomography (CT) imaging from individuals.

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Key information

Conditions

Family History of Lung Cancer Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Massachusetts General Hospital

Boston, Massachusetts, 02114, United States

Location contact

Allison Chang, MD

CONTACT

[email protected]

617-724-4000

About this study

This non-therapeutic study will enroll individuals who have family history of lung cancer. Participants will undergo a low-dose non-contrast computed tomography of the chest (LDCT) and may also send images from any chest CT scan(s) obtained as part of routine clinical care, outside of the study. The images and data collected will be analyzed by an image-based deep learning model (Sybil). Sybil is a type of artificial intelligence model that has been shown to accurately predict individuals' future risk of lung cancer based solely on images from a CT Chest scan, but it remains unclear whether Sybil works well in people with a family history of lung cancer. The goals of this study are: 1) to obtain CT Chest images from individuals with a family history of lung cancer in order to test whether Sybil continues to work well, and 2) offer free screening CT scans to qualifying individuals. It is expected that 250 people will take part in this research study.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

  • Age: Must meet both the upper and lower age limit criteria.
  • Upper age limit: ≤80 years of age
  • Lower age limit:
  • ≥40 years of age OR
  • ≥18 years of age AND ≤10 years of youngest relative's age at time of lung cancer diagnosis (e.g., if a relative was diagnosed at 35 years of age, participant can enroll at ≥25 years of age)
  • Positive family history of lung cancer (defined as):
  • Has ≥1 first-degree relative, OR
  • Has ≥2 second-degree relatives with a diagnosis of non-small cell lung cancer or small cell lung cancer (NB: a first-degree relative = parent, sibling, or child, a second-degree relative = grandparent, blood-related aunt or uncle, grandchild, blood-related niece or nephew, half-sibling)

Exclusion criteria

  • Must not have a personal history of lung cancer at the time of enrollment.
  • Must not have a personal history of stage IV cancer of any type at the time of enrollment.
  • Must not have had surgical removal of any portion of the lung, excluding needle or core lung biopsy at the time of enrollment.
  • Must not have had a chest CT within 12 months prior to trial enrollment.

Treatment and study plan

CT Scan

Diagnostic Test

Computed tomography scan

Sybil

Other

Image-based deep learning model

Primary outcomes

  1. Sybil's performance in predicting future lung cancer diagnoses

    Time frame: Annually, from time of initial CT scan to up to 5 years after the scan.

    All subjects will be followed for lung cancer diagnosis scan for up to 5 years following the baseline scan. Sybil's performance in predicting future lung cancer diagnoses across the study population will be calculated using the area under the receiver operating curve (AUROC), which is a measure of a risk prediction model's ability to discriminate between cases and controls. Sybil's output corresponds to the cumulative annual risk of lung cancer for up to 6 years following a given scan.

Secondary outcomes

  1. Compare the distribution of Sybil lung cancer risk scores in this trial to the distribution of Sybil risk scores from the NLST clinical trial

    Time frame: Initial provided CT scan will represent time 0. Additional provided CT scans will vary between individuals and will be measured in years relative to time 0 (e.g., time -3.5 years, time +2 years, etc). Sybil risk scores will be calculated for each scan.

    Investigators will compare the distribution of Sybil scores (ranging from 0-1) from participants in this study with the distribution of Sybil scores from historical data from participants in the National Lung Screening Trial.

  2. Incidence and prevalence of lung cancer in the study population

    Time frame: Annually, from time of initial CT scan to up to 5 years after the scan.

    Investigators will estimate the incidence and prevalence of lung cancer in the LEGACY population. Incidence will be reported per person per year. Prevalence will be reported separately as a measure over the 5-year study follow up period.

  3. Incidence of lung nodules in this population

    Time frame: Annually, from time of initial CT scan to up to 5 years after the scan.

    Investigators will estimate the incidence of lung nodules in the LEGACY population. Incidence will be measured per person per year.

  4. Prevalence of lung nodules in this population

    Time frame: Annually, from time of initial CT scan to up to 5 years after the scan.

    Investigators will estimate the prevalence of lung nodules in the LEGACY population. This will be measured over the 5-year study follow up period.

  5. Describe the characteristics of lung nodules in this population

    Time frame: At time of each provided CT scan to up to 5 years after the scan.

    Investigators will describe the characteristics of lung nodules in the study population, including but not limited to size, location, and attenuation.

Study contacts

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

Allison Chang, MD

CONTACT

[email protected]

617-724-4000

Sponsors and collaborators

Lead sponsor

Massachusetts General Hospital

Other

Registry information

Important dates

Study start
2026
Primary completion
2033
Study completion
2035
First posted
Jul 6, 2026
Registry last updated
Jul 6, 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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