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NCT Number: NCT07600801

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

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 with a family history of lung cancer.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

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 is a non-therapeutic study that will enroll individuals who have a family history of lung cancer. During the study, participants will provide questionnaire responses regarding their personal medical history, family lung cancer history, and exposures along with contributing images from at least one previously obtained CT chest scan. 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 is unknown if it works well in people with a family history of lung cancer. It is expected that 2,250 will take part in this research study.

Who can participate

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

Inclusion criteria

  • ≥18 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)
  • Willing to provide images from at least one previously obtained CT Chest scan, if available.

Exclusion criteria

  • None

Treatment and study plan

CT Scan

Diagnostic Test

Previously obtained computed tomography scan

Sybil

Other

Image-based deep learning model

Primary outcomes

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

    Time frame: From date of receival of retrospective CT scan for up to 2 years.

    We will estimate future lung cancer diagnoses using the area under the receiver operating curve (AUROC).

Secondary outcomes

  1. Distribution of Sybil lung cancer risk scores compared to participants in the NLST clinical trial

    Time frame: From receival of retrospective CT scan for up to 2 years.

    We will compare the distribution of Sybil scores between participants in the LEGACY study and National Lung Screening Trial.

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

    Time frame: From receival of retrospective CT scan for up to 2 years.

    We will estimate the incidence of lung cancer in the LEGACY population.

  3. Incidence, prevalence, and characteristics of lung nodules in this population

    Time frame: From receival of retrospective CT scan for up to 2 years.

    We will estimate the incidence, prevalence, and characteristics of lung nodules in the LEGACY population.

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
May 22, 2026
Registry last updated
May 27, 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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