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NCT07600801Recruiting

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

Massachusetts General Hospital

Start Date

6/17/2026

Completion Date

12/31/2035

Summary

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.

Detailed Description

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.

Eligibility Criteria

Age Range: 18 years to No maximum

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

Interventions

DIAGNOSTIC_TEST

CT scan

OTHER

Sybil

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Conditions

Family History of Lung Cancer

Locations

Massachusetts General Hospital

Boston, Massachusetts 02114

United States