LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol B
Massachusetts General Hospital
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.
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 Che…
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- Not specified
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 TestCT scan
Previously obtained computed tomography scan
- OtherSybil
Image-based deep learning model
Location
- Massachusetts General HospitalBoston, Massachusetts