LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol A
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.
Description
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 Sy…
Eligibility
- Age range
- 18–80 years
- Sex
- All
- Healthy volunteers
- No
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 c…
Interventions
- Diagnostic TestCT scan
Computed tomography scan
- OtherSybil
Image-based deep learning model
Location
- Massachusetts General HospitalBoston, Massachusetts