LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol B
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: CT scan, Sybil.
- Who it may be relevant to
- Registry conditions: Family History of Lung Cancer. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Diagnostic test CT scan
Previously obtained computed tomography scan - Other Sybil
Image-based deep learning model
Primary outcome measures
- Sybil's performance in predicting future lung cancer diagnoses [Time frame: From date of receival of retrospective CT scan for up to 2 years.]
Secondary outcome measures (3)
- 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.]
- Incidence and prevalence of lung cancer in the study population [Time frame: From receival of retrospective CT scan for up to 2 years.]
- Incidence, prevalence, and characteristics of lung nodules in this population [Time frame: From receival of retrospective CT scan for up to 2 years.]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
United States · 1 center
- Massachusetts General Hospital — Boston
Identifiers
NCT: NCT07600801 · 26-054