LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol B
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 2,250
- 试验地点
- 2
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •≥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.
排除标准
- 未提供
研究者
Allison Chang
Principal Investigator
Massachusetts General Hospital