LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol A
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 250
- 试验地点
- 1
- 主要终点
- Sybil's performance in predicting future lung cancer diagnoses
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.
详细描述
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 Sybil works well in people with a family history of lung cancer. The goals of this study are: 1) to obtain CT Chest images from individuals with a family history of lung cancer in order to test whether Sybil continues to work well, and 2) offer free screening CT scans to qualifying individuals. It is expected that 250 people will take part in this research study.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Screening
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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 child, a second-degree relative = grandparent, blood-related aunt or uncle, grandchild, blood-related niece or nephew, half-sibling)
排除标准
- •Must not have a personal history of lung cancer at the time of enrollment.
- •Must not have a personal history of stage IV cancer of any type at the time of enrollment.
- •Must not have had surgical removal of any portion of the lung, excluding needle or core lung biopsy at the time of enrollment.
- •Must not have had a chest CT within 12 months prior to trial enrollment.
结局指标
主要结局
Sybil's performance in predicting future lung cancer diagnoses
时间窗: Annually, from time of initial CT scan to up to 5 years after the scan.
All subjects will be followed for lung cancer diagnosis scan for up to 5 years following the baseline scan. Sybil's performance in predicting future lung cancer diagnoses across the study population will be calculated using the area under the receiver operating curve (AUROC), which is a measure of a risk prediction model's ability to discriminate between cases and controls. Sybil's output corresponds to the cumulative annual risk of lung cancer for up to 6 years following a given scan.
次要结局
- Compare the distribution of Sybil lung cancer risk scores in this trial to the distribution of Sybil risk scores from the NLST clinical trial(Initial provided CT scan will represent time 0. Additional provided CT scans will vary between individuals and will be measured in years relative to time 0 (e.g., time -3.5 years, time +2 years, etc). Sybil risk scores will be calculated for each scan.)
- Incidence and prevalence of lung cancer in the study population(Annually, from time of initial CT scan to up to 5 years after the scan.)
- Incidence of lung nodules in this population(Annually, from time of initial CT scan to up to 5 years after the scan.)
- Prevalence of lung nodules in this population(Annually, from time of initial CT scan to up to 5 years after the scan.)
- Describe the characteristics of lung nodules in this population(At time of each provided CT scan to up to 5 years after the scan.)
研究者
Allison Chang
Principal Investigator
Massachusetts General Hospital