LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol B
2026年8月21日 更新者：Allison Chang、Massachusetts General Hospital
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.
研究概览
详细说明
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.
研究类型
观察性的
注册 (估计的)
2250
联系人和位置
本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。
学习联系方式
- 姓名：Allison Chang, MD
- 电话号码：617-724-4000
- 邮箱：aechang@mgb.org
学习地点
-
-
Massachusetts
-
Boston、Massachusetts、美国、02114
- 招聘中
- Massachusetts General Hospital
-
接触：
- Allison Chang, MD
- 电话号码：617-724-4000
- 邮箱：aechang@mgb.org
-
-
参与标准
研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
- 成人
- 年长者
接受健康志愿者
不适用
取样方法
非概率样本
研究人群
This study will enroll individuals who have a family history of lung cancer (≥1 first-degree relative or ≥2 second-degree relatives).
描述
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
学习计划
本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。
研究是如何设计的？
设计细节
队列和干预
团体/队列 |
干预/治疗 |
|---|---|
|
Retrospective CT scan
Participants will contribute images and corresponding radiology reports from at least one retrospective CT chest scan.
|
Previously obtained computed tomography scan
Image-based deep learning model
|
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Sybil's performance in predicting future lung cancer diagnoses
大体时间：From date of receival of retrospective CT scan for up to 2 years.
|
We will estimate future lung cancer diagnoses using the area under the receiver operating curve (AUROC).
|
From date of receival of retrospective CT scan for up to 2 years.
|
次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Distribution of Sybil lung cancer risk scores compared to participants in the NLST clinical trial
大体时间：From receival of retrospective CT scan for up to 2 years.
|
We will compare the distribution of Sybil scores between participants in the LEGACY study and National Lung Screening Trial.
|
From receival of retrospective CT scan for up to 2 years.
|
|
Incidence and prevalence of lung cancer in the study population
大体时间：From receival of retrospective CT scan for up to 2 years.
|
We will estimate the incidence of lung cancer in the LEGACY population.
|
From receival of retrospective CT scan for up to 2 years.
|
|
Incidence, prevalence, and characteristics of lung nodules in this population
大体时间：From receival of retrospective CT scan for up to 2 years.
|
We will estimate the incidence, prevalence, and characteristics of lung nodules in the LEGACY population.
|
From receival of retrospective CT scan for up to 2 years.
|
合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
调查人员
- 首席研究员：Allison Chang, MD、Massachusetts General Hospital
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (实际的)
2026年6月17日
初级完成 (估计的)
2033年12月31日
研究完成 (估计的)
2035年12月31日
研究注册日期
首次提交
2026年5月14日
首先提交符合 QC 标准的
2026年5月14日
首次发布 (实际的)
2026年5月22日
研究记录更新
最后更新发布 (实际的)
2026年8月25日
上次提交的符合 QC 标准的更新
2026年8月21日
最后验证
2026年8月1日
更多信息
与本研究相关的术语
关键字
其他相关的 MeSH 术语
其他研究编号
- 26-054
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
是的
IPD 计划说明
The Dana-Farber / Harvard Cancer Center encourages and supports the responsible and ethical sharing of data from clinical trials.
De-identified participant data from the final research dataset used in the published manuscript may only be shared under the terms of a Data Use Agreement.
Requests may be directed to: Allison Chang, MD (aechang@mgb.org).
The protocol and statistical analysis plan will be made available on Clinicaltrials.gov
only as required by federal regulation or as a condition of awards and agreements supporting the research.
IPD 共享时间框架
Data can be shared no earlier than 1 year following the date of publication
IPD 共享访问标准
Contact the Partners Innovations team at http://www.partners.org/innovation
IPD 共享支持信息类型
- 研究方案
- 树液
- 国际碳纤维联合会
药物和器械信息、研究文件
研究美国 FDA 监管的药品
不
研究美国 FDA 监管的设备产品
不
此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.