LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol B
1 other identifier
observational
2,250
1 country
1
Brief 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 with a family history of lung cancer.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2026
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
May 14, 2026
CompletedFirst Posted
Study publicly available on registry
May 22, 2026
CompletedStudy Start
First participant enrolled
June 15, 2026
ExpectedPrimary Completion
Last participant's last visit for primary outcome
December 31, 2033
Study Completion
Last participant's last visit for all outcomes
December 31, 2035
May 22, 2026
May 1, 2026
7.6 years
May 14, 2026
May 14, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Sybil's performance in predicting future lung cancer diagnoses
We will estimate the association between the year 2 Sybil score and diagnosis of incident lung cancer within two years of the index CT scan.
From date of receival of retrospective CT scan for up to 2 years.
Secondary Outcomes (3)
Distribution of Sybil lung cancer risk scores compared to participants in the NLST clinical trial
From receival of retrospective CT scan for up to 2 years.
Incidence and prevalence of lung cancer in the study population
From receival of retrospective CT scan for up to 2 years.
Incidence, prevalence, and characteristics of lung nodules in this population
From receival of retrospective CT scan for up to 2 years.
Study Arms (1)
Retrospective CT scan
Participants will contribute images and corresponding radiology reports from at least one retrospective CT chest scan.
Interventions
Eligibility Criteria
This study will enroll individuals who have a family history of lung cancer (≥1 first-degree relative or ≥2 second-degree relatives).
You may qualify if:
- ≥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.
You may not qualify if:
- \- None
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Massachusetts General Hospital
Boston, Massachusetts, 02114, United States
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Allison Chang, MD
Massachusetts General Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Principal Investigator
Study Record Dates
First Submitted
May 14, 2026
First Posted
May 22, 2026
Study Start (Estimated)
June 15, 2026
Primary Completion (Estimated)
December 31, 2033
Study Completion (Estimated)
December 31, 2035
Last Updated
May 22, 2026
Record last verified: 2026-05
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP, ICF
- Time Frame
- Data can be shared no earlier than 1 year following the date of publication
- Access Criteria
- Contact the Partners Innovations team at http://www.partners.org/innovation
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.