Family History of Lung CancerNCT07600801
LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol B
An observational study of Family History of Lung Cancer, run by Massachusetts General Hospital at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Family History of Lung Cancer
- What is being tested?
- CT scan and Sybil
- How many people
- 2,250 participants
- Where
- 1 siteUnited States
- Study dates
- 17 Jun 2026to 31 Dec 2035
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: This study will enroll individuals who have a family history of lung cancer (≥1 first-degree relative or ≥2 second-degree relatives).
Who may be able to join
- ≥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.
Who cannot join
- None
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Massachusetts General Hospital
Boston, Massachusetts, 02114
Allison Chang, MD, 617-724-4000, aechang@mgb.org
About this study
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.
Read the 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.
- Conditions
- Family History of Lung Cancer
- Being tested
- CT scan, Sybil
- Keywords
- Screening, CT Scan Images, Lung Cancer
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CT scanDiagnostic test
Previously obtained computed tomography scan
SybilOther
Image-based deep learning model
| Study group | Role | What they receive |
|---|---|---|
| Retrospective CT scan | Participants will contribute images and corresponding radiology reports from at least one retrospective CT chest scan. Receives: Diagnostic Test: CT scan; Other: Sybil |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sybil's performance in predicting future lung cancer diagnoses
We will estimate future lung cancer diagnoses using the area under the receiver operating curve (AUROC).
Measured: 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
We will compare the distribution of Sybil scores between participants in the LEGACY study and National Lung Screening Trial.
Measured: From receival of retrospective CT scan for up to 2 years.
Incidence and prevalence of lung cancer in the study population
We will estimate the incidence of lung cancer in the LEGACY population.
Measured: From receival of retrospective CT scan for up to 2 years.
Incidence, prevalence, and characteristics of lung nodules in this population
We will estimate the incidence, prevalence, and characteristics of lung nodules in the LEGACY population.
Measured: From receival of retrospective CT scan for up to 2 years.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2026
- Study start17 Jun 2026
- Primary completion31 Dec 2033 (estimated)
- Study completion31 Dec 2035 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Family History of Lung CancerStudies of the same condition, open and under way.
- More trials run by Massachusetts General HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 2,250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Massachusetts General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Allison Chang, Massachusetts General Hospital
- Investigators
- Allison Chang, MD, Principal investigator, Massachusetts General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07600801
- Sponsor's protocol ID
- 26-054
- Status last verified
- Aug 2026
- First submitted
- 14 May 2026
- Last updated
- 25 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Study at a glance
From the registry record, last updated by the sponsor on 25 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol B
- ClinicalTrials.gov ID
- NCT07600801
- Acronym / sponsor ID
- 26-054
- Conditions
- Family History of Lung Cancer
- Keywords
- Screening, CT Scan Images, Lung Cancer
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,250 participants (estimated)
- Lead sponsor
- Massachusetts General HospitalUniversity, hospital or other
- Study start
- 17 Jun 2026 (actual)
- Primary completion
- 31 Dec 2033 (estimated)
- Study completion
- 31 Dec 2035 (estimated)
- First posted
- 22 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retrospective CT scan | – | Diagnostic Test: CT scan, Other: SybilParticipants will contribute images and corresponding radiology reports from at least one retrospective CT chest scan. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- This study will enroll individuals who have a family history of lung cancer (≥1 first-degree relative or ≥2 second-degree relatives).
Key inclusion criteria (5 listed)
- ≥18 years of age
- Positive family history of lung cancer (defined as):
- Willing to provide images from at least one previously obtained CT Chest scan, if available.
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CT scanPreviously obtained computed tomography scan | Diagnostic test | Retrospective CT scan |
| SybilImage-based deep learning model | Other | Retrospective CT scan |
Primary outcome
Sybil's performance in predicting future lung cancer diagnoses
Time frame: 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).
3 secondary outcomes
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Aug 2026.
- Individual participant dataSharing planned: Yes. 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.
Related trials for Family History of Lung Cancer
RecruitingObservational study
Development of Non-Invasive Prenatal Diagnosis for Single Gene Disorders
Invasive PreNatal Diagnosis in a Context of Family History of Single-gene Disorders, Including, Sickle Cell Disease
Enrolling by invitation
Personalizing Veterans' Lung Cancer Screening and Diagnosis
Lung Cancer Screening, Military Service-related Environmental and Occupational Exposures
Not yet recruiting
LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol A
Family History of Lung Cancer
RecruitingObservational study
Screening for Lung Cancer in Subjects With Family History of Lung Cancer
Lung Cancer
RecruitingObservational study
Taiwan Real-world LDCT Screening Behavior and Outcome Research for High Risk Subjects Based on Health Promotion Administration
Lung Cancer, High-Risk Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Allison Chang, MD principal investigator
Massachusetts General Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Family History of Lung Cancer, run by Massachusetts General Hospital at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 25 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Allison Chang, MD (617-724-4000, aechang@mgb.org), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sybil's performance in predicting future lung cancer diagnoses (time frame: From date of receival of retrospective CT scan for up to 2 years.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Aug 2026.