Family History of Lung CancerNCT07685028
LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol A
A study testing CT scan and Sybil in people with 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Family History of Lung Cancer
- What is being tested?
- CT scan and Sybil
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 250 participants
- Where
- 1 siteUnited States
- Study dates
- 6 Oct 2026to 31 Dec 2035
- Phase
- Interventional
- 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 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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)
Who cannot join
- 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.
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
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.
Read the detailed description
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.
- Conditions
- Family History of Lung Cancer
- Being tested
- CT scan, Sybil
- Keywords
- CT Scan, Lung cancer, Screening
- 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
Computed tomography scan
SybilOther
Image-based deep learning model
| Study group | Role | What they receive |
|---|---|---|
| Chest CT Scan | Other | Participants will undergo a single prospective low-dose non-contrast enhanced chest CT within 6 months of study enrollment. 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
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.
Measured: Annually, from time of initial CT scan to up to 5 years after the scan.
Secondary outcomes (5)
Compare the distribution of Sybil lung cancer risk scores in this trial to the distribution of Sybil risk scores from the NLST clinical trial
Investigators will compare the distribution of Sybil scores (ranging from 0-1) from participants in this study with the distribution of Sybil scores from historical data from participants in the National Lung Screening Trial.
Measured: 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
Investigators will estimate the incidence and prevalence of lung cancer in the LEGACY population. Incidence will be reported per person per year. Prevalence will be reported separately as a measure over the 5-year study follow up period.
Measured: Annually, from time of initial CT scan to up to 5 years after the scan.
Incidence of lung nodules in this population
Investigators will estimate the incidence of lung nodules in the LEGACY population. Incidence will be measured per person per year.
Measured: Annually, from time of initial CT scan to up to 5 years after the scan.
Prevalence of lung nodules in this population
Investigators will estimate the prevalence of lung nodules in the LEGACY population. This will be measured over the 5-year study follow up period.
Measured: Annually, from time of initial CT scan to up to 5 years after the scan.
Describe the characteristics of lung nodules in this population
Investigators will describe the characteristics of lung nodules in the study population, including but not limited to size, location, and attenuation.
Measured: At time of each provided CT scan to up to 5 years after the scan.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2026
- Study start6 Oct 2026 (estimated)
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 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
- NCT07685028
- Sponsor's protocol ID
- 26-044
- Status last verified
- Jul 2026
- First submitted
- 14 May 2026
- Last updated
- 6 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- 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 6 Jul 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 A
- ClinicalTrials.gov ID
- NCT07685028
- Acronym / sponsor ID
- 26-044
- Conditions
- Family History of Lung Cancer
- Keywords
- CT Scan, Lung cancer, Screening
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- Massachusetts General HospitalUniversity, hospital or other
- Study start
- 6 Oct 2026 (estimated)
- Primary completion
- 31 Dec 2033 (estimated)
- Study completion
- 31 Dec 2035 (estimated)
- First posted
- 6 Jul 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.
Detailed description
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.
Study design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Chest CT Scan | Other | Diagnostic Test: CT scan, Other: SybilParticipants will undergo a single prospective low-dose non-contrast enhanced chest CT within 6 months of study enrollment. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (8 listed)
- 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)
Key exclusion criteria (4 listed)
- 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.
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 scanComputed tomography scan | Diagnostic test | Chest CT Scan |
| SybilImage-based deep learning model | Other | Chest CT Scan |
Primary outcome
Sybil's performance in predicting future lung cancer diagnoses
Time frame: 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.
5 secondary outcomes
Compare the distribution of Sybil lung cancer risk scores in this trial to the distribution of Sybil risk scores from the NLST clinical trial
Time frame: 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
Time frame: Annually, from time of initial CT scan to up to 5 years after the scan.
Incidence of lung nodules in this population
Time frame: Annually, from time of initial CT scan to up to 5 years after the scan.
Prevalence of lung nodules in this population
Time frame: Annually, from time of initial CT scan to up to 5 years after the scan.
Describe the characteristics of lung nodules in this population
Time frame: At time of each provided CT scan to up to 5 years after the scan.
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 6 Jul 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.
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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?
A study testing CT scan and Sybil in people with 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 8 inclusion and 4 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: opening soon, as of the sponsor's last update on 6 Jul 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: Annually, from time of initial CT scan to up to 5 years after the scan.). 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 6 Jul 2026.