LEGACY: Lung Cancer Screening in Individuals With a Lung Cancer Family History-Protocol A

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Family History of Lung Cancer
• Intervention / Treatment: Other: Sybil; Diagnostic test: CT scan

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 Type: Interventional
• Enrollment (Estimated): 250
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Allison Chang, MD; Phone Number: 617-724-4000; Email: aechang@mgb.org

Study Locations

• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02114
      • Massachusetts General Hospital
      • Contact: Allison Chang, MD; Phone Number: 617-724-4000; Email: aechang@mgb.org

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• 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)

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: Chest CT Scan; Participants will undergo a single prospective low-dose non-contrast enhanced chest CT within 6 months of study enrollment.	Other: Sybil; Image-based deep learning model; Diagnostic test: CT scan; Computed tomography scan

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Annually, from time of initial CT scan to up to 5 years after the scan.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	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.	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.	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.	At time of each provided CT scan to up to 5 years after the scan.

Collaborators and Investigators

• Sponsor: Massachusetts General Hospital
• Investigators: Principal Investigator: Allison Chang, MD, Massachusetts General Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): October 6, 2026
• Primary Completion (Estimated): December 31, 2033
• Study Completion (Estimated): December 31, 2035

Study Registration Dates

• First Submitted: May 14, 2026
• First Submitted That Met QC Criteria: July 2, 2026
• First Posted (Actual): July 6, 2026

Study Record Updates

• Last Update Posted (Actual): July 6, 2026
• Last Update Submitted That Met QC Criteria: July 2, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: Lung cancer; Screening; CT Scan
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Diagnostic Techniques and Procedures; Diagnosis; Tomography; Diagnostic Imaging; Radiography; Image Interpretation, Computer-Assisted; Radiographic Image Enhancement; Image Enhancement; Photography; Tomography, X-Ray; Tomography, X-Ray Computed
• Other Study ID Numbers: 26-044

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: 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 Sharing Time Frame: Data can be shared no earlier than 1 year following the date of publication
• IPD Sharing Access Criteria: Contact the Partners Innovations team at http://www.partners.org/innovation
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No