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

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.

Study Overview

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

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 Type: Observational
• Enrollment (Estimated): 2250

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: N/A

• Sampling Method: Non-Probability Sample

Study Population

This study will enroll individuals who have a family history of lung cancer (≥1 first-degree relative or ≥2 second-degree relatives).

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Retrospective CT scan; Participants will contribute images and corresponding radiology reports from at least one retrospective CT chest scan.	Diagnostic test: CT scan; Previously obtained computed tomography scan; Other: Sybil; Image-based deep learning model

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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).	From date of receival of retrospective CT scan for up to 2 years.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	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.	From receival of retrospective CT scan for up to 2 years.

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): June 15, 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: May 14, 2026
• First Posted (Actual): May 22, 2026

Study Record Updates

• Last Update Posted (Actual): May 27, 2026
• Last Update Submitted That Met QC Criteria: May 22, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Lung Cancer; Screening; CT Scan Images
• 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: 26-054

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: No