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

NCT07685028 | Not Yet Recruiting DMC FDA Device

• 1 other identifier: 26-044
• Study Type: interventional
• Target: 250
• Locations: 1 country
• Sites: 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.

Conditions

Family History of Lung Cancer

Keywords

CT Scan; Lung cancer; Screening

Outcome Measures

Primary Outcomes (1)

• 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 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

  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

  Annually, from time of initial CT scan to up to 5 years after the scan.

• Incidence of lung nodules in this population

  Annually, from time of initial CT scan to up to 5 years after the scan.

• Prevalence of lung nodules in this population

  Annually, from time of initial CT scan to up to 5 years after the scan.

• Describe the characteristics of lung nodules in this population

  At time of each provided CT scan to up to 5 years after the scan.

Study Arms (1)

Chest CT Scan

OTHER

Participants will undergo a single prospective low-dose non-contrast enhanced chest CT within 6 months of study enrollment.

Diagnostic Test: CT scan; Other: Sybil

Interventions

CT scan DIAGNOSTIC_TEST

Computed tomography scan

Chest CT Scan

Sybil OTHER

Image-based deep learning model

Chest CT Scan

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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)

You may not qualify if:

• 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.

Sponsors & Collaborators

• Massachusetts General Hospital: lead

Study Sites (1)

Massachusetts General Hospital

Boston, Massachusetts, 02114, United States

Location

MeSH Terms

Conditions

Lung Neoplasms

Interventions

Tomography, X-Ray Computed

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Intervention Hierarchy (Ancestors)

Image Interpretation, Computer-Assisted; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Radiographic Image Enhancement; Image Enhancement; Photography; Radiography; Tomography, X-Ray; Tomography

Study Officials

• Allison Chang, MD

  Massachusetts General Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Allison Chang, MD

CONTACT

617-724-4000; aechang@mgb.org

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: May 14, 2026
• First Posted: July 6, 2026
• Study Start (Estimated): October 6, 2026
• Primary Completion (Estimated): December 31, 2033
• Study Completion (Estimated): December 31, 2035
• Last Updated: July 6, 2026

Record last verified: 2026-07

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

Locations

US (1)