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

Massachusetts General Hospital2,250 enrolled

Overview

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.

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

2,250

Conditions

Family History of Lung Cancer

Interventions

CT scanDIAGNOSTIC_TEST

Previously obtained computed tomography scan

SybilOTHER

Image-based deep learning model

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Locations (1)

Massachusetts General Hospital

Boston, Massachusetts, United States

RECRUITING

Outcomes

Primary Outcomes

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).

Time frame: From date of receival of retrospective CT scan for up to 2 years.

Secondary Outcomes

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.

Time frame: 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.

Time frame: 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.

Time frame: From receival of retrospective CT scan for up to 2 years.

Central Contacts

Allison Chang, MD

CONTACT

617-724-4000 aechang@mgb.org

Data from ClinicalTrials.gov

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