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

2. juli 2026 opdateret af: Allison Chang, Massachusetts General Hospital
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.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Interventionel

Tilmelding (Anslået)

250

Fase

  • Ikke anvendelig

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

  • Navn: Allison Chang, MD
  • Telefonnummer: 617-724-4000
  • E-mail: aechang@mgb.org

Studiesteder

  • Forenede Stater
    • Massachusetts
      • Boston, Massachusetts, Forenede Stater, 02114
        • Massachusetts General Hospital
        • Kontakt:

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Primært formål: Screening
  • Tildeling: N/A
  • Interventionel model: Enkelt gruppeopgave
  • Maskning: Ingen (Åben etiket)

Våben og indgreb

Deltagergruppe / Arm
Intervention / Behandling
Andet: Chest CT Scan
Participants will undergo a single prospective low-dose non-contrast enhanced chest CT within 6 months of study enrollment.
Andet: Sybil
Image-based deep learning model
Diagnostisk test: CT scan
Computed tomography scan

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Sybil's performance in predicting future lung cancer diagnoses
Tidsramme: 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.
Annually, from time of initial CT scan to up to 5 years after the scan.

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Compare the distribution of Sybil lung cancer risk scores in this trial to the distribution of Sybil risk scores from the NLST clinical trial
Tidsramme: 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.
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
Tidsramme: Annually, from time of initial CT scan to up to 5 years after the scan.
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
Tidsramme: Annually, from time of initial CT scan to up to 5 years after the scan.
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
Tidsramme: Annually, from time of initial CT scan to up to 5 years after the scan.
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
Tidsramme: At time of each provided CT scan to up to 5 years after the scan.
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.

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Massachusetts General Hospital

Efterforskere

  • Ledende efterforsker: Allison Chang, MD, Massachusetts General Hospital

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Anslået)

6. oktober 2026

Primær færdiggørelse (Anslået)

31. december 2033

Studieafslutning (Anslået)

31. december 2035

Datoer for studieregistrering

Først indsendt

14. maj 2026

Først indsendt, der opfyldte QC-kriterier

2. juli 2026

Først opslået (Faktiske)

6. juli 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

6. juli 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

2. juli 2026

Sidst verificeret

1. juli 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • 26-044

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

JA

IPD-planbeskrivelse

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-delingstidsramme

Data can be shared no earlier than 1 year following the date of publication

IPD-delingsadgangskriterier

Contact the Partners Innovations team at http://www.partners.org/innovation

IPD-deling Understøttende informationstype

  • STUDY_PROTOCOL
  • SAP
  • ICF

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Kliniske forsøg med Family History of Lung Cancer

Kliniske forsøg med Sybil

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