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

22. maj 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 with a family history of lung cancer.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

2250

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

N/A

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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

Beskrivelse

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

Studieplan

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

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
Retrospective CT scan
Participants will contribute images and corresponding radiology reports from at least one retrospective CT chest scan.
Diagnostisk test: CT scan
Previously obtained computed tomography scan
Andet: Sybil
Image-based deep learning model

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Sybil's performance in predicting future lung cancer diagnoses
Tidsramme: From date of receival of retrospective CT scan for up to 2 years.
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.

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Distribution of Sybil lung cancer risk scores compared to participants in the NLST clinical trial
Tidsramme: From receival of retrospective CT scan for up to 2 years.
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
Tidsramme: From receival of retrospective CT scan for up to 2 years.
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
Tidsramme: From receival of retrospective CT scan for up to 2 years.
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.

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)

15. juni 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

14. maj 2026

Først opslået (Faktiske)

22. maj 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

27. maj 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

22. maj 2026

Sidst verificeret

1. maj 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • 26-054

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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Betingelser

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Placeringer

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