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Auswirkungen der künstlichen Intelligenz auf das Brustkrebs -Screening

5. November 2026 aktualisiert von: Jonsson Comprehensive Cancer Center

Bewertung der Auswirkungen künstlicher Intelligenz auf die Qualität und Wirksamkeit von Brustkrebs -Screening: Eine prospektive randomisierte kontrollierte Studie

The goal of this clinical trial is to compare patient-centered outcomes when screening digital breast tomosynthesis (DBT) exams are interpreted with versus without a leading FDA-cleared artificial intelligence (AI) decision-support tool in real-world U.S. settings and to assess patients' and radiologists' perspectives on AI in medicine.

The main question it aims to answer is: Does an FDA-cleared AI decision-support tool for digital tomosynthesis (DBT) improve screening outcomes in real world US clinical settings?

This trial will include all interpreting radiologists and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (University of California, Los Angeles (UCLA), University of California, San Diego (UCSD), University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period.

All screening mammograms at these facilities will be randomized to either intervention (radiologist assisted by an AI decision support tool) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient screening outcomes.

We are targeting 400,000 screening exams across the participating health systems in this trial.

Studienübersicht

Status

Rekrutierung

Bedingungen

Intervention / Behandlung

Detaillierte Beschreibung

During the RCT the AI support tool will be randomized to be turned on or off (1:1) at the mammography exam level. Patients who return for screening exams in year 2 of recruitment will be randomized again (e.g., they will not retain their prior randomization). Radiologists will not be able to sort exams based on AI availability or AI scores. Randomizing by exam level will ensure that we capture a substantial number of interpretations with vs. without AI for each radiologist, allowing for quantification of the radiologist-level AI learning curve. We are not randomizing at the facility level as some radiologists interpret exams acquired at different facilities on the same day. By randomizing AI at the exam level, we will have the best ability to estimate and adjust for temporal trends in screening outcomes across individual radiologists. Randomization across large regional health systems will be managed independently at each participating site.

Our RCT randomizes screening mammography exams to be interpreted either with or without an AI decision-support tool. As a result, radiologists cannot be blinded to study arm during screening mammography interpretation. However, interpreting radiologists and facility staff (e.g., those scheduling the exams) will not know in advance which patients will be randomized to the AI tool. Randomization occurs within minutes after the breast imaging acquisition (i.e., when the mammography technologist captures the images) by an automated system that was developed by a third-party AI platform and successfully piloted at UCLA. Thus, the AI data (or lack thereof) is embedded within the mammogram before the radiologist opens the exam, preventing any option to "add AI" to an exam randomized to be interpreted without AI. Radiologists will be aware of AI availability only at the time of interpretation, as AI information will appear upon opening the exam (e.g., the AI information pops up with the exam images).

Studientyp

Interventionell

Einschreibung (Geschätzt)

400000

Phase

  • Phase 4

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studienorte

    • California
      • Los Angeles, California, Vereinigte Staaten, 90024
        • Rekrutierung
        • University of California Los Angeles Health System
        • Hauptermittler:
          • Hannah S. Milch, MD
        • Kontakt:
        • Hauptermittler:
          • Joann G Elmore, MD, MPH
      • San Diego, California, Vereinigte Staaten, 92093
        • Rekrutierung
        • University of California, San Diego
        • Hauptermittler:
          • Haydee Ojeda-Fournier, MD
        • Kontakt:
    • Florida
      • Miami, Florida, Vereinigte Staaten, 33136
        • Rekrutierung
        • University of Miami Health System
        • Kontakt:
        • Hauptermittler:
          • Jose Net, MD
    • Massachusetts
      • Boston, Massachusetts, Vereinigte Staaten, 02118
        • Rekrutierung
        • Boston Medical Center
        • Kontakt:
        • Hauptermittler:
          • Clare Poynton, MD, PhD
    • Washington
      • Seattle, Washington, Vereinigte Staaten, 98195
        • Rekrutierung
        • University of Washington Health System
        • Kontakt:
        • Hauptermittler:
          • Janie Lee, MD, MSc
    • Wisconsin
      • Madison, Wisconsin, Vereinigte Staaten, 53706
        • Rekrutierung
        • University of Wisconsin-Madison
        • Kontakt:
        • Hauptermittler:
          • Christoph Lee, MD, MSc

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

18 Jahre und älter (Erwachsene, Älterer Erwachsener)

Akzeptiert gesunde Freiwillige

Ja

Studienberechtigte Geschlechter

Alle

Beschreibung

This trial will include all radiologists interpreting screening mammography and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (UCLA, UC San Diego, University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period. Individuals must meet the following eligiblity criteria.

Inclusion Criteria:

  1. Be at least 18 years of age or older
  2. Receive a screening mammogram at one of the participating breast imaging facilities OR be a radiologist who interprets screening mammograms at one of the participating breast imaging facilities.

Exclusion Criteria:

1. Patients who have opted out of all research at the health system

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

  • Hauptzweck: Screening
  • Zuteilung: Zufällig
  • Interventionsmodell: Parallele Zuordnung
  • Maskierung: Single

Waffen und Interventionen

Teilnehmergruppe / Arm
Intervention / Behandlung
Aktiver Komparator: Intervention (radiologist assisted by AI)

3D screening exams randomized to this arm will be interpreted by the radiologist assisted by the AI decision-support tool (i.e., intervention).

The intervention is an AI decision-support tool to help radiologists interpret 3D screening mammograms. For exams randomized to this intervention arm, the first image displayed to the radiologist upon opening an exam on the viewing station will be a one-page, standardized AI report showing the overall exam risk (elevated, intermediate, or low), image region markings, lesion scores from 1-100 (100 being the highest suspicion), bounding boxes, and relevant slice locations for 3D exams. Radiologists can toggle markings on/off and retain full control over the final interpretation of the exam as positive or negative (i.e., they can choose to ignore the AI information).

Randomization occurs 1:1 at the exam level via automated code at image acquisition. Returning patients in year two will be re-randomized. Radiologists cannot filter their exam lists by AI availability or risk, and randomization will be independently managed at each participating health system.

Kein Eingriff: Standard care (radiologist alone)

3D screening exams randomized to this arm will be interpreted in accordance with standard care (i.e., interpreted by the radiologist alone, without an AI decision-support tool's assistance).

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Cancer detection rate
Zeitfenster: Cancer diagnosed within 90 days of positive study entry screening mammogram

Number of screening exams recommended for breast biopsy (final Breast Imaging- Reporting and Data System [BI-RADS] assessment of 4 or 5) resulting in detected cancer, per 1,000 screening exams

Cancer diagnosed within 90 days of positive study entry screening mammogram
Recall rate
Zeitfenster: Through study completion, an average of 1 year

Number of screening exams recalled for diagnostic work-up (initial BI-RADS assessment of 0, 3, 4, or 5), per 1,000 screening exams

Through study completion, an average of 1 year

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Interval cancer rate (i.e., false-negative rate)
Zeitfenster: Cancer diagnosed within 365 days of a negative study entry screening mammogram

Number of screening exams with a negative assessment (final BI-RADS assessment of 1 or 2) and breast cancer diagnosed within 1 year, per 1,000 screening exams

Cancer diagnosed within 365 days of a negative study entry screening mammogram
False positive recall rate
Zeitfenster: No cancer diagnosed within 365 days of a positive study entry screening mammogram

Proportion of screening exams recalled for additional imaging (final BI-RADS assessment of 1, 2, or 3), with no breast cancer diagnosed within 1 year

No cancer diagnosed within 365 days of a positive study entry screening mammogram
False positive short-interval follow-up recommendation rate
Zeitfenster: No cancer diagnosed within 365 days of a positive study entry screening mammogram

Proportion of screening exams recalled for short-interval follow-up (final BI-RADS assessment of 3) with no breast cancer diagnosed within 1 year

No cancer diagnosed within 365 days of a positive study entry screening mammogram
False positive biopsy recommendation rate
Zeitfenster: No cancer diagnosed within 365 days of a positive study entry screening mammogram

Proportion of screening exams recalled for breast biopsy (final BI-RADS assessment of 4 or 5) with no breast cancer diagnosed within 1 year

No cancer diagnosed within 365 days of a positive study entry screening mammogram
Trust and confidence in AI
Zeitfenster: Years 1,2 and Years 4,5

Trust and confidence in AI gathered from focus group and survey data

Years 1,2 and Years 4,5
Efficiency metrics (only for the UCLA site)
Zeitfenster: Through study completion, an average of 1 year

Interpretation time required for radiologists to interpret each mammogram with versus without AI.

Delivery time, using time stamp data from exam acquisition to delivery of results to patients (aka turnaround time).

Through study completion, an average of 1 year

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

Mitarbeiter

Ermittler

  • Hauptermittler: Diana Miglioretti, PhD, University of California, Davis
  • Hauptermittler: Joann G Elmore, MD, MPH, University of California, Los Angeles

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Tatsächlich)

15. Oktober 2025

Primärer Abschluss (Geschätzt)

1. März 2028

Studienabschluss (Geschätzt)

1. März 2030

Studienanmeldedaten

Zuerst eingereicht

11. April 2025

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

11. April 2025

Zuerst gepostet (Tatsächlich)

18. April 2025

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

9. November 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

5. November 2026

Zuletzt verifiziert

1. November 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Andere Studien-ID-Nummern

  • 24-1192

Plan für individuelle Teilnehmerdaten (IPD)

Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?

JA

Beschreibung des IPD-Plans

A de-identified dataset from this study will be deposited in the Patient-Centered Outcomes Data Repository (PCODR) housed at the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan, in compliance with PCORI's Policy on Data Management and Data Sharing.

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Ja

Produkt, das in den USA hergestellt und aus den USA exportiert wird

Nein

Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .