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Impact de l'intelligence artificielle sur le dépistage du cancer du sein

5 novembre 2026 mis à jour par: Jonsson Comprehensive Cancer Center

Évaluation de l'impact de l'intelligence artificielle sur la qualité et l'efficacité du dépistage du cancer du sein: un essai contrôlé randomisé prospectif

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.

Aperçu de l'étude

Statut

Recrutement

Les conditions

Intervention / Traitement

Description détaillée

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).

Type d'étude

Interventionnel

Inscription (Estimé)

400000

Phase

  • Phase 4

Contacts et emplacements

Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.

Coordonnées de l'étude

Lieux d'étude

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

Critères de participation

Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.

Critère d'éligibilité

Âges éligibles pour étudier

18 ans et plus (Adulte, Adulte plus âgé)

Accepte les volontaires sains

Oui

Sexes éligibles pour l'étude

Tout

La description

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

Plan d'étude

Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.

Comment l'étude est-elle conçue ?

Détails de conception

  • Objectif principal: Dépistage
  • Répartition: Randomisé
  • Modèle interventionnel: Affectation parallèle
  • Masquage: Seul

Armes et Interventions

Groupe de participants / Bras
Intervention / Traitement
Comparateur actif: 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).
Dispositif: Artificial intelligence (AI) decision-support tool

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.
Aucune intervention: 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).

Que mesure l'étude ?

Principaux critères de jugement

Mesure des résultats
Description de la mesure
Délai
Cancer detection rate
Délai: 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
Délai: 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

Mesures de résultats secondaires

Mesure des résultats
Description de la mesure
Délai
Interval cancer rate (i.e., false-negative rate)
Délai: 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
Délai: 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
Délai: 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
Délai: 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
Délai: 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)
Délai: 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

Collaborateurs et enquêteurs

C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.

Parrainer

Jonsson Comprehensive Cancer Center

Collaborateurs

Patient-Centered Outcomes Research Institute
University of California, San Diego
University of California, Los Angeles
University of Miami
University of Wisconsin, Madison
Boston Medical Center
University of California, Davis
University of Washington
California Breast Cancer Research Program

Les enquêteurs

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

Dates d'enregistrement des études

Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.

Dates principales de l'étude

Début de l'étude (Réel)

15 octobre 2025

Achèvement primaire (Estimé)

1 mars 2028

Achèvement de l'étude (Estimé)

1 mars 2030

Dates d'inscription aux études

Première soumission

11 avril 2025

Première soumission répondant aux critères de contrôle qualité

11 avril 2025

Première publication (Réel)

18 avril 2025

Mises à jour des dossiers d'étude

Dernière mise à jour publiée (Réel)

9 novembre 2026

Dernière mise à jour soumise répondant aux critères de contrôle qualité

5 novembre 2026

Dernière vérification

1 novembre 2026

Plus d'information

Termes liés à cette étude

Mots clés

Autres numéros d'identification d'étude

  • 24-1192

Plan pour les données individuelles des participants (IPD)

Prévoyez-vous de partager les données individuelles des participants (DPI) ?

OUI

Description du régime IPD

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.

Informations sur les médicaments et les dispositifs, documents d'étude

Étudie un produit pharmaceutique réglementé par la FDA américaine

Non

Étudie un produit d'appareil réglementé par la FDA américaine

Oui

produit fabriqué et exporté des États-Unis.

Non

Ces informations ont été extraites directement du site Web clinicaltrials.gov sans aucune modification. Si vous avez des demandes de modification, de suppression ou de mise à jour des détails de votre étude, veuillez contacter register@clinicaltrials.gov. Dès qu'un changement est mis en œuvre sur clinicaltrials.gov, il sera également mis à jour automatiquement sur notre site Web .

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