A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence (ODELIA)

February 27, 2024 updated by: Technische Universität Dresden
ODELIA is a project that aims to improve breast cancer detection in magnetic resonance imaging by utilizing artificial intelligence and swarm learning (MRI). The project will create an open-source swarm learning software framework that will be used to train AI models for breast cancer detection. These models' performance will be compared to that of conventional AI models, and the results will be used to assess the effectiveness of swarm learning in improving the accuracy and robustness of AI models. The project will use retrospective, anonymized breast MRI datasets with manual ground truth labels for cancer presence. The study is not associated with any patient treatment or intervention. The project's goal is to provide evidence of the clinical benefits of swarm learning in the context of breast cancer screening, such as accelerated development, improved performance, and robust generalizability.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

Artificial Intelligence (AI) is set to revolutionize healthcare as its diagnostic performance approaches that of clinical experts. In particular, in cancer screening, AI could help patients to make better-informed decisions and reduce medical error. However, this requires large datasets whose collection faces severe practical, ethical and legal obstacles. These obstacles could potentially be overcome with swarm learning (SL) where partners jointly train AI models without sharing any data. Yet, access to SL technology is currently limited because no studies have implemented SL in a true multinational setup, no freely usable implementation of SL is available, researchers & healthcare providers have no experience with setting up SL networks and policymakers are currently unaware of the broader implications of SL.

ODELIA will aim to solve these issues: ODELIA will build an open-source software framework for SL, providing an assembly line for the streamlined development of AI solutions in a preclinical setting. To serve as a blueprint for future SL-based AI systems, ODELIA partners collaborate as a consortium to develop AI models for the detection of breast cancer in magnetic resonance imaging (MRI). The size of ODELIA's distributed database will be substantial and ODELIA's AI models could reach expert-level performance for breast cancer screening.

Thereby, ODELIA will could not just deliver a useful medical application, but provide evidence to summarize the clinical benefit of SL in terms of accelerated development, increased performance and robust generalizability.

To achieve this, ODELIA partners will collect retrospective, anonymized breast MRI datasets with manual ground truth labels for the presence of cancer, and will train AI models conventionelly and via SL. The performance of these technical approaches will be compared. The aim of the study is to test the methodology of Swarm Learning and the performance of AI algorithms developed within ODELIA on retrospective data. There will be no effect on treatment of patients as all evaluations will be done retrospectively. No patient treatment or any intervention is associated with the study.

Study Type

Observational

Enrollment (Estimated)

25000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Germany
    • NRW
      • Aachen, NRW, Germany, 52074
        • Daniel Truhn
    • Saxony
      • Dresden, Saxony, Germany, 01309
        • Jakob Nikolas Kather

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 90 years (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Retrospective magnetic resonance imaging data of women undergoing breast cancer screening.

Description

Inclusion Criteria:

  • Female
  • age at the MRI examination from 18-90 years

Exclusion Criteria:

  • insufficient image quality as judged by a blinded radiologist before start of the analysis
  • non-identifiably ground truth (i.e., diagnosis has not yet been established)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Women undergoing breast cancer screening with MRI
No interventions are administered. Data is retrospectively collected in an anonymized way after ethical approval at each site.
Other: No intervention.
No intervention.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic performance for breast cancer detection (Sensitivity and specificity)
Time Frame: 5 years
Diagnostic performance for breast cancer detection (Sensitivity and specificity) compared to the gold stnandard method of expert-based assessment of breast MRI, may be summarized in a receiver operating characteristic curve for multiple threshold values, comparing multiple technical approaches, including swarm-learning based AI models and local AI models.
5 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Technische Universität Dresden

Collaborators

Radboud University Medical Center
UMC Utrecht
Cambridge University Hospitals NHS Foundation Trust
University Hospital, Zürich
Vall d'Hebron Institute of Oncology
University Hospital, Aachen
European Institute for Biomedical Imaging Research (EIBIR), Austria
Mitera Hospital
Ribera Salud Hospitals, Spain
Fraunhofer Institute for Digital Medicine (MEVIS), Germany

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2023

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

January 16, 2023

First Submitted That Met QC Criteria

January 16, 2023

First Posted (Actual)

January 26, 2023

Study Record Updates

Last Update Posted (Actual)

February 28, 2024

Last Update Submitted That Met QC Criteria

February 27, 2024

Last Verified

February 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • ODELIA

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

Data are collected at multiple study centers and the decision whether or not to release IPD is made at each study center.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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