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

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.

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Key information

Conditions

Breast Cancer Breast Diseases Breast Neoplasms Neoplasms Neoplasms by Site Skin Diseases Skin and Connective Tissue Diseases

Age range

18 year–90 year

Sex eligibility

Female

Study type

Observational

Primary location

Daniel Truhn, Aachen, North Rhine-Westphalia, Germany

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

No intervention.

Other

No intervention.

Primary outcomes

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.

Sponsors and collaborators

Lead sponsor

Technische Universität Dresden

Other

Collaborators

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

Registry information

Acronym: ODELIA

Important dates

Study start: 2023
Primary completion: 2027
Study completion: 2027
First posted: Jan 26, 2023
Registry last updated: Feb 28, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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