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Trial Title:
A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
NCT ID:
NCT05698056
Condition:
Breast Cancer
Conditions: Official terms:
Breast Neoplasms
Conditions: Keywords:
artificial intelligence
biomarker
image analysis
MRI
radiology
Study type:
Observational
Overall status:
Active, not recruiting
Study design:
Time perspective:
Retrospective
Intervention:
Intervention type:
Other
Intervention name:
No intervention.
Description:
No intervention.
Arm group label:
Women undergoing breast cancer screening with MRI
Summary:
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.
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.
Criteria for eligibility:
Study pop:
Retrospective magnetic resonance imaging data of women undergoing breast cancer
screening.
Sampling method:
Non-Probability Sample
Criteria:
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)
Gender:
Female
Gender based:
Yes
Gender description:
Female patients only, as defined per the European Society of Breast Imaging (EUSOBI)
screening guidelins of 2023
Minimum age:
18 Years
Maximum age:
90 Years
Locations:
Facility:
Name:
Daniel Truhn
Address:
City:
Aachen
Zip:
52074
Country:
Germany
Facility:
Name:
Jakob Nikolas Kather
Address:
City:
Dresden
Zip:
01309
Country:
Germany
Start date:
January 1, 2023
Completion date:
December 31, 2027
Lead sponsor:
Agency:
Technische Universität Dresden
Agency class:
Other
Collaborator:
Agency:
European Institute for Biomedical Imaging Research (EIBIR), Austria
Agency class:
Other
Collaborator:
Agency:
University Hospital, Aachen
Agency class:
Other
Collaborator:
Agency:
Vall d'Hebron Institute of Oncology
Agency class:
Other
Collaborator:
Agency:
Mitera Hospital
Agency class:
Other
Collaborator:
Agency:
Radboud University Medical Center
Agency class:
Other
Collaborator:
Agency:
UMC Utrecht
Agency class:
Other
Collaborator:
Agency:
Ribera Salud Hospitals, Spain
Agency class:
Other
Collaborator:
Agency:
Fraunhofer Institute for Digital Medicine (MEVIS), Germany
Agency class:
Other
Collaborator:
Agency:
University Hospital, Zürich
Agency class:
Other
Collaborator:
Agency:
Cambridge University Hospitals NHS Foundation Trust
Agency class:
Other
Source:
Technische Universität Dresden
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05698056
http://www.odelia.ai
