Clinical trial · Observational
A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| No intervention. | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Women undergoing breast cancer screening with MRI
- description
- No interventions are administered. Data is retrospectively collected in an anonymized way after ethical approval at each site.
- interventionNames
- Other: No intervention.
Primary outcomes (1)
- measure
- Diagnostic performance for breast cancer detection (Sensitivity and specificity)
- timeFrame
- 5 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
- Maximum age
- 90 Years
Show eligibility criteria text
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)
References
Publications (1)
- BACKGROUNDSaldanha OL, Muti HS, Grabsch HI, Langer R, Dislich B, Kohlruss M, Keller G, van Treeck M, Hewitt KJ, Kolbinger FR, Veldhuizen GP, Boor P, Foersch S, Truhn D, Kather JN. Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning. Gastric Cancer. 2023 Mar;26(2):264-274. doi: 10.1007/s10120-022-01347-0. Epub 2022 Oct 20. PMID 36264524