NCT05698056

Brief 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.

Trial Health

75
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
25,000

participants targeted

Target at P75+ for all trials

Timeline
20mo left

Started Jan 2023

Longer than P75 for all trials

Geographic Reach
1 country

2 active sites

Status
active not recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress67%
Jan 2023Dec 2027

Study Start

First participant enrolled

January 1, 2023

Completed
15 days until next milestone

First Submitted

Initial submission to the registry

January 16, 2023

Completed
10 days until next milestone

First Posted

Study publicly available on registry

January 26, 2023

Completed
4.9 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 31, 2027

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2027

Last Updated

February 28, 2024

Status Verified

February 1, 2024

Enrollment Period

5 years

First QC Date

January 16, 2023

Last Update Submit

February 27, 2024

Conditions

Breast Cancer

Keywords

artificial intelligencebiomarkerimage analysisMRIradiology

Outcome Measures

Primary Outcomes (1)

  • Diagnostic performance for breast cancer detection (Sensitivity and specificity)

    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

Study Arms (1)

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.

Interventions

No intervention.OTHER

No intervention.

Women undergoing breast cancer screening with MRI

Eligibility Criteria

Age18 Years - 90 Years
Sexfemale(Gender-based eligibility)
Gender Eligibility DetailsFemale patients only, as defined per the European Society of Breast Imaging (EUSOBI) screening guidelins of 2023
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

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

You may qualify if:

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

You may not qualify if:

  • 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)

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

Daniel Truhn

Aachen, North Rhine-Westphalia, 52074, Germany

Location

Jakob Nikolas Kather

Dresden, Saxony, 01309, Germany

Location

Related Publications (1)

  • Saldanha 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: 36264524BACKGROUND

Related Links

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

January 16, 2023

First Posted

January 26, 2023

Study Start

January 1, 2023

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2027

Last Updated

February 28, 2024

Record last verified: 2024-02

Locations

DE(2)