Breast CancerODELIANCT05698056
A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
An observational study of Breast Cancer, run by Technische Universität Dresden at 2 sites in Germany.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 90 years, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 25,000 participants
- Where
- 2 sitesGermany
- Study dates
- 1 Jan 2023to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- Women only
- Adult, Older adult
Study population: Retrospective magnetic resonance imaging data of women undergoing breast cancer screening.
Who may be able to join
- Female
- age at the MRI examination from 18-90 years
Who cannot join
- 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)
Trial locations and contacts
2 sites in 1 country.
Germany2 sites
Daniel Truhn
Aachen, North Rhine-Westphalia, 52074
Jakob Nikolas Kather
Dresden, Saxony, 01309
About this study
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.
Read the 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.
- Conditions
- Breast Cancer
- Being tested
- No intervention.
- Keywords
- artificial intelligence, biomarker, image analysis, MRI, radiology
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention.Other
No intervention.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: No intervention. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jan 2023
- Study start1 Jan 2023
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- More trials run by Technische Universität DresdenThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 25,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Technische Universität Dresden
- Sponsor type
- University, hospital or other
- Collaborators
- European Institute for Biomedical Imaging Research (EIBIR), Austria, University Hospital, Aachen, Vall d'Hebron Institute of Oncology, Mitera Hospital, Radboud University Medical Center, UMC Utrecht, Ribera Salud Hospitals, Spain, Fraunhofer Institute for Digital Medicine (MEVIS), Germany, University Hospital, Zürich, Cambridge University Hospitals NHS Foundation Trust
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05698056
- Sponsor's protocol ID
- ODELIA
- Acronym
- ODELIA
- Status last verified
- Feb 2024
- First submitted
- 16 Jan 2023
- Last updated
- 28 Feb 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Data are collected at multiple study centers and the decision whether or not to release IPD is made at each study center.
Publications
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.
PubMed 36264524
Study at a glance
From the registry record, last updated by the sponsor on 28 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
- ClinicalTrials.gov ID
- NCT05698056
- Acronym / sponsor ID
- ODELIA / ODELIA
- Conditions
- Breast Cancer
- Keywords
- artificial intelligence, biomarker, image analysis, MRI, radiology
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 25,000 participants (estimated)
- Lead sponsor
- Technische Universität DresdenUniversity, hospital or other
- Collaborators
- European Institute for Biomedical Imaging Research (EIBIR), Austria, University Hospital, Aachen, Vall d'Hebron Institute of Oncology, Mitera Hospital, Radboud University Medical Center, UMC Utrecht, Ribera Salud Hospitals, Spain, Fraunhofer Institute for Digital Medicine (MEVIS), Germany, University Hospital, Zürich, Cambridge University Hospitals NHS Foundation Trust
- Study start
- 1 Jan 2023 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 26 Jan 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Women undergoing breast cancer screening with MRI | – | Other: No intervention.No interventions are administered. Data is retrospectively collected in an anonymized way after ethical approval at each site. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- Female only
- Age groups
- Adult, Older adult
- Study population
- Retrospective magnetic resonance imaging data of women undergoing breast cancer screening.
Key inclusion criteria (2 listed)
- Female
- age at the MRI examination from 18-90 years
Key exclusion criteria (2 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention.No intervention. | Other | Women undergoing breast cancer screening with MRI |
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Feb 2024.
- Background: PubMed 36264524Saldanha 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.
- Consortium websiteLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided. Data are collected at multiple study centers and the decision whether or not to release IPD is made at each study center.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Technische Universität Dresden at 2 sites in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 28 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance for breast cancer detection (Sensitivity and specificity) (time frame: 5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05698056, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Feb 2024.