Ignite Creation Date:
2025-12-24 @ 11:31 PM
Ignite Modification Date:
2025-12-25 @ 9:19 PM
Study NCT ID:
NCT05698056
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-02-28
First Post:
2023-01-16
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001943', 'term': 'Breast Neoplasms'}], 'ancestors': [{'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D001941', 'term': 'Breast Diseases'}, {'id': 'D012871', 'term': 'Skin Diseases'}, {'id': 'D017437', 'term': 'Skin and Connective Tissue Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 25000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2023-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-02', 'completionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-02-27', 'studyFirstSubmitDate': '2023-01-16', 'studyFirstSubmitQcDate': '2023-01-16', 'lastUpdatePostDateStruct': {'date': '2024-02-28', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-01-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'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.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['artificial intelligence', 'biomarker', 'image analysis', 'MRI', 'radiology'], 'conditions': ['Breast Cancer']}, 'referencesModule': {'references': [{'pmid': '36264524', 'type': 'BACKGROUND', 'citation': '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.'}], 'seeAlsoLinks': [{'url': 'http://www.odelia.ai', 'label': 'Consortium website'}]}, 'descriptionModule': {'briefSummary': "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.", 'detailedDescription': "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.\n\nODELIA 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.\n\nThereby, 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.\n\nTo 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."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '18 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Retrospective magnetic resonance imaging data of women undergoing breast cancer screening.', 'genderDescription': 'Female patients only, as defined per the European Society of Breast Imaging (EUSOBI) screening guidelins of 2023', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Female\n* age at the MRI examination from 18-90 years\n\nExclusion Criteria:\n\n* insufficient image quality as judged by a blinded radiologist before start of the analysis\n* non-identifiably ground truth (i.e., diagnosis has not yet been established)'}, 'identificationModule': {'nctId': 'NCT05698056', 'acronym': 'ODELIA', 'briefTitle': 'A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence', 'organization': {'class': 'OTHER', 'fullName': 'Technische Universität Dresden'}, 'officialTitle': 'A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence', 'orgStudyIdInfo': {'id': 'ODELIA'}}, 'armsInterventionsModule': {'armGroups': [{'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.']}], 'interventions': [{'name': 'No intervention.', 'type': 'OTHER', 'description': 'No intervention.', 'armGroupLabels': ['Women undergoing breast cancer screening with MRI']}]}, 'contactsLocationsModule': {'locations': [{'zip': '52074', 'city': 'Aachen', 'state': 'North Rhine-Westphalia', 'country': 'Germany', 'facility': 'Daniel Truhn', 'geoPoint': {'lat': 50.77664, 'lon': 6.08342}}, {'zip': '01309', 'city': 'Dresden', 'state': 'Saxony', 'country': 'Germany', 'facility': 'Jakob Nikolas Kather', 'geoPoint': {'lat': 51.05089, 'lon': 13.73832}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': 'Data are collected at multiple study centers and the decision whether or not to release IPD is made at each study center.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Technische Universität Dresden', 'class': 'OTHER'}, 'collaborators': [{'name': 'European Institute for Biomedical Imaging Research (EIBIR), Austria', 'class': 'UNKNOWN'}, {'name': 'University Hospital, Aachen', 'class': 'OTHER'}, {'name': "Vall d'Hebron Institute of Oncology", 'class': 'OTHER'}, {'name': 'Mitera Hospital', 'class': 'OTHER'}, {'name': 'Radboud University Medical Center', 'class': 'OTHER'}, {'name': 'UMC Utrecht', 'class': 'OTHER'}, {'name': 'Ribera Salud Hospitals, Spain', 'class': 'UNKNOWN'}, {'name': 'Fraunhofer Institute for Digital Medicine (MEVIS), Germany', 'class': 'UNKNOWN'}, {'name': 'University Hospital, Zürich', 'class': 'OTHER'}, {'name': 'Cambridge University Hospitals NHS Foundation Trust', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}