Ignite Creation Date:
2025-12-24 @ 6:43 PM
Ignite Modification Date:
2026-01-04 @ 2:59 PM
Study NCT ID:
NCT05968157
Status:
RECRUITING
Last Update Posted:
2025-12-17
First Post:
2023-07-21
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick
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': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 200}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-02-04', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-12', 'completionDateStruct': {'date': '2027-09', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-13', 'studyFirstSubmitDate': '2023-07-21', 'studyFirstSubmitQcDate': '2023-07-21', 'lastUpdatePostDateStruct': {'date': '2025-12-17', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2023-08-01', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'CDR Mirai Assessment versus CDR Traditional High Risk Screening', 'timeFrame': '1.5 years (duration of patient recruitment and outcome data collection)', 'description': 'Cancer detection rate from breast MRI following Mirai assessment of high risk on a screening mammogram performed less than 1 year ago and compared with established CDR in traditional high risk screening.'}], 'secondaryOutcomes': [{'measure': 'Cancer development within study population versus general population of average risk women', 'timeFrame': '1.5 years (duration of patient recruitment and outcome data collection)', 'description': 'On subsequent follow-up with standard of care, assessment of what percentage of the study population develops breast cancer as compared to the general population of women at average risk of breast cancer.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isUnapprovedDevice': True, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'keywords': ['Breast Cancer', 'Screening', 'Artificial Intelligence'], 'conditions': ['Breast Cancer']}, 'descriptionModule': {'briefSummary': 'Accurate risk assessment is essential for the success of population screening programs and early detection efforts in breast cancer. Mirai is a new deep learning model based on full resolution mammograms.\n\nMirai is a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and found to be significantly more accurate than the Tyrer-Cuzick model, a current clinical standard.\n\nThe primary aim of this study is to prospectively quantify the clinical benefit (i.e. MRI/CEM cancer detection rate) of Mirai-based guidelines and to compare them to the current standard of care.\n\n1. Conduct a prospective study where patients who are identified as high risk by Mirai guidelines are invited to receive supplemental MRI within 12 months.\n2. Compare cancer outcomes between patients only identified as high risk by Mirai and patients identified as high risk by existing guidelines The secondary aim is to study the impact of new guidelines by race and ethnicity, to ensure equitable improvements in cancer screening.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '40 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Women who were identified as high risk on the retrospective study (dating from 2017-2025) using MIRAI will be recruited and consented for the prospective study\n* Women over 40 years of age identified as high risk according to traditional guidelines will also be potentially eligible for this study\n* Following consent and enrollment in the study, a participant will subsequently receive the following:\n\n 1. These patients will be invited to receive a supplemental MRI examination currently considered the most sensitive test for breast cancer detection.\n 2. Any positive diagnosis on MRI will be followed by biopsy to confirm \'truth" of diagnosis.\n* To be selected, a given record must include the following:\n\n 1. A report of a routine screening mammogram or diagnostic mammogram, and availability of the DICOM images from that report with the PACS system.\n 2. Reports of all follow up screening and diagnostic studies documented on PACS.\n 3. Some may have interventional procedures (as long as all of these are done at one of Umass sites) and documentation of these biopsy results in the hospitals EHR.\n\nExclusion Criteria:\n\n* Under age 40. Women under 40 years are not routinely xrayed with a mammogram.\n* Xray breast cancer screening imaging study that has artifacts, corruption, or other image quality degradation.\n* Pregnant patients because they do not routinely receive screening mammogram\n* Adult male patients with breast cancer'}, 'identificationModule': {'nctId': 'NCT05968157', 'briefTitle': 'MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick', 'organization': {'class': 'OTHER', 'fullName': 'University of Massachusetts, Worcester'}, 'officialTitle': 'MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick', 'orgStudyIdInfo': {'id': 'STUDY000000485'}, 'secondaryIdInfos': [{'id': 'MIT_s5822', 'type': 'OTHER_GRANT', 'domain': 'Massachusetts Institute of Technology Subaward'}, {'id': 'SPEC-22-015', 'type': 'OTHER_GRANT', 'domain': 'Breast Cancer Research Foundation'}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'High Risk Participants--MIRAI', 'description': 'Patients who are deemed high risk on standard breast screening mammogram by the MIRAI model', 'interventionNames': ['Diagnostic Test: Breast MRI', 'Device: MIRAI']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'High Risk Participants--non-MIRAI', 'description': 'Patients who are deemed high risk by Tyrer-Cuzick model but not MIRAI', 'interventionNames': ['Diagnostic Test: Breast MRI']}], 'interventions': [{'name': 'Breast MRI', 'type': 'DIAGNOSTIC_TEST', 'description': 'Supplemental MRI (in addition to standard of care MRI).', 'armGroupLabels': ['High Risk Participants--MIRAI', 'High Risk Participants--non-MIRAI']}, {'name': 'MIRAI', 'type': 'DEVICE', 'description': 'Artificial intelligence software', 'armGroupLabels': ['High Risk Participants--MIRAI']}]}, 'contactsLocationsModule': {'locations': [{'zip': '01655', 'city': 'Worcester', 'state': 'Massachusetts', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Mohammed Shazeeb, PhD', 'role': 'CONTACT', 'email': 'mohammed.shazeeb@umassmed.edu', 'phone': '508-856-4255'}], 'facility': 'UMass Medical School', 'geoPoint': {'lat': 42.26259, 'lon': -71.80229}}], 'centralContacts': [{'name': 'Sara Schiller, MPH', 'role': 'CONTACT', 'email': 'sara.schiller1@umassmed.edu', 'phone': '7744417731'}], 'overallOfficials': [{'name': 'Mohammed Salman Shazeeb, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'UMass Chan Medical School'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Massachusetts, Worcester', 'class': 'OTHER'}, 'collaborators': [{'name': 'Massachusetts Institute of Technology', 'class': 'OTHER'}, {'name': 'Breast Cancer Research Foundation', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Director - Image Processing & Analysis Core; Director of Preclinical MRI & Co-Director of Scientific Affairs (Advanced MRI Center); UMass Chan Medical School', 'investigatorFullName': 'Mohammad Salman Shazeeb', 'investigatorAffiliation': 'University of Massachusetts, Worcester'}}}}