[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100643796":3},{"organization":4,"armGroups":7,"interventions":41,"overallOfficials":10,"centralContacts":47,"locations":10,"responsibleParty":53,"collaborators":10,"id":57,"slug":58,"hasResults":59,"nctId":60,"briefTitle":61,"officialTitle":62,"acronym":10,"eligibilityCriteria":63,"healthyVolunteers":64,"sex":65,"minAge":66,"maxAge":10,"enrollmentInfo":67,"targetDuration":10,"studyType":70,"phases":10,"briefSummary":71,"conditions":72,"keywords":78,"overallStatus":83,"whyStopped":10,"lastUpdateSubmitDate":84,"lastUpdatePostDateStruct":85,"startDateStruct":88,"completionDateStruct":90,"leadSponsor":92,"locationsCount":10},{"fullName":5,"class":6},"Peking University People's Hospital","OTHER",[8,14,18,22,26,30,34,37],{"label":9,"type":10,"description":11,"interventionNames":12},"Training cohort",null,"Participants were retrospectively collected from Peking university people's hospital. All participants have completed the MRI examination and have available images for evaluation.",[13],"Diagnostic Test: Non-contrast breast MRI diagnostic model",{"label":15,"type":10,"description":16,"interventionNames":17},"External test cohort A","Participants were retrospectively collected from Center A. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing.",[13],{"label":19,"type":10,"description":20,"interventionNames":21},"External test cohort B","Participants were retrospectively collected from center B. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing.",[13],{"label":23,"type":10,"description":24,"interventionNames":25},"External test cohort C","Participants were retrospectively collected from center C. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing.",[13],{"label":27,"type":10,"description":28,"interventionNames":29},"External test cohort D","Participants were retrospectively collected from center D. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing.",[13],{"label":31,"type":10,"description":32,"interventionNames":33},"External test cohort E","Participants were retrospectively collected from center E. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing.",[13],{"label":35,"type":10,"description":36,"interventionNames":10},"External test cohort F","Participants were prospectively enrolled from Center F. All participants will undergo MRI examination and have available images for evaluation. All enrolled data will be used for the model testing.",{"label":38,"type":10,"description":39,"interventionNames":40},"External test cohort G","Participants were prospectively enrolled from Peking University People's Hospital. All participants will undergo MRI examination and have images available for evaluation. All enrolled data will be used for the model testing.",[13],[42],{"type":43,"name":44,"description":45,"armGroupLabels":46,"otherNames":10},"DIAGNOSTIC_TEST","Non-contrast breast MRI diagnostic model","An integrated AI model capable of generating synthetic contrast-enhanced images and distinguishing between benign and malignant lesions, as well as performing risk stratification",[15,19,23,27,31,38,9],[48],{"name":49,"role":50,"phone":51,"phoneExt":10,"email":52},"HAOQUAN CHEN, MD","CONTACT","+86 010-88325811","CHENHAOQUANSZ@163.COM",{"type":54,"investigatorFullName":55,"investigatorTitle":56,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Wang Yi","Professor","100643796","non-contrast-breast-mri-diagnosis-and-risk-stratification-using-dwi-generated-synthetic-contrast-enhancement-100643796",false,"NCT07598084","Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement","Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI: Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification","Inclusion Criteria:\n\n1. Complete breast MRI data;\n2. Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;\n3. Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;\n4. Original data that can be used to verify clinical status, including radiological and pathological reports;\n\nExclusion Criteria:\n\n1. Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;\n2. Poor image quality;\n3. Implants in the affected breast;",true,"ALL","18 Years",{"count":68,"type":69},12000,"ESTIMATED","OBSERVATIONAL","This study is conducted under the ethics-approved project titled \"Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification.\n\nParticipants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening.\n\nThis study seeks to answer two main questions:\n\n* Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions?\n* Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.",[73,74,75,76,77],"Breast Neoplasms","Artificial Intelligence (AI)","Magnetic Resonance Imaging (MRI)","Diffusion Magnetic Resonance Imaging","Deep Learning",[79,80,81,82],"Breast","Magnetic Resonance Imaging","Artificial Intelligence","Deep learning","NOT_YET_RECRUITING","2026-06-05",{"date":86,"type":87},"2026-06-09","ACTUAL",{"date":89,"type":69},"2026-06",{"date":91,"type":69},"2027-05",{"name":5,"class":6}]