[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100608724":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":26,"centralContacts":31,"locations":26,"responsibleParty":37,"collaborators":39,"id":43,"slug":44,"hasResults":45,"nctId":46,"briefTitle":47,"officialTitle":48,"acronym":49,"eligibilityCriteria":50,"healthyVolunteers":45,"sex":51,"minAge":52,"maxAge":53,"enrollmentInfo":54,"targetDuration":26,"studyType":57,"phases":58,"briefSummary":60,"conditions":61,"keywords":65,"overallStatus":71,"whyStopped":26,"lastUpdateSubmitDate":72,"lastUpdatePostDateStruct":73,"startDateStruct":76,"completionDateStruct":78,"leadSponsor":80,"locationsCount":26},{"fullName":5,"class":6},"Second Affiliated Hospital, School of Medicine, Zhejiang University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Breast Cancer\u002FSuspected Cases","EXPERIMENTAL","Participants will undergo non-contrast multiparametric breast MRI, including T2-weighted imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping. Imaging data will be analyzed using radiomics and AI-based algorithms for breast cancer detection and diagnosis.",[13],"Diagnostic Test: Non-contrast multiparametric breast MRI with AI-based radiomics analysis",{"label":15,"type":16,"description":17,"interventionNames":18},"Standard Radiologist Reading","ACTIVE_COMPARATOR","Participants undergo standardized non-contrast multiparametric breast MRI (T2WI, DWI, ADC). Imaging data are interpreted by radiologists without AI assistance, representing the current standard of care",[19],"Diagnostic Test: Standard radiologist reading of non-contrast multiparametric breast MRI",[21,27],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":26},"DIAGNOSTIC_TEST","Non-contrast multiparametric breast MRI with AI-based radiomics analysis","Participants will receive standardized non-contrast multiparametric breast MRI scans (T2WI, DWI, ADC). Imaging features will be extracted and analyzed using artificial intelligence-based radiomics and deep learning algorithms to improve early detection and diagnosis of breast cancer.",[9],null,{"type":22,"name":28,"description":29,"armGroupLabels":30,"otherNames":26},"Standard radiologist reading of non-contrast multiparametric breast MRI","Imaging data interpreted by trained radiologists following routine clinical practice, without AI assistance.",[15],[32],{"name":33,"role":34,"phone":35,"phoneExt":26,"email":36},"Chao Ni, Doctor","CONTACT","+86 13989463951","drnichao@zju.edu.cn",{"type":38,"investigatorFullName":26,"investigatorTitle":26,"investigatorAffiliation":26,"oldNameTitle":26,"oldOrganization":26},"SPONSOR",[40],{"name":41,"class":42},"Alibaba DAMO Academy","UNKNOWN","100608724","ai-based-self-supervised-learning-model-using-non-contrast-breast-mri-for-early-screening-and-clinical-utility-evaluation-100608724",false,"NCT07205276","AI-Based Self-Supervised Learning Model Using Non-Contrast Breast MRI for Early Screening and Clinical Utility Evaluation","Construction of an Early Breast Cancer Screening Warning Model Based on Self-supervised Learning With Plain MRI Scans and Prospective Clinical Utility Evaluation","B-MRI-AI","* Inclusion Criteria:\n\n  1. Female, age 30-70 years\n  2. Completed breast MRI scan, including at least T2WI, DWI, and ADC sequences\n  3. Multimodal data acquired within the same time window (≤90 days)\n  4. A clear clinical outcome: pathologically confirmed or ≥12-24 months of negative follow-up\n  5. The time window between imaging examination and outcome determination was ≤90 days\n  6. Signed informed consent\n* Exclusion Criteria:\n\n  1. Absolute contraindications to MRI (pacemaker, cochlear implant, ocular metal foreign body, etc.)\n  2. Pregnant or lactating women\n  3. Recent history of breast surgery\u002Fradiotherapy (≤6 months) or imaging after neoadjuvant therapy\n  4. Substandard image quality (severe motion artifact, signal-to-noise ratio below threshold)\n  5. Incomplete clinical data or time window exceeded\n  6. Known breast cancer metastasis or recurrence","FEMALE","30 Years","70 Years",{"count":55,"type":56},30000,"ESTIMATED","INTERVENTIONAL",[59],"NA","Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility.\n\nThis investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification.\n\nThe ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.",[62,63,64],"Breast Cancer Detection","Early Detection of Cancer","AI (Artificial Intelligence)",[66,67,68,69,70],"Breast MRI","Non-contrast MRI","Radiomics","Deep Learning","Breast Cancer Screening","NOT_YET_RECRUITING","2025-09-25",{"date":74,"type":75},"2025-10-03","ACTUAL",{"date":77,"type":56},"2025-10-01",{"date":79,"type":56},"2027-12-01",{"name":5,"class":6}]