[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100645532":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":24,"locations":29,"responsibleParty":93,"collaborators":10,"id":95,"slug":96,"hasResults":97,"nctId":98,"briefTitle":99,"officialTitle":100,"acronym":10,"eligibilityCriteria":101,"healthyVolunteers":97,"sex":102,"minAge":103,"maxAge":10,"enrollmentInfo":104,"targetDuration":10,"studyType":107,"phases":10,"briefSummary":108,"conditions":109,"keywords":10,"overallStatus":31,"whyStopped":10,"lastUpdateSubmitDate":112,"lastUpdatePostDateStruct":113,"startDateStruct":116,"completionDateStruct":118,"leadSponsor":120,"locationsCount":121},{"fullName":5,"class":6},"Fujian Cancer Hospital","OTHER_GOV",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"HR+\u002FHER2- Breast Cancer Cohort Receiving Neoadjuvant Chemotherapy",null,"Multicenter prospective observational cohort of patients with HR+\u002FHER2- invasive breast cancer who receive routine standard neoadjuvant chemotherapy. All participants undergo pre-treatment DCE-MRI scanning, and an MRI-based AI model is applied to stratify patients into high and low chemotherapy benefit subgroups.",[13],"Diagnostic Test: Pre-treatment DCE-MRI-based AI model",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"DIAGNOSTIC_TEST","Pre-treatment DCE-MRI-based AI model","Preoperative dynamic contrast-enhanced MRI images are input into an artificial intelligence prediction model to stratify HR+\u002FHER2- breast cancer patients into high and low neoadjuvant chemotherapy benefit subgroups.",[9],[21],{"name":22,"affiliation":5,"role":23},"Chuangui Song, doctor","PRINCIPAL_INVESTIGATOR",[25],{"name":22,"role":26,"phone":27,"phoneExt":10,"email":28},"CONTACT","13960709993","songcg1971@outlook.com",[30,44,54,67,80],{"facility":5,"status":31,"city":32,"state":33,"zip":10,"country":34,"countryCode":35,"cosmosGeoPoint":36,"geoPoint":41,"contacts":42},"RECRUITING","Fuzhou","Fujian","China","CN",{"type":37,"coordinates":38},"Point",[39,40],119.30611,26.06139,{"lat":40,"lon":39},[43],{"name":22,"role":26,"phone":27,"phoneExt":10,"email":28},{"facility":45,"status":31,"city":32,"state":33,"zip":10,"country":34,"countryCode":35,"cosmosGeoPoint":46,"geoPoint":48,"contacts":49},"Fujian Provincial Hospital",{"type":37,"coordinates":47},[39,40],{"lat":40,"lon":39},[50],{"name":51,"role":26,"phone":52,"phoneExt":10,"email":53},"ruijuan wang, doctor","13799367490","Rjwang2025@126.com",{"facility":55,"status":31,"city":56,"state":33,"zip":10,"country":34,"countryCode":35,"cosmosGeoPoint":57,"geoPoint":61,"contacts":62},"The Second Affiliated Hospital of Fujian Medical University","Quanzhou",{"type":37,"coordinates":58},[59,60],118.58583,24.91389,{"lat":60,"lon":59},[63],{"name":64,"role":26,"phone":65,"phoneExt":10,"email":66},"kaiyan Huang, doctor","15905059388","kaiyanhuang@fjmu.edu.cn",{"facility":68,"status":31,"city":69,"state":69,"zip":10,"country":34,"countryCode":35,"cosmosGeoPoint":70,"geoPoint":74,"contacts":75},"Ningde First Hospital","Ningde",{"type":37,"coordinates":71},[72,73],119.52278,26.66167,{"lat":73,"lon":72},[76],{"name":77,"role":26,"phone":78,"phoneExt":10,"email":79},"zirong jiang, doctor","15892129077","zirongjiang@outlook.com",{"facility":81,"status":31,"city":82,"state":82,"zip":10,"country":34,"countryCode":35,"cosmosGeoPoint":83,"geoPoint":87,"contacts":88},"Sanming Second Hospital","Sanming",{"type":37,"coordinates":84},[85,86],117.61861,26.24861,{"lat":86,"lon":85},[89],{"name":90,"role":26,"phone":91,"phoneExt":10,"email":92},"junxiao wang, doctor","15159110696","25985991@qq.com",{"type":94,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100645532","pre-treatment-dce-mri-ai-models-predict-neoadjuvant-chemotherapy-response-in-hrher2--breast-cancer-100645532",false,"NCT07702708","Pre-Treatment DCE-MRI AI Models Predict Neoadjuvant Chemotherapy Response in HR+\u002FHER2- Breast Cancer","A Multicenter Prospective Observational Cohort Study: Predicting Neoadjuvant Chemotherapy Response Using Pre-Treatment DCE-MRI-Based AI Models in HR+\u002FHER2- Breast Cancer","Inclusion Criteria:\n\n1. Female patients aged ≥ 18 years old.\n2. Histopathologically confirmed invasive breast carcinoma.\n3. Hormone receptor positive (ER and\u002For PR ≥1%), HER2-negative status (IHC 0-1+, or IHC 2+ with negative FISH result).\n4. Clinical stage II-III breast cancer per the 8th AJCC staging system, with clinical indication for neoadjuvant chemotherapy or primary surgery.\n5. Standard pre-treatment breast DCE-MRI performed before neoadjuvant chemotherapy, with image quality eligible for AI model analysis.\n6. ECOG performance status 0 or 1; adequate function of major vital organs to tolerate planned clinical treatment.\n7. Voluntary participation with written informed consent obtained.\n\nExclusion Criteria:\n\n1. Prior systemic anti-tumor therapy for breast cancer other than planned neoadjuvant chemotherapy.\n2. Inflammatory breast cancer or distant metastatic disease (M1).\n3. Concurrent active malignant tumors of other origins.\n4. Contraindications to MRI examination or unqualified MRI images that cannot support model analysis.\n5. Severe comorbidities incompatible with neoadjuvant chemotherapy or surgical resection.\n6. Any other conditions judged ineligible for enrollment by the investigator.","FEMALE","18 Years",{"count":105,"type":106},100,"ESTIMATED","OBSERVATIONAL","This study is a multicenter, prospective, observational cohort study to evaluate the predictive performance of pre-treatment DCE-MRI-based artificial intelligence (AI) models for neoadjuvant chemotherapy benefit in HR+\u002FHER2- breast cancer. The study plans to enroll eligible HR+\u002FHER2- breast cancer patients receiving routine standard neoadjuvant chemotherapy and stratify participants into high-benefit and low-benefit subgroups via the established AI model based on baseline breast DCE-MRI images.\n\nAll enrolled patients will undergo systematic collection of baseline clinical-pathological data, pre-treatment DCE-MRI scans, neoadjuvant chemotherapy regimens, postoperative residual cancer burden (RCB) classification, objective response rate (ORR), and long-term survival endpoints including disease-free survival (DFS) and overall survival (OS). The primary objective compares the rate of RCB 0-1 between AI-defined high-benefit patients and published historical control data; secondary analyses compare ORR, RCB 0-1 proportion, DFS and OS between AI-stratified high-benefit and low-benefit subgroups to comprehensively verify the clinical value of this imaging AI model for individualized neoadjuvant chemotherapy selection.",[110,111],"HR+\u002FHER2- Breast Cancer","Breast Neoplasms","2026-07-13",{"date":114,"type":115},"2026-07-14","ACTUAL",{"date":117,"type":115},"2026-06-01",{"date":119,"type":106},"2027-06-30",{"name":5,"class":6},5]