[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100644613":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":21,"centralContacts":26,"locations":20,"responsibleParty":36,"collaborators":20,"id":38,"slug":39,"hasResults":40,"nctId":41,"briefTitle":9,"officialTitle":42,"acronym":20,"eligibilityCriteria":43,"healthyVolunteers":40,"sex":44,"minAge":45,"maxAge":46,"enrollmentInfo":47,"targetDuration":20,"studyType":50,"phases":51,"briefSummary":53,"conditions":54,"keywords":20,"overallStatus":56,"whyStopped":20,"lastUpdateSubmitDate":57,"lastUpdatePostDateStruct":58,"startDateStruct":61,"completionDateStruct":63,"leadSponsor":65,"locationsCount":20},{"fullName":5,"class":6},"Yunnan Cancer Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data","EXPERIMENTAL","To develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data.",[13],"Diagnostic Test: To explore the value of a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information in predicting pCR and long-term prognosis.",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":20},"DIAGNOSTIC_TEST","To explore the value of a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information in predicting pCR and long-term prognosis.","MRI and ultrasound were performed in addition to conventional treatment regimens",[9],null,[22],{"name":23,"affiliation":24,"role":25},"Lianhua Ye","Ethics Committee of Yunnan Provincial Cancer Hospital","STUDY_DIRECTOR",[27,32],{"name":28,"role":29,"phone":30,"phoneExt":20,"email":31},"Yu Xie","CONTACT","13708445492","xieyu@kmmu.edu.cn",{"name":33,"role":29,"phone":34,"phoneExt":20,"email":35},"Zhenhui LI","13698736132","lizhenhui@kmmu.edu.cn",{"type":37,"investigatorFullName":20,"investigatorTitle":20,"investigatorAffiliation":20,"oldNameTitle":20,"oldOrganization":20},"SPONSOR","100644613","prediction-of-neoadjuvant-therapy-efficacy-and-prognosis-for-breast-cancer-based-on-multimodal-data-100644613",false,"NCT07671690","Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data: A Multicenter Retrospective and Prospective Validation","Inclusion Criteria:\n\n1. Histopathologically confirmed invasive breast cancer;\n2. Planned to receive a full course of neoadjuvant therapy;\n3. Complete baseline imaging data (MRI\u002Fultrasound\u002Fmammography) and core needle pathology results available.\n\nExclusion Criteria:\n\n1. Previous history of ipsilateral breast cancer or chest radiotherapy;\n2. Distant metastasis (Stage IV);\n3. Poor image quality or missing clinical data exceeding 20%.","FEMALE","18 Years","80 Years",{"count":48,"type":49},1800,"ESTIMATED","INTERVENTIONAL",[52],"NA","This study aims to develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data. To externally validate the model in an independent prospective cohort, and evaluate its accuracy in predicting pathological complete response (pCR), 3-year and 5-year disease-free survival (DFS). To establish visual tools such as nomograms, assisting clinicians in identifying patients with chemoresistance and facilitating individualized de-escalation or escalation treatment strategies.",[55],"Breast Carcinoma","NOT_YET_RECRUITING","2026-06-22",{"date":59,"type":60},"2026-06-26","ACTUAL",{"date":62,"type":49},"2026-06-01",{"date":64,"type":49},"2029-06-30",{"name":5,"class":6}]