[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"phyllodes-breast-tumor\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:phyllodes-breast-tumor":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,44],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":43},"100641646","molecular-subtype-guided-postoperative-radiotherapy-for-phyllodes-tumor-of-the-breast-a-randomized-controlled-trial-100641646",false,"NCT07657169","Molecular Subtype-Guided Postoperative Radiotherapy for Phyllodes Tumor of the Breast: A Randomized Controlled Trial","Efficacy and Safety of Molecular Subtype-Guided Postoperative Radiotherapy for Phyllodes Tumor of the Breast: A Prospective, Open-Label, Randomized Controlled Trial","Inclusion Criteria:\n\n1. Female patients aged ≥18 years and ≤75 years.\n2. Histologically confirmed breast phyllodes tumor (PT) by the central laboratory, with molecular classification as MN1 or MN2 subtype via transcriptome sequencing or IHC;\n3. Primary or ipsilateral local recurrence following R0 resection (negative margins) before enrollment;\n4. Pathologically confirmed borderline or malignant PT;\n5. No evidence of distant metastasis (M0);\n6. ECOG performance status 0-1;\n7. Signed informed consent before treatment;\n8. Expected randomization and study entry within 8-12 weeks (no later than 16 weeks) after surgery.\n\nExclusion Criteria:\n\n1. Previous radiation to the same-side breast or chest;\n2. women, or those of childbearing potential refusing effective contraception; Pregnancy, lactation, or refusal of contraception by fertile subjects;\n3. Grade III-IV bone marrow suppression: WBC≤1.9\\*109\u002FL，ANC≤0.9\\*109\u002FL，PLT≤49\\*109\u002FL，AST, ALT≥2\\*ULN;\n4. Significant diarrhea, severe active infection, uncontrolled systemic disease, interstitial lung disease, active connective tissue disease, or LVEF \\\u003C 50%;\n5. Significant diarrhea, severe active infection, uncontrolled systemic disease, interstitial lung disease, active connective tissue disease, or LVEF \\\u003C 50%;\n6. Prior or planned systemic anti-tumor therapy (chemotherapy, targeted therapy, immunotherapy, or investigational agents) during the study;\n7. Participation in other clinical trials that precludes study inclusion;\n8. Any other condition that, in the opinion of the investigator, renders the patient unsuitable for the trial.","FEMALE","18 Years","75 Years",{"count":20,"type":21},160,"ESTIMATED","INTERVENTIONAL",[24],"NA","Phyllodes tumor (PT) of the breast is a rare fibroepithelial neoplasm, and the role of postoperative radiotherapy (PORT) remains controversial. Our team has previously established a molecular subtyping system for PT, classifying patients into four subtypes. Among them, the malignant novel 1\u002F2 (MN1\u002FMN2) subtypes exhibit extremely high risk of local recurrence, and retrospective data suggest that PORT may significantly improve local control in these subtypes. This study aims to evaluate the efficacy and safety of molecular subtype-guided postoperative radiotherapy (PORT) in patients with MN-subtype phyllodes tumor of the breast. This prospective, multicenter, open-label, randomized controlled trial plans to enroll 160 patients with molecularly confirmed MN1 or MN2 subtype who have undergone R0 resection. Patients will be randomized in a 1:1 ratio to either the PORT group or the observation-only group, with stratification by negative margin width (\\\u003C1 cm vs. ≥1 cm) and molecular subtype (MN1 vs. MN2). The primary endpoint is 2-year local recurrence-free survival (LRFS). Secondary endpoints include distant metastasis-free survival (DMFS), disease-free survival (DFS), overall survival (OS), and the incidence of acute and late radiotherapy-related toxicities. By using an innovative molecular subtyping system to precisely select the target population, this study seeks to assess the benefit and safety of PORT in MN-subtype phyllodes tumors. The results are expected to provide the highest level of evidence for this specific subgroup, advance treatment strategies toward \"molecular subtype-guided precision radiotherapy,\" improve patient outcomes, and inform future clinical guidelines.",[27,28,29,30],"Radiotherapy, Adjuvant","Phyllodes Breast Tumor","Prognosis","Molecular Typing","NOT_YET_RECRUITING","2026-06-15",{"date":34,"type":35},"2026-06-18","ACTUAL",{"date":37,"type":21},"2026-06-01",{"date":39,"type":21},"2030-12-31",{"name":41,"class":42},"Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University","OTHER",4,{"id":45,"slug":46,"hasResults":11,"nctId":47,"briefTitle":48,"officialTitle":49,"acronym":4,"eligibilityCriteria":50,"healthyVolunteers":11,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":51,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":54,"conditions":55,"keywords":4,"overallStatus":60,"whyStopped":4,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":64,"completionDateStruct":66,"leadSponsor":68,"locationsCount":43},"100538087","ai-assisted-system-for-accurate-diagnosis-and-prognosis-of-breast-phyllodes-tumors-100538087","NCT06286267","AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors","Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors","Inclusion Criteria:\n\n* Patients diagnosed with a phyllodes tumor of the breast\n\nExclusion Criteria:\n\n* Blurred images, imaging artifacts",{"count":52,"type":21},4000,"OBSERVATIONAL","Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates.\n\nIn recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine.\n\nThe research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading.\n\nThe project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.",[28,56,57,58,59],"Artificial Intelligence","Multiomics","Prognostic Cancer Model","Diagnosis","RECRUITING","2024-02-22",{"date":63,"type":35},"2024-02-29",{"date":65,"type":35},"2023-03-01",{"date":67,"type":21},"2027-12-31",{"name":41,"class":42}]