[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Shantou Central Hospital\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":90},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,40,65],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":4,"eligibilityCriteria":14,"healthyVolunteers":11,"sex":15,"minAge":16,"maxAge":17,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":22,"briefSummary":24,"conditions":25,"keywords":4,"overallStatus":27,"whyStopped":4,"lastUpdateSubmitDate":28,"lastUpdatePostDateStruct":29,"startDateStruct":32,"completionDateStruct":34,"leadSponsor":36,"locationsCount":39},"100495214","phase-2-neoadjuvant-dose-dense-chemotherapy-for-hrher2--breast-cancer-patients-with-high-proliferation-index-100495214",false,"NCT05728268","Neoadjuvant Dose-dense Chemotherapy for HR+\u002FHER2- Breast Cancer Patients With High Proliferation Index","Inclusion Criteria:\n\n* Females with age of 18 to 70 years old.\n* Newly diagnosed breast cancer patients.\n* Planned neoadjuvant chemotherapy.\n* Hormone receptor status: Estrogen receptor (ER)-positive or Progesterone Receptor (PR)- positive.\n* HER2\u002Fneu-negative.\n* Ki67≥30%.\n* Clinical stage IIB-IIIC.\n* Informed consent form understood and signed.\n* Patient agrees to all follow-up visits.\n* Eastern Cooperative Oncology Group (ECOG) performance status 0 to 1.\n* Women of childbearing potential must have a negative serum pregnancy test.\n\nExclusion Criteria:\n\n* Metastatic disease\n* Pregnancy.\n* Nursing mothers.\n* Active or uncontrolled infection.\n* Presence of another malignancies.\n* Granulocyte count \\\u003C 1.5\\*10\\^9\u002FL.\n* Platelet count \\\u003C 100\\*10\\^9\u002FL.\n* Hemoglobin \\\u003C 90g\u002FL.\n* Serum Creatinine more than 1.5 upper limit.\n* AST and ALT more than 2.5 upper limit.\n* LVEF\\\u003C 50%.","FEMALE","18 Years","70 Years",{"count":19,"type":20},64,"ESTIMATED","INTERVENTIONAL",[23],"PHASE2","Studies have shown that dose-dense chemotherapy reduces the risk of recurrence and death of breast cancer patients comparing with standard-schedule chemotherapy. But some research data indicate that HR+\u002FHER2- breast cancer patients benefit from dose-dense chemotherapy while some have negative results. How to identify HR+\u002FHER2 breast cancer patients who are more likely to benefit from dose-dense chemotherapy is strongly necessary. Ki67 is a marker reflecting the proliferation of tumor cells. Breast cancer patients with high Ki67 are more likely to benefit from dose-dense chemotherapy. The purpose of this project is to investigate the efficacy and safety of neoadjuvant dose-dense chemotherapy for HR+\u002FHER2- breast cancer patients with high proliferation index. At the same time, we will explore biomarkers, such as MammaPrint and BluePrint, to further identify patients who benefit more from neoadjuvant dose-dense chemotherapy.",[26],"Breast Cancer","RECRUITING","2025-12-28",{"date":30,"type":31},"2026-01-02","ACTUAL",{"date":33,"type":31},"2021-08-01",{"date":35,"type":20},"2025-12-30",{"name":37,"class":38},"Shantou Central Hospital","OTHER",1,{"id":41,"slug":42,"hasResults":11,"nctId":43,"briefTitle":44,"officialTitle":45,"acronym":4,"eligibilityCriteria":46,"healthyVolunteers":11,"sex":15,"minAge":16,"maxAge":17,"enrollmentInfo":47,"targetDuration":4,"studyType":21,"phases":49,"briefSummary":51,"conditions":52,"keywords":53,"overallStatus":27,"whyStopped":4,"lastUpdateSubmitDate":57,"lastUpdatePostDateStruct":58,"startDateStruct":60,"completionDateStruct":62,"leadSponsor":64,"locationsCount":39},"100554126","airwave-compression-therapy-to-prevent-breast-cancer-related-lymphedema-100554126","NCT06495034","Airwave Compression Therapy to Prevent Breast Cancer-related Lymphedema","A Randomized Controlled Study on Early Intervention of Airwave Compression Therapy to Prevent Breast Cancer-related Upper Limb Lymphedema","Inclusion Criteria:\n\n* Volunteer to participate in this clinical trial. Fully understand and be informed about the study and sign an informed consent form (ICF). Willing to follow and able to complete all test procedures;\n* Female, ≥18 years old and ≤70 years old at the time of signing the ICF;\n* Patients with unilateral breast cancer diagnosed by histopathology or cytology;\n* No distant metastasis;\n* To undergo unilateral axillary sentinel lymph node biopsy or unilateral axillary lymph node dissection;\n* Have not received any treatment for arm lymphedema;\n* No lymphangitis and other infections.\n\nExclusion Criteria:\n\n* Breast malignancy derived from other tumors rather than the primary breast cancer;\n* Patients with second primary tumor;\n* Contraindication to airwave compression therapy: Acute venous thrombosis; Acute inflammatory skin disease; Erysipelas; Arrhythmia; Pulmonary edema; Unstable hypertension; People with artificial pacemakers; Deep thrombophlebitis;\n* Have a history of mental illness or other reasons can not cooperate with treatment;\n* Embolism, cardiogenic edema, hepatogenic edema and the history of upper limb trauma and other related complications;\n* Patients using any medication that affects fluid or electrolyte balance.",{"count":48,"type":20},132,[50],"NA","This study aims to explore the effectiveness of early intervention of airwave compression therapy to prevent the occurrence of postoperative breast cancer-related lymphedema(BCRL).",[26],[54,55,56],"breast cancer-related lymphedema","airwave compression therapy","quality of life","2024-07-09",{"date":59,"type":31},"2024-07-10",{"date":61,"type":31},"2023-07-01",{"date":63,"type":20},"2026-06-30",{"name":37,"class":38},{"id":66,"slug":67,"hasResults":11,"nctId":68,"briefTitle":69,"officialTitle":69,"acronym":4,"eligibilityCriteria":70,"healthyVolunteers":11,"sex":15,"minAge":16,"maxAge":4,"enrollmentInfo":71,"targetDuration":4,"studyType":73,"phases":4,"briefSummary":74,"conditions":75,"keywords":77,"overallStatus":27,"whyStopped":4,"lastUpdateSubmitDate":82,"lastUpdatePostDateStruct":83,"startDateStruct":85,"completionDateStruct":87,"leadSponsor":89,"locationsCount":39},"100516208","serum-and-tissue-metabolite-based-prediction-of-sentinel-lymph-node-metastasis-in-breast-cancer-100516208","NCT06001528","Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer","Inclusion Criteria:\n\n* Pathological diagnosis of breast cancer\n* No preoperative therapy including chemotherapy or endocrine therapy\n* No distant metastasis\n* Underwent mastectomy or breast-conserving surgery with sentinel lymph node biopsy\n* Agreed to provide preoperative peripheral blood samples\n* Had access to imaging, pathological and follow-up data for preoperative and postoperative evaluation of the disease\n\nExclusion Criteria:\n\n* Neoadjuvant therapy\n* Presence of distant metastasis at time of diagnosis\n* Primary malignancies other than breast cancer\n* Bilateral breast cancer or previous contralateral breast cancer\n* Undergo modified radical surgery for breast cancer without sentinel lymph node biopsy\n* Incomplete pathological data and follow-up data\n* Pregnancy and other conditions determined by the investigator to be ineligible for inclusion in the study",{"count":72,"type":20},2400,"OBSERVATIONAL","Breast cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Among patients who underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological results and in other words, these 70 % of the patients received unnecessary surgery. At present, imaging and pathological diagnosis is the main measure of lymph node metastasis in breast cancer. However, limitations remained. Artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible technique, which can make a more accuracy prediction through machine-based collection, learning and processing of previous information, especially in radiology and pathology-based diagnosis. With the intensification of the concept of precision medicine and the development of non-invasive technology, the investigators intend to use the artificial intelligence technology to develop a serum and tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with imaging and pathological information, providing specific, efficient and non-invasive biological indicators for the monitoring and early intervention of lymph node metastasis in patient with breast cancer. Therefore, the investigators retrospectively include serum samples from early breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort and a modeling cohort. Metabolites were detected and screened in the discovery cohort and then as the target metabolites for targeted detection in the modeling cohort. Combined with preoperative imaging and pathological information, a prediction model of breast cancer sentinel lymph node metastasis based on serum metabolites would be established. Subsequently, multi-center breast cancer patients will prospectively be included to verify the accuracy and stability of the model.",[26,76],"Lymph Node Metastasis",[78,79,80,81],"breast cancer","sentinel lymph node metastasis","metabolic reprogramming","artificial intelligence","2023-09-26",{"date":84,"type":31},"2023-09-28",{"date":86,"type":31},"2021-01-01",{"date":88,"type":20},"2026-08-31",{"name":37,"class":38},""]