Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,500
- 试验地点
- 5
- 主要终点
- Disease free survival (DFS)
研究概览
简要总结
This bi-directional, multicentre study aims to assess multiparametric MRI Radiomics-based prediction model for identifying metastasis lymph nodes and prognostic prediction in breast cancer.
详细描述
Sensitivity for prediction of lymph node metastasis and survival of currently available prognostic scores in limited. This study proposes to establish a deep learning algorithms of multiparametric MRI radiomics and nomogram for identifying lymph node metastasis and prognostic prediction of breast cancer. The study will investigate the relationship between the radiomics and the tumor microenvironment. The study includes the construction of multiparametric MRI radiomics-based prediction model and the validation of the prediction model.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •The primary lesion was diagnosed as invasive breast cancer
- •Patients can have regional lymph node metastasis,but no distant organ metastasis
- •Complete the breast MRI examination before treatment
- •Accept breast cancer surgery or lymph node biopsy
- •Eastern Cooperative Oncology Group performance status 0-2
排除标准
- •Inflammatory breast cancer
- •Accompanied with other primary malignant tumors
- •Perform surgery,radiotherapy and lymph node biopsy before breast MRI examination
- •Patients who have neoadjuvant chemotherapy
- •Patients had distant and contralateral axillary lymph node metastasis
- •The pathologic diagnosis was extensive ductal carcinoma in situ
结局指标
主要结局
Disease free survival (DFS)
时间窗: 5 years
Disease free survival (DFS), which defined as the time from the diagnosis of breast cancer to the confirmed time of metastatic disease, or death due to any other cause.
次要结局
- Lymph node metastasis(Baseline)
- Overall survival (OS)(5 years)
- The correlation of radiomics features and tumor microenvironment(baseline (Completed MRI data before biopsy,surgery,neoadjuvant and radiotherapy.))
- Beast cancer specific motality (BCSM)(5 years)
- Recurrence free survival (RFS)(5 years)
研究者
Herui Yao
Principal Investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University