Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 1,199
- 主要终点
- DFS
研究概览
简要总结
Breast cancer poses a significant global health challenge, especially among women, with high rates of recurrence and distant spread despite early interventions. The timely identification of metastasis risk and accurate prediction of treatment strategies are critical for improving prognosis. However, the complex heterogeneity of breast tumors presents challenges in precise prognosis prediction. Therefore, the development of innovative methods for tumor segmentation and prognosis assessment is essential.
The research conducted is a multicenter study that enrolled 1,199 non-metastatic breast cancer patients from four independent centers. Our study leverages the advancements in artificial intelligence (AI) to address this challenge. This study is the first successful application of MRI-based multimodal prediction system to precisely identify the risk of postoperative recurrence in breast cancer patients.
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 —(Adult, Older Adult)
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Histologically confirmed stage I-III invasive BC
- •Age ≥ 18 years
- •The patient having undergone surgery
- •The existence of MRI scans
排除标准
- •Lacked pathological results
- •Had other, simultaneous malignancies
- •Had MR imaging issues were excluded
结局指标
主要结局
DFS
时间窗: The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.
Disease-free survival
次要结局
未报告次要终点
研究者
Yunfang Yu
attending physician
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
