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临床试验/NCT06546072
NCT06546072已完成不适用

Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University0 个研究点目标入组 1,199 人开始时间: 2011年3月23日最近更新:
适应症

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

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

申办方类型
Other
责任方
Principal Investigator
主要研究者

Yunfang Yu

attending physician

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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