Multimodal AI-based Therapy Response Prediction and Risk Stratification for Esophageal Cancer
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- Shu Peng
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- overall survival
研究概览
简要总结
This AI-driven model leverages multimodal data-such as radiomics, pathomics, genomics, and broader multi-omics profiles-to capture complementary aspects of tumor biology and predict treatment response and prognosis.
详细描述
Built upon retrospective cohorts for model development and rigorously validated in prospective cohorts, the proposed AI predictive model integrates multimodal data (radiomics, pathomics, genomics, and multi-omics)-each reflecting distinct dimensions of tumor heterogeneity-to enable joint prediction of treatment response and clinical outcomes.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Histopathologically diagnosed esophageal cancer
- •Complete baseline clinical data available (including demographic characteristics, ECOG performance score, TNM staging, etc.)
- •No other primary malignant tumors
- •Provision of informed consent
- •Availability of pre-treatment CT imaging
排除标准
- •Imaging data quality insufficient for analysis
- •Presence of another primary malignant tumor
- •Severe systemic disease
研究组 & 干预措施
Surgical resection cohort
neither neoajuvant therapy nor anti-tumor treatment prior to surgery
neoadjuvant therapy cohort
received neoadjuvant therapy and esophagectomy
conservative treatment
concervative treatment includes chemo/immuno/radiotherapy and targeted theray
Endoscopic submucosal dissection (ESD)
Endoscopic submucosal dissection (ESD)
结局指标
主要结局
overall survival
时间窗: From enrollment to the end of treatment at 3 years
overall survival rate in 3-years
次要结局
未报告次要终点