Artificial Intelligence Diagnosis of Different Histopathological Growth Patterns of Colorectal Cancer Liver Metastasis
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 437
- 试验地点
- 1
- 主要终点
- The accuracy rate of the predictive model for HGP classification
研究概览
简要总结
This study selected cases of colorectal cancer liver metastasis patients who underwent liver metastasis tumor resection, retrieved the pathological HE sections of the metastatic lesions, and constructed a predictive model. AI software was applied to delineate different types of regions, achieving full automation of HGP prediction and constructing a predictive model. Statistical analysis was conducted on the classification of histopathological growth patterns (HGP) of liver metastasis and the survival prognosis of patients, and the differences in prognosis among different HGP classification methods were compared. This provides a new method for judging prognosis and treatment for clinical treatment of colorectal cancer liver metastasis patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients with colorectal cancer liver metastases who underwent resection of liver metastases;
- •Confirmed by a pathologist as having liver metastases from colorectal cancer;
排除标准
- •Cases of colorectal cancer liver metastasis that cannot be classified by histopathology.
结局指标
主要结局
The accuracy rate of the predictive model for HGP classification
时间窗: Half a year
We will build an AI prediction model for HGP prediction and verify the accuracy of the AI-assisted prediction model in classifying HGP.
次要结局
- The time for the predictive model to perform HGP classification(Half a year)
- Progression-free survival of patients with different HGP classifications(Through study completion, an average of 1 year)
研究者
Yanhong Deng
Principal Investigator
Sun Yat-sen University