Multi-center and Multi-modal Deep Learning Study of Diagnosis, Therapeutic Outcome and Prognosis of Gastric Cancer
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 3,300
- 试验地点
- 6
- 主要终点
- Growth pattern
研究概览
简要总结
To assist postoperative pathological diagnosis and classification of gastric cancer by machine learning; To improve the accuracy of pathological diagnosis of gastric cancer by machine learning; To predict the effectiveness of treatment for gastric cancer by deep learning; To construct a model to predict the survival of gastric cancer patients by multimodal deep learning.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 79 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •The diagnosis of gastric cancer was confirmed by pathology;
- •Preoperative enhanced abdominal CT;
- •Available detailed clinical and pathological data;
- •Integrated follow-up data.
排除标准
- •The patients had severe underlying disease;
- •Overall survival was less than 3 months;
- •No detailed information could be collected.
结局指标
主要结局
Growth pattern
时间窗: 1 day
To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.
Nucleus shape
时间窗: 1 day
To obtain the nucleus shape of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Enhancement pattern
时间窗: 1 day
To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.
Maximum diameter of tumor
时间窗: 1 day
To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.
Enhancement degree
时间窗: 1 day
To assess the enhancement degree on preoperative enhanced abdominal CT of patients with gastric cancer, including hypoenhancement, isoenhancement and hyperenhancement.
Nucleus size
时间窗: 1 day
To obtain the nucleus size of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Distribution of pixel intensity
时间窗: 1 day
To obtain the distribution of pixel intensity of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Texture of nuclei
时间窗: 1 day
To obtain the texture of nuclei of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
次要结局
- Survival status(1 day)
- Recurrence/metastasis(1 day)
- Overall survival(1 day)
研究者
Kai Li
Deputy Director of surgical Oncology
First Hospital of China Medical University