NCT06317610招募中不适用
Development and Validation of Growth Prediction Model for Ovarian Cancer Organoids Based on Bright Field Image and Deep Learning
Chongqing University Cancer Hospital1 个研究点 分布在 1 个国家目标入组 100 人开始时间: 2022年1月1日最近更新:
适应症
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Accuracy of growth prediction using deep learning model
研究概览
简要总结
The present study aims to collect early bright field image of patient-derived organoids with ovarian cancer. By leveraging artificial intelligence, this study will seek to construct and refine algorithms that able to predict growth of ovarian cancer organoids.
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
- Female
- 接受健康志愿者
- 否
入选标准
- •Patients must have histologically confirmed diagnosis of epithelial ovarian cancer
- •Patients received biopsy or puncture to obtain tumor tissues or malignant effusion
- •Patients voluntarily participated in the study and signed informed consent.
排除标准
- •Non-epithelial ovarian cancer
- •No sufficient amount of tumor tissues or malignant effusion for organoids establishment.
结局指标
主要结局
Accuracy of growth prediction using deep learning model
时间窗: up to 3 years
Accuracy=( the number of correctly classified samples)/( the number of total samples)
AUC of growth prediction performance using deep learning model
时间窗: up to 3 years
AUC =Area under receiver operating characteristic curve
次要结局
未报告次要终点
研究者
研究点 (1)
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