CT and Endoscopic Biopsy Image-Based Deep Learning for Predicting Left Recurrent Laryngeal Nerve Lymph Node Metastasis in Esophageal 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
- 500
- 试验地点
- 1
- 主要终点
- AUROC (Area Under the Receiver Operating Characteristic Curve)
研究概览
简要总结
The goal of this observational study is to develop a predictive model for left recurrent laryngeal nerve (RLN) lymph node metastasis using deep learning algorithms. The model will be developed using clinical data from previous esophageal cancer surgeries, including preoperative CT imaging, and histopathological images from gastroscopic biopsies. The model will also be validated through prospective clinical trials to guide the intraoperative lymph node dissection, thereby reducing postoperative risks of RLN injury.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Preoperative gastroscopic biopsy confirmed esophageal squamous cell carcinoma；
- •The patient underwent esophagectomy with lymph nodes dissection along the left recurrent laryngeal nerve.
排除标准
- •The patient's medical records are incomplete；
- •The patient refused to participate in the trial.
结局指标
主要结局
AUROC (Area Under the Receiver Operating Characteristic Curve)
时间窗: From enrollment to the end of treatment at 4 weeks
The discriminant ability of the comprehensive evaluation model at different thresholds (positive vs. negative)
次要结局
未报告次要终点