Multimodal Clinical-imaging-pathology-driven Artificial Intelligence Model for Predicting Postoperative Recurrence of Locally Advanced Gastric Cancer
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 93
- 试验地点
- 1
- 主要终点
- Prediction accuracy of postoperative recurrence in locally advanced gastric cancer
研究概览
简要总结
This study focuses on developing an advanced model that combines clinical information, imaging, and pathology data to predict the likelihood of cancer returning after surgery in patients with locally advanced gastric cancer. By using artificial intelligence (AI), this model analyzes various data sources to create a more accurate prediction of recurrence risk, which can help doctors, patients, and families better understand the chances of recurrence. This AI-driven approach allows healthcare providers to make more informed decisions about personalized follow-up care and potential additional treatments to improve patient outcomes.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients diagnosed with locally advanced gastric cancer (Stage II or III).
- •Patients who have undergone surgical resection for gastric cancer.
- •Patients with complete clinical, imaging, and pathology data available for analysis.
- •Age 18 years or older.
- •Patients who provide informed consent to participate in the study.
排除标准
- •Patients with distant metastasis (Stage IV) at the time of diagnosis.
- •Patients with incomplete or missing clinical, imaging, or pathology data.
- •Patients who have received prior treatment for gastric cancer other than surgical resection.
- •Patients with other concurrent malignancies.
- •Patients who are unable or unwilling to comply with the study follow-up requirements.
结局指标
主要结局
Prediction accuracy of postoperative recurrence in locally advanced gastric cancer
时间窗: 24 months postoperative follow-up
The primary outcome measure is the accuracy of the multimodal AI model in predicting the risk of postoperative recurrence in patients with locally advanced gastric cancer. This is assessed by comparing the model's predictions with actual recurrence events over a specified follow-up period, allowing evaluation of its effectiveness in identifying high-risk patients and guiding clinical decisions.
次要结局
未报告次要终点
研究者
Qun Zhao
Professor
Hebei Medical University