NCT07111364招募中不适用
Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound
适应症
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 400
- 试验地点
- 1
- 主要终点
- Overall Diagnostic Accuracy
研究概览
简要总结
This study aims to develop an ultrasound image-based deep learning system to enable automatic segmentation, T-staging, and pathological grading prediction of bladder tumors. It seeks to enhance the objectivity, accuracy, and efficiency of bladder cancer diagnosis, reduce reliance on physician experience, and provide support for precision medicine and resource optimization.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 85 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •① Suspected bladder mass detected by abdominal ultrasound (age ≥18 years);② Patients scheduled for surgical treatment of bladder tumors.
排除标准
- •Age >85 years;
- •Patients unable to undergo abdominal/transrectal ultrasound (e.g., uncooperative individuals, technically inadequate images);
- •History of bladder tumor surgery, radiotherapy, chemotherapy, or systemic therapy within 3 months; ④ Patients with indwelling medical devices (e.g., double-J ureteral stents, urinary catheters);
- •Failure to undergo bladder tumor surgery within 2 weeks post-ultrasound; ⑥ Non-urothelial carcinoma or pathologically unconfirmed diagnoses.
结局指标
主要结局
Overall Diagnostic Accuracy
时间窗: From may 2025 to may 2027
次要结局
未报告次要终点
研究者
研究点 (1)
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