Can Vesical Imaging-Reporting And Data System and Apparent Diffusion Coefficient Values (the VI-RADS/ADC) Accurately Predict Non-Muscle Invasive Bladder Cancer
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 150
- 试验地点
- 2
- 主要终点
- Vesical Imaging-Reporting And Data System (VI-RADS) for discriminating muscle invasive bladder cancer (MIBC) from non-muscle invasive bladder cancer (NMIBC) using multi-parametric MRI.
研究概览
简要总结
Vesical Imaging-Reporting And Data System (VI-RADS) is proposed for predicting muscle invasive bladder cancer (MIBC) using multi-parametric MRI. However, No validation study on VI-RADS has been reported yet. Apparent diffusion coefficient (ADC) values on diffusion-weighted MRI are reportedly significantly lower in MIBC than those in non-MIBC(NMIBC).
详细描述
Vesical Imaging-Reporting And Data System (VI-RADS) is proposed for predicting muscle invasive bladder cancer (MIBC) using multi-parametric MRI. However, No validation study on VI-RADS has been reported yet. Apparent diffusion coefficient (ADC) values on diffusion-weighted MRI are reportedly significantly lower in MIBC than those in non-MIBC(NMIBC).
We aim to examine the accuracy of VI-RADS for predicting MIBC in final histopathology specimen. To assess whether incorporating ADC into VI-RADS improves the MIBC predictive accuracy of VI-RADS
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients diagnosed at the out-patient cystoscopy with papillary or small nodular bladder tumour
排除标准
- •fungating lesions
- •beyond the scope of enbloc resection
结局指标
主要结局
Vesical Imaging-Reporting And Data System (VI-RADS) for discriminating muscle invasive bladder cancer (MIBC) from non-muscle invasive bladder cancer (NMIBC) using multi-parametric MRI.
时间窗: 18 months
assessed by final histopathology specimen retrieved by enbloc transurethral resection of bladder tumors (ERBT)
次要结局
未报告次要终点
研究者
Mahmoud Laymon
Principal Investigator
Mansoura University