A Multiphase CT-based Deep Learning Model for Predicting Malignancy in Bosniak II-III Cystic Renal Masses
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 223
- 试验地点
- 1
- 主要终点
- the area under the receiver operating characteristic curve
研究概览
简要总结
This retrospective study combine radiomics and deep learning models to predict malignancy in Bosniak II-III cystic renal masses, aiming for improving preoperative risk assessment and reducing unnecessary surgery for benign lesions and avoiding delayed treatment of malignant disease.
The main question it aims to answer is:
- How to specially predict malignancy in Bosniak II-III cystic renal masses？ The investigators retrospectively included patients diagnosed with Bosniak II-III cystic renal masses based on preoperative contrast-enhanced CT.
详细描述
Patients diagnosed with Bosniak II-III cystic renal masses based on preoperative contrast-enhanced CT were included. The exclusion criteria were solid portion > 25%, polycystic kidney disease, maximum diameter<1cm, Von Hippel-Lindau syndrome, without complete CT examination or histopathology-proven CRMs, poor image quality and Bosniak I and Bosniak IV masses. Deep learning models were developed to classify renal cystic lesions as benign or malignant using multiphase CT images. The performance measure were the area under the receiver operating characteristic curve, sensitivity, specificity, and balanced accuracy.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
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
- •diagnosed with cystic renal masses
排除标准
- •olid portion > 25%;
- •polycystic kidney disease;
- •maximum diameter<1cm;
- •Von Hippel-Lindau syndrome;
- •without complete CT examination or histopathology-proven CRMs;
- •poor image quality;
- •Bosniak I and Bosniak IV masses.
研究组 & 干预措施
Benign
Patients who were istopathologically diagnosed benign renal cysts
Malignant
Patients who were istopathologically diagnosed malignant renal cysts
结局指标
主要结局
the area under the receiver operating characteristic curve
时间窗: preoperatively
The area under the receiver operating characteristic curve measures the overall ability of a binary classifier to distinguish between positive and negative classes, with values ranging from 0.5 (random guessing) to 1.0 (perfect classification).
次要结局
- sensitivity(preoperatively)
- specificity(preoperatively)
- balanced accuracy(preoperatively)