Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Overall survival
研究概览
简要总结
Upper Tract Urothelial Carcinoma (UTUC), characterized by its anatomical complexity and often aggressive clinical behavior, presents substantial difficulties in accurate diagnosis and reliable prognostication. The stratification of postoperative survival utilizing radiomics features derived from imaging and characteristics from whole slide images could prove instrumental in guiding therapeutic decisions to enhance patient outcomes. In this research, our objective is to construct a deep learning-based prognostic-stratification system designed for the automated prediction of overall and cancer-specific survival in individuals diagnosed with UTUC.
详细描述
Upper Tract Urothelial Carcinoma (UTUC) can be challenging to accurately diagnose and its course difficult to predict, as the disease manifestations and aggressiveness can differ significantly among individuals. This research seeks to create an innovative system employing artificial intelligence to process patient data, encompassing images from diagnostic scans and surgical pathology slides. This system would then be capable of automatically forecasting a patient's overall survival and their specific likelihood of surviving UTUC. Such insights could empower clinicians to tailor more effective treatment strategies for each individual patient.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients with Upper Tract Urothelial Carcinoma (UTUC) who had radical nephroureterectomy (RNU).
- •Contrast-enhanced CT scan (e.g., CT urography) less than two weeks before surgery.
- •Complete CT image data and clinical data.
- •Complete whole slide image data.
排除标准
- •Patients with a postoperative diagnosis of non-urothelial carcinoma.
- •Poor quality of CT images and/or whole slide image data.
- •Incomplete clinical and follow-up data.
结局指标
主要结局
Overall survival
时间窗: up to 10 years
the time from the date of surgery to death from any cause or the date of last contact (censored observation) at the date of data cut-off.
次要结局
- Recurrence free survival(up to 10 years)
研究者
Mingzhao Xiao
Professor
First Affiliated Hospital of Chongqing Medical University