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Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma
Sponsor: Mingzhao Xiao
Summary
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.
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
1000
Start Date
2025-01-01
Completion Date
2025-11-01
Last Updated
2025-05-29
Healthy Volunteers
No
Conditions
Interventions
Deep learning system for prognostication prediction in upper tract urothelial carcinoma
develop and validate a deep learning system for prognostication prediction in upper tract urothelial carcinoma based on CT radiomics and whole slide images.
Locations (1)
Department of Urology, The First Affiliated Hospital of Chongqing Medical University, chongqing, chongqing 400016 Recruiting
Chongqing, China