Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study 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.

Eligibility

Primary outcome measure(s)

• Overall survival — 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.

Trial sites (1)

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