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.
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.
Study Type
OBSERVATIONAL
Enrollment
1,000
develop and validate a deep learning system for prognostication prediction in upper tract urothelial carcinoma based on CT radiomics and whole slide images.
Department of Urology, The First Affiliated Hospital of Chongqing Medical University, chongqing, chongqing 400016 Recruiting
Chongqing, China
Overall survival
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.
Time frame: up to 10 years
Recurrence free survival
the time from the date of surgery to the date of first documented disease recurrence. Patients without recurrence at the time of analysis will be censored
Time frame: up to 10 years
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.