UTUC
Conditions
Brief 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.
Detailed description
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.
Interventions
OTHERDeep 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.
Sponsors
Mingzhao Xiao
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Recurrence free survival | up to 10 years | 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 |
Countries
China
Outcome results
None listed