Not provided
Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| K2024-187-07 | Other Identifier | The First Affiliated Hospital of Chongqing Medical University |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-UTUC | Patients with Upper Tract Urothelial Carcinoma (UTUC) who underwent radical nephroureterectomy (RNU) |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Deep learning system for prognostication prediction in upper tract urothelial carcinoma | Other | develop and validate a deep learning system for prognostication prediction in upper tract urothelial carcinoma based on CT radiomics and whole slide images. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | up to 10 years |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | up to 10 years |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
We included patients who had surgery only or who had neoadjuvant chemotherapy before surgery. We excluded patients with a postoperative diagnosis of non-urothelial carcinoma.
Not provided
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Urology, The First Affiliated Hospital of Chongqing Medical University, chongqing, chongqing 400016 Recruiting | Chongqing | 400016 | China |
The datasets analyzed during the current study are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided