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

NCT06993779 | Active Not Recruiting

• 2 other identifiers: AI-UTUCK2024-187-07
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

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.

Conditions

UTUC

Outcome Measures

Primary Outcomes (1)

• 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

Secondary Outcomes (1)

• Recurrence free survival

  up to 10 years

Study Arms (1)

AI-UTUC

Patients with Upper Tract Urothelial Carcinoma (UTUC) who underwent radical nephroureterectomy (RNU)

Other: Deep learning system for prognostication prediction in upper tract urothelial carcinoma

Interventions

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.

AI-UTUC

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Mingzhao Xiao: lead

Study Sites (1)

Department of Urology, The First Affiliated Hospital of Chongqing Medical University, chongqing, chongqing 400016 Recruiting

Chongqing, 400016, China

Location

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: May 20, 2025
• First Posted: May 29, 2025
• Study Start: January 1, 2025
• Primary Completion: June 1, 2025
• Study Completion: November 1, 2025
• Last Updated: May 29, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)