Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer

Recruiting

• Conditions: Bladder Cancer
• Interventions: Other: Deep learning system for prognostication prediction in bladder cancer

• Registration Number: NCT06389019
• Lead Sponsor: Mingzhao Xiao

Brief Summary

Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis. Postoperative survival stratification based on radiomics feature and whole slide image feature may be useful for treatment decisions to improve prognosis. In this research, we aim to develop a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with BLCA.

Detailed Description

Bladder cancer can be difficult to diagnose and predict outcomes for, as the disease can vary greatly between patients. This research aims to develop a new system that uses artificial intelligence to analyze patient information, including images from surgery and scans. This system could then automatically predict a patient\'s overall survival and how likely they are to survive specifically from bladder cancer. This information could be used by doctors to make better treatment decisions for each patient.

Study Timeline

• Study Start: 2024-01-01
• Study First Submitted: 2024-04-25
• Study First Submitted QC: 2024-04-26
• Study First Posted: 2024-04-29
• Status Verified: 2025-05
• Last Update Submitted: 2025-05-27
• Last Update Posted: 2025-05-28
• Primary Completion: 2025-06-01
• Study Completion: 2025-10-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

• patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT)
• contrast-CT scan 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
• incomplete clinical and follow-up data

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
BLCA	Deep learning system for prognostication prediction in bladder cancer	patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT).

Primary Outcome Measures

Name	Time	Method
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 Outcome Measures

Name	Time	Method
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

Trial Locations

Locations (1)

Department of Urology, The First Affiliated Hospital of Chongqing Medical University; 🇨🇳 Chongqing, Chongqing, China