NCT06389019

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: Mingzhao Xiao · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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.

Conditions

• Bladder Cancer

Interventions

Type	Name	Description
OTHER	Deep learning system for prognostication prediction in bladder cancer	develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.

Timeline

• Start date: 2024-01-01
• Primary completion: 2025-06-01
• Completion: 2025-10-01
• First posted: 2024-04-29
• Last updated: 2025-05-28

Locations

1 site across 1 country: China