Actively Recruiting

All Genders
NCT06389019

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

Led by Mingzhao Xiao · Updated on 2025-05-28

1000

Participants Needed

1

Research Sites

91 weeks

Total Duration

On this page

AI-Summary

What this Trial Is About

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.

CONDITIONS

Official Title

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

Who Can Participate

All Genders

Eligibility Criteria

Eligible

You may qualify if you...

  • 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
Not Eligible

You will not qualify if you...

  • Patients with a postoperative diagnosis of non-urothelial carcinoma
  • Poor quality of CT images
  • Incomplete clinical and follow-up data

AI-Screening

AI-Powered Screening

Complete this quick 3-step screening to check your eligibility

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Trial Site Locations

Total: 1 location

1

Department of Urology, The First Affiliated Hospital of Chongqing Medical University

Chongqing, Chongqing Municipality, China, 400016

Actively Recruiting

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Research Team

Q

QuanHao He

CONTACT

M

Mingzhao Xiao, PHD

CONTACT

How is the study designed?

Study Type

OBSERVATIONAL

Masking

N/A

Allocation

N/A

Model

N/A

Primary Purpose

N/A

Number of Arms

1

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