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NCT Number: NCT06389019

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

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.

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Key information

Conditions

Bladder Cancer Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Male Urogenital Diseases Neoplasms Neoplasms by Site Urinary Bladder Diseases Urinary Bladder Neoplasms Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Deep learning system for prognostication prediction in bladder cancer

Other

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

Primary outcomes

  1. Overall survival

    Time frame: 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 outcomes

  1. Recurrence free survival

    Time frame: 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

Study contacts

Contact information is provided by the study sponsor or research team.

Mingzhao Xiao, PHD

CONTACT

[email protected]

800-555-5555

QuanHao He

CONTACT

[email protected]

800-555-5555

Sponsors and collaborators

Lead sponsor

Mingzhao Xiao

Other

Registry information

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Apr 29, 2024
Registry last updated
May 28, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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