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

NCT06389019 | Recruiting

• 2 other identifiers: BLCA_CMUFHK2024-187-01
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Bladder Cancer

Keywords

deep learning; Radiomics; Histopathological tissue slides; Tomography

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)

BLCA

patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT).

Other: Deep learning system for prognostication prediction in bladder cancer

Interventions

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.

BLCA

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 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

You may not qualify if:

• patients with a postoperative diagnosis of non-urothelial carcinoma
• poor quality of CT images
• 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 Municipality, 400016, China

RECRUITING

MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

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

Central Study Contacts

QuanHao He

CONTACT

800-555-5555; 2020120460@stu.cqmu.edu.cn

Mingzhao Xiao, PHD

CONTACT

800-555-5555; 2023140134@stu.cqmu.edu.cn

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: April 25, 2024
• First Posted: April 29, 2024
• Study Start: January 1, 2024
• Primary Completion: June 1, 2025
• Study Completion: October 1, 2025
• Last Updated: May 28, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)