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

Bladder Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06389019
• Other study ID #: BLCA_CMUFH
• Secondary ID: K2024-187-01
• Status: Recruiting
• Start date: January 1, 2024
• Est. completion date: October 1, 2024

Study information

• Verified date: April 2024
• Source: First Affiliated Hospital of Chongqing Medical University
• Contact: QuanHao He
• Phone: 800-555-5555
• Email: 2020120460[@]stu.cqmu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: October 1, 2024
• Est. primary completion date: June 1, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Bladder Cancer
• Urinary Bladder Neoplasms

Intervention

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.

Locations

Country	Name	City	State
China	Department of Urology, The First Affiliated Hospital of Chongqing Medical University	Chongqing	Chongqing

Sponsors (1)

Lead Sponsor	Collaborator
Mingzhao Xiao

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Recurrence free survival	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	up to 10 years