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.

Study Overview

• Status: Recruiting
• Conditions: Bladder Cancer
• Intervention / Treatment: Other: Deep learning system for prognostication prediction in bladder cancer

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.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: QuanHao He; Phone Number: 800-555-5555; Email: 2020120460@stu.cqmu.edu.cn
• Study Contact Backup: Name: Mingzhao Xiao, PHD; Phone Number: 800-555-5555; Email: 2023140134@stu.cqmu.edu.cn

Study Locations

• China
  • Chongqing
    • Chongqing, Chongqing, China, 400016
      • Recruiting
      • Department of Urology, The First Affiliated Hospital of Chongqing Medical University
      • Contact: QuanHao He, PHD; Phone Number: 800-555-5555; Email: 2020120460@stu.cqmu.edu.cn
      • Contact: Mingzhao Xiao, PHD; Phone Number: 023-89012557; Email: 2023140134@stu.cqmu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Mingzhao Xiao

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2024
• Primary Completion (Estimated): June 1, 2024
• Study Completion (Estimated): October 1, 2024

Study Registration Dates

• First Submitted: April 25, 2024
• First Submitted That Met QC Criteria: April 26, 2024
• First Posted (Actual): April 29, 2024

Study Record Updates

• Last Update Posted (Actual): April 29, 2024
• Last Update Submitted That Met QC Criteria: April 26, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: Tomography; deep learning; Radiomics; Histopathological tissue slides
• Additional Relevant MeSH Terms: Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Urologic Diseases; Urinary Bladder Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Urinary Bladder Neoplasms
• Other Study ID Numbers: BLCA_CMUFH; K2024-187-01 (Other Identifier: The First Affiliated Hospital of Chongqing Medical University)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: The datasets analyzed during the current study are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No