Deep learning system for predicting outcomes in bladder cancer
Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
This study is testing a new computer system that uses images of bladder cancer tissue and scans to see if it can better predict how long patients will live and how their cancer will behave after surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 1000 (estimated) |
| Sex | All |
| Sponsor | First Affiliated Hospital of Chongqing Medical University Academic / other |
| Locations | 1 site (Chongqing, Chongqing Municipality) |
| Trial ID | NCT06389019 on ClinicalTrials.gov |
What this trial studies
This research focuses on developing a deep learning-based system to predict overall and cancer-specific survival in patients with bladder cancer. By analyzing histopathological tissue slides and CT scans, the system aims to provide accurate prognostic stratification. The goal is to enhance treatment decision-making by offering insights into patient outcomes based on their unique cancer characteristics. The study will involve patients who have undergone surgical procedures for bladder cancer and have complete imaging and clinical data.
Who should consider this trial
Good fit: Ideal candidates for this study are patients with bladder cancer who have undergone surgery such as radical cystectomy or transurethral resection of bladder tumor.
Not a fit: Patients with non-urothelial carcinoma or those with incomplete imaging and clinical data may not benefit from this study.
Why it matters
Potential benefit: If successful, this system could significantly improve treatment decisions and patient outcomes for those diagnosed with bladder cancer.
How similar studies have performed: Other studies utilizing deep learning and radiomics in cancer prognosis have shown promising results, indicating potential for success in this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
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
Where this trial is running
Chongqing, Chongqing Municipality
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University — Chongqing, Chongqing Municipality, China (Recruiting)
Study contacts
- Study coordinator: QuanHao He
- Email: 2020120460@stu.cqmu.edu.cn
- Phone: 800-555-5555
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.