AI tool using scans and tissue slides to predict bladder cancer outcomes
Tests an AI tool using scans and tissue slides in people with bladder cancer.
Plain-language title generated from the registry record
Registry title: Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
Key facts
- Where
- Chongqing, ChinaSee the site
Where in China
- Department of Urology, The First Affiliated Hospital of Chongqing Medical UniversityChongqing, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Mingzhao Xiao
- Trial code
- NCT06389019
- Record updated
- 9 November 2026
- Original description
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.This observational study evaluates a deep learning system that combines CT images and digitized pathology slides to predict outcomes in patients with bladder cancer. It includes a retrospective cohort and a prospective cohort of newly enrolled patients. The prospective cohort will assess the system in patients enrolled during (2024-2026), with follow-up for survival and recurrence.Read full original descriptionShow less
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.This observational study evaluates a deep learning system that combines CT images and digitized pathology slides to predict outcomes in patients with bladder cancer. It includes a retrospective cohort and a prospective cohort of newly enrolled patients. The prospective cohort will assess the system in patients enrolled during (2024-2026), with follow-up for survival and recurrence.
Inclusion: 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: patients with a postoperative diagnosis of non-urothelial carcinoma; poor quality of CT images.