Status: Recruiting

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.

Participants needed: 1,000

Trial details

Biological sex: AllType: ObservationalSponsor: Mingzhao XiaoUpdated: May 28, 2025Locations: 1

Eligibility criteria

patients with bladder cancer who had surgery like radical cystectomy or transure... [+3]

patients with a postoperative diagnosis of non-urothelial carcinoma [+2]

Status: Recruiting

Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)

This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.

Participants needed: 800

Trial details

Biological sex: AllType: ObservationalSponsor: Mingzhao XiaoUpdated: May 31, 2025Locations: 1

Eligibility criteria

underwent partial/radical nephrectomies [+2]

with incomplete clinic-pathological data [+3]

Clinical trials

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

Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)