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

NCT06389019 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2025-05-28

No results posted yet for this study

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.

Conditions

Interventions

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.

Sponsors & Collaborators

  • Mingzhao Xiao

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-01-01
Primary Completion
2025-06-01
Completion
2025-10-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06389019 on ClinicalTrials.gov