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Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma

NCT06993779 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2025-05-29

No results posted yet for this study

Summary

Upper Tract Urothelial Carcinoma (UTUC), characterized by its anatomical complexity and often aggressive clinical behavior, presents substantial difficulties in accurate diagnosis and reliable prognostication. The stratification of postoperative survival utilizing radiomics features derived from imaging and characteristics from whole slide images could prove instrumental in guiding therapeutic decisions to enhance patient outcomes. In this research, our objective is to construct a deep learning-based prognostic-stratification system designed for the automated prediction of overall and cancer-specific survival in individuals diagnosed with UTUC.

Conditions

  • UTUC

Interventions

OTHER

Deep learning system for prognostication prediction in upper tract urothelial carcinoma

develop and validate a deep learning system for prognostication prediction in upper tract urothelial carcinoma based on CT radiomics and whole slide images.

Sponsors & Collaborators

  • Mingzhao Xiao

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2025-06-01
Completion
2025-11-01

Countries

  • China

Study Locations

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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 NCT06993779 on ClinicalTrials.gov