Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer

Last updated: May 27, 2025
Sponsor: First Affiliated Hospital of Chongqing Medical University
Overall Status: Active - Recruiting

Phase

N/A

Condition

Bladder Cancer

Urothelial Carcinoma

Urothelial Cancer

Treatment

develop and validate a deep learning radiomics model based on preoperative enhanced CT image

Clinical Study ID

NCT06092450
AI-BLCA
2022-K508
  • All Genders

Study Summary

Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy. Postoperative survival stratification based on radiomics and deep learning may be useful for treatment decisions to improve prognosis. This study was aimed to develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • patients with pathologically confirmed MIBC after radical cystectomy;

  • contrast-CT scan less than two weeks before surgery;

  • complete CT image data and clinical data.

Exclusion

Exclusion Criteria:

  • patients who received neoadjuvant therapy;

  • non-urothelial carcinoma;

  • poor quality of CT images;

  • incomplete clinical and follow-up data.

Study Design

Total Participants: 500
Treatment Group(s): 1
Primary Treatment: develop and validate a deep learning radiomics model based on preoperative enhanced CT image
Phase:
Study Start date:
August 01, 2023
Estimated Completion Date:
June 01, 2025

Connect with a study center

  • Department of Urology, The First Affiliated Hospital of Chongqing Medical University

    Chongqing, Chongqing 400016
    China

    Active - Recruiting

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