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

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

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Phase N/A
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  • Simplified (AI rewritten)

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Overview

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

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 Criteria:

  • patients who received neoadjuvant therapy;
  • non-urothelial carcinoma;
  • poor quality of CT images;
  • incomplete clinical and follow-up data.

Study details
    Bladder Cancer

NCT06092450

First Affiliated Hospital of Chongqing Medical University

27 January 2024

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