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

Recruitingn/aAges any - anyAllMingzhao Xiao
NCT06389019 on ClinicalTrials.gov ↗
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In plain words

What the trial is, and whether it could suit you

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.

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📋 Eligibility rules (as published)
Inclusion Criteria: * patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT) * contrast-CT scan less than two weeks before surgery * complete CT image data and clinical data * complete whole slide image data Exclusion Criteria: * patients with a postoperative diagnosis of non-urothelial carcinoma * poor quality of CT images * incomplete clinical and follow-up data
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What it tests

  • Deep learning system for prognostication prediction in bladder cancer
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Where

1 site
Department of Urology, The First Affiliated Hospital of Chongqing Medical University
Chongqing, China
🤝 Taking part is your choice, and you can leave at any time. Only the trial team can confirm you are eligible — always decide together with your own doctor.
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