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Updated: Jan 19, 2026

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System
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Augmented Bladder Tumor Detection Using Deep Learning.

Eugene Shkolyar1, Xiao Jia2, Timothy C Chang1

  • 1Department of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.

European Urology
|September 21, 2019
PubMed
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This summary is machine-generated.

Deep learning-powered CystoNet significantly improves bladder cancer detection during cystoscopy. This artificial intelligence tool enhances tumor localization and resection efficacy, potentially reducing cancer recurrence rates.

Area of Science:

  • Urology
  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Standard white light cystoscopy misses up to 20% of bladder tumors, increasing cancer recurrence risk.
  • Improved tumor detection during transurethral resection of bladder tumor (TURBT) is crucial for effective bladder cancer treatment.

Purpose of the Study:

  • To develop and validate a deep learning algorithm, CystoNet, for augmented cystoscopic detection of bladder cancer.
  • To enhance tumor localization and surgical resection accuracy in bladder cancer patients.

Main Methods:

  • A convolutional neural network-based image analysis platform, CystoNet, was developed using a dataset of 95 patients for training and 5 for testing.
  • Prospective validation was performed on an additional 54 patients undergoing cystoscopy/TURBT.
Keywords:
Bladder cancerComputer-assisted image analysisCystoscopyDeep learningDiagnostic imaging

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  • White light videos were recorded, and frames with papillary urothelial carcinoma were annotated for algorithm training and testing.

    • Main Results:

    • CystoNet achieved high diagnostic performance in the validation dataset: per-frame sensitivity of 90.9% and specificity of 98.6%.
    • Per-tumor sensitivity was also 90.9%, with successful detection of 39 of 41 papillary and all 3 flat bladder cancers.
    • The algorithm demonstrated high accuracy in identifying various bladder cancer types.

    Conclusions:

    • CystoNet shows significant potential to improve the diagnostic yield of cystoscopy.
    • The deep learning augmentation may enhance the efficacy of transurethral resection of bladder tumor (TURBT).
    • Artificial intelligence-assisted cystoscopy offers a promising approach to improve bladder cancer detection and patient outcomes.