Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer.

Journal: Urologic oncology
Published Date: Jun 24, 2020

Abstract

PURPOSE: When exploring survival outcomes for patients with bladder cancer, most studies rely on conventional statistical methods such as proportional hazards models. Given the successful application of machine learning to handle big data in many disciplines outside of medicine, we sought to determine if machine learning could be used to improve our ability to predict survival in bladder cancer patients. We compare the performance of artificial neural networks (ANN), a type of machine learning algorithm, with that of multivariable Cox proportional hazards (CPH) models in the prediction of 5-year disease-specific survival (DSS) and overall survival (OS) in patients with bladder cancer.

Authors

  • Hriday P Bhambhvani
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Alvaro Zamora
    Department of Physics, Stanford University, Stanford, CA.
  • Eugene Shkolyar
    Department of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
  • Kris Prado
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Daniel R Greenberg
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Alex M Kasman
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Joseph Liao
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Sumit Shah
    Division of Medical Oncology, Stanford University Medical Center, Stanford, CA.
  • Sandy Srinivas
    Division of Medical Oncology, Stanford University Medical Center, Stanford, CA.
  • Eila C Skinner
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Jay B Shah
    Department of Urology, Stanford University, Stanford, CA.

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