Deep learning enabled prediction of 5-year survival in pediatric genitourinary rhabdomyosarcoma.

Journal: Surgical oncology
Published Date: Mar 1, 2021

Abstract

BACKGROUND: Genitourinary rhabdomyosarcoma (GU-RMS) is a rare, pediatric malignancy originating from embryonic mesenchyme. Current approaches to prognostication rely upon conventional statistical methods such as Cox proportional hazards (CPH) models and have suboptimal predictive ability. Given the success of deep learning approaches in other specialties, we sought to develop and compare deep learning models with CPH models for the prediction of 5-year survival in pediatric GU-RMS patients.

Authors

  • Hriday P Bhambhvani
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Alvaro Zamora
    Department of Physics, Stanford University, Stanford, CA.
  • Kyla Velaer
    Department of Urology, Stanford University Medical Center, Stanford, CA, USA.
  • Daniel R Greenberg
    Department of Urology, Stanford University Medical Center, Stanford, CA.
  • Kunj R Sheth
    Department of Urology, Stanford University Medical Center, Stanford, CA, USA. Electronic address: shethk@stanford.edu.

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