Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database.

Journal: The Lancet. Digital health
Published Date: Feb 3, 2021

Abstract

BACKGROUND: Accurate prognostication is crucial in treatment decisions made for men diagnosed with non-metastatic prostate cancer. Current models rely on prespecified variables, which limits their performance. We aimed to investigate a novel machine learning approach to develop an improved prognostic model for predicting 10-year prostate cancer-specific mortality and compare its performance with existing validated models.

Authors

  • Changhee Lee
    University of California, Los Angeles, CA, USA.
  • Alexander Light
    Division of Urology, Department of Surgery, University of Cambridge, UK.
  • Ahmed Alaa
    Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
  • David Thurtle
    Department of Surgery, Division of Urology, University of Cambridge, Cambridge, UK; Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Mihaela van der Schaar
    University of California, Los Angeles, CA, USA.
  • Vincent J Gnanapragasam
    Division of Urology, Department of Surgery, University of Cambridge, UK.

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