Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer.

Journal: The Prostate
Published Date: Oct 13, 2024

Abstract

BACKGROUND: Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use readily available clinicopathologic variables.

Authors

  • Hemal Semwal
    Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA.
  • Colton Ladbury
    Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA.
  • Ali Sabbagh
    Department of Radiation Oncology, University of California-San Francisco, San Francisco, CA, USA.
  • Osama Mohamad
    Department of Radiation Oncology, University of California, San Francisco, San Francisco.
  • Derya Tilki
    Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany. Electronic address: d.tilki@uke.de.
  • Arya Amini
    Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA.
  • Jeffrey Wong
    Computer Science Department, University of California, Los Angeles, CA 90095, USA.
  • Yun Rose Li
    Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California, USA.
  • Scott Glaser
    Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA. Electronic address: sglaser@coh.org.
  • Bertram Yuh
    City of Hope National Medical Center, Duarte, California.
  • Savita Dandapani
    Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California, USA.

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