Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

Journal: Medical physics
Published Date: Jul 1, 2018

Abstract

PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.

Authors

  • Timo M Deist
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Frank J W M Dankers
    Department of Radiation Oncology, GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
  • Gilmer Valdes
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Robin Wijsman
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • I-Chow Hsu
    Department of Radiation Oncology, University of California at San Francisco, San Francisco, California.
  • Cary Oberije
    Kheiron Medical Technologies, London, UK.
  • Tim Lustberg
    Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
  • Johan van Soest
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Frank Hoebers
    Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
  • Arthur Jochems
    a Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology , Maastricht University Medical Centre , Maastricht , The Netherlands.
  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.
  • Leonard Wee
    Maastricht University Medical Centre, Netherlands.
  • Olivier Morin
    Department of Radiation Oncology, University of California, San Francisco, California.
  • David R Raleigh
    Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
  • Wouter Bots
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Johannes H Kaanders
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • José Belderbos
    Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
  • Margriet Kwint
    Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
  • Timothy Solberg
    Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
  • René Monshouwer
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Johan Bussink
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Andre Dekker
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Philippe Lambin
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.

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