Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy.

Journal: Journal of applied clinical medical physics
Published Date: Jun 7, 2022

Abstract

PURPOSE: Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural network (ANN) for predicting the delivered leaf positions for VMAT plans.

Authors

  • Sruthi Sivabhaskar
    Department of Radiation Oncology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
  • Ruiqi Li
    Department of Radiation Oncology, UT Health San Antonio, San Antonio, TX, USA.
  • Arkajyoti Roy
    Department of Management Science and Statistics, University of Texas at San Antonio, San Antonio, Texas, USA.
  • Neil Kirby
    Department of Radiological Sciences, UT Health San Antonio, San Antonio, TX, 78229, USA.
  • Mohamad Fakhreddine
    Department of Radiological Sciences, UT Health San Antonio, San Antonio, TX, 78229, USA.
  • Nikos Papanikolaou
    Department of Radiation Oncology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.

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