A mathematical framework for virtual IMRT QA using machine learning.

Journal: Medical physics
Published Date: Jul 1, 2016

Abstract

PURPOSE: It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consuming and not altogether instructive due to the myriad sources that may produce a failing result. The purpose of this study was to develop an algorithm capable of predicting IMRT QA passing rates a priori.

Authors

  • G Valdes
    Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
  • R Scheuermann
    Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
  • C Y Hung
    Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
  • A Olszanski
    Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
  • M Bellerive
    Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
  • T D Solberg
    Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.

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