Using prediction models to evaluate magnetic resonance image guided radiation therapy plans

M. Allan Thomas, Joshua Olick-Gibson, Yabo Fu, Parag J. Parikh, Olga Green, Deshan Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Comprehensive analysis of daily, online adaptive plan quality and safety in magnetic resonance imaging (MRI) guided radiation therapy is critical to its widespread use. Artificial neural network models developed with offline plans created after simulation were used to analyze and compare online plans that were adapted and reoptimized in real time prior to treatment. Roughly one third of 60Co adapted plans were of inferior quality relative to fully optimized, offline plans, but MRI-linac adapted plans were essentially equivalent to offline plans. The models also enabled clear justification that MRI-linac plans are superior to 60Co in an overwhelming majority of cases.

Original languageEnglish
Pages (from-to)99-102
Number of pages4
JournalPhysics and Imaging in Radiation Oncology
Volume16
DOIs
StatePublished - Oct 2020

Keywords

  • Adaptive radiation therapy
  • Magnetic resonance image guidance
  • Neural network
  • Treatment plan quality

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