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

doi:10.1016/j.phro.2020.10.002

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 journal › Article › peer-review

2 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 language	English
Pages (from-to)	99-102
Number of pages	4
Journal	Physics and Imaging in Radiation Oncology
Volume	16
DOIs	https://doi.org/10.1016/j.phro.2020.10.002
State	Published - Oct 2020

Keywords

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

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title = "Using prediction models to evaluate magnetic resonance image guided radiation therapy plans",

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.",

keywords = "Adaptive radiation therapy, Magnetic resonance image guidance, Neural network, Treatment plan quality",

author = "Thomas, {M. Allan} and Joshua Olick-Gibson and Yabo Fu and Parikh, {Parag J.} and Olga Green and Deshan Yang",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors",

year = "2020",

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doi = "10.1016/j.phro.2020.10.002",

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AU - Thomas, M. Allan

AU - Olick-Gibson, Joshua

AU - Fu, Yabo

AU - Parikh, Parag J.

AU - Green, Olga

AU - Yang, Deshan

PY - 2020/10

Y1 - 2020/10

N2 - 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.

AB - 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.

KW - Adaptive radiation therapy

KW - Magnetic resonance image guidance

KW - Neural network

KW - Treatment plan quality

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JO - Physics and Imaging in Radiation Oncology

JF - Physics and Imaging in Radiation Oncology

ER -

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

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Keywords

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