TY - JOUR
T1 - Using prediction models to evaluate magnetic resonance image guided radiation therapy plans
AU - Thomas, M. Allan
AU - Olick-Gibson, Joshua
AU - Fu, Yabo
AU - Parikh, Parag J.
AU - Green, Olga
AU - Yang, Deshan
N1 - Funding Information:
This research was partially supported by the Agency for Healthcare Research and Quality (AHRQ) grant number R01-HS022888, National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant R03-EB028427 and National Heart, Lung, and Blood Institute (NHLBI) grant R01-HL148210. This paper is part of a special issue that contains contributions originally submitted to the scientific meeting MR in RT, which was planned to take place 05/2020, organized by the German Research Center (DKFZ) in Heidelberg. We acknowledge funding by DKFZ for the publication costs of this special issue.
Funding Information:
This research was partially supported by the Agency for Healthcare Research and Quality (AHRQ) grant number R01-HS022888 , National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant R03-EB028427 and National Heart, Lung, and Blood Institute (NHLBI) grant R01-HL148210 .
Publisher Copyright:
© 2020 The Authors
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
UR - http://www.scopus.com/inward/record.url?scp=85094187330&partnerID=8YFLogxK
U2 - 10.1016/j.phro.2020.10.002
DO - 10.1016/j.phro.2020.10.002
M3 - Article
C2 - 33458351
AN - SCOPUS:85094187330
SN - 2405-6316
VL - 16
SP - 99
EP - 102
JO - Physics and Imaging in Radiation Oncology
JF - Physics and Imaging in Radiation Oncology
ER -