Using prediction models to evaluate magnetic resonance image guided radiation therapy plans
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 plan...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | Physics and Imaging in Radiation Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631620300646 |
| _version_ | 1818393245029236736 |
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| author | M. Allan Thomas Joshua Olick-Gibson Yabo Fu Parag J. Parikh Olga Green Deshan Yang |
| author_facet | M. Allan Thomas Joshua Olick-Gibson Yabo Fu Parag J. Parikh Olga Green Deshan Yang |
| author_sort | M. Allan Thomas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T05:42:14Z |
| format | Article |
| id | doaj.art-f53f24788f4141dfb570fa60a070914c |
| institution | Directory Open Access Journal |
| issn | 2405-6316 |
| language | English |
| last_indexed | 2024-12-14T05:42:14Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj.art-f53f24788f4141dfb570fa60a070914c2022-12-21T23:15:00ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162020-10-011699102Using prediction models to evaluate magnetic resonance image guided radiation therapy plansM. Allan Thomas0Joshua Olick-Gibson1Yabo Fu2Parag J. Parikh3Olga Green4Deshan Yang5Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO 63108, United States; Department of Imaging Physics, UT MD Anderson Cancer Center, Houston, TX 77030, United StatesDepartment of Radiation Oncology, Washington University in St. Louis, St. Louis, MO 63108, United StatesDepartment of Radiation Oncology, Washington University in St. Louis, St. Louis, MO 63108, United States; Department of Radiation Oncology, Emory University, Atlanta, GA 30332, United StatesDepartment of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI 48202, United StatesDepartment of Radiation Oncology, Washington University in St. Louis, St. Louis, MO 63108, United StatesDepartment of Radiation Oncology, Washington University in St. Louis, St. Louis, MO 63108, United States; Corresponding author at: Department of Radiation Oncology, Washington University, School of Medicine, St. Louis, MO 63110, United States.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.http://www.sciencedirect.com/science/article/pii/S2405631620300646Neural networkAdaptive radiation therapyTreatment plan qualityMagnetic resonance image guidance |
| spellingShingle | M. Allan Thomas Joshua Olick-Gibson Yabo Fu Parag J. Parikh Olga Green Deshan Yang Using prediction models to evaluate magnetic resonance image guided radiation therapy plans Physics and Imaging in Radiation Oncology Neural network Adaptive radiation therapy Treatment plan quality Magnetic resonance image guidance |
| title | Using prediction models to evaluate magnetic resonance image guided radiation therapy plans |
| title_full | Using prediction models to evaluate magnetic resonance image guided radiation therapy plans |
| title_fullStr | Using prediction models to evaluate magnetic resonance image guided radiation therapy plans |
| title_full_unstemmed | Using prediction models to evaluate magnetic resonance image guided radiation therapy plans |
| title_short | Using prediction models to evaluate magnetic resonance image guided radiation therapy plans |
| title_sort | using prediction models to evaluate magnetic resonance image guided radiation therapy plans |
| topic | Neural network Adaptive radiation therapy Treatment plan quality Magnetic resonance image guidance |
| url | http://www.sciencedirect.com/science/article/pii/S2405631620300646 |
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