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Journal articles on the topic 'Adaptive radiotherapy implementation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Moeller, D., U. V. Elstrøm, M. S. Assenholt, and L. Hoffmann. "SP-0214: Clinical implementation of adaptive radiotherapy." Radiotherapy and Oncology 127 (April 2018): S116. http://dx.doi.org/10.1016/s0167-8140(18)30524-3.

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2

Hamming-Vrieze, O. "SP-038: Clinical implementation of adaptive radiotherapy: challenges ahead." Radiotherapy and Oncology 122 (March 2017): 21. http://dx.doi.org/10.1016/s0167-8140(17)30306-7.

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3

Brock, K. K., A. N. Ohrt, S. Gryshkevych, M. M. McCulloch, G. Cazoulat, A. S. Mohamed, R. He, et al. "Clinical Implementation of Daily Dose Accumulation and Adaptive Radiotherapy." International Journal of Radiation Oncology*Biology*Physics 108, no. 3 (November 2020): e371-e372. http://dx.doi.org/10.1016/j.ijrobp.2020.07.2381.

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4

Avgousti, R., C. Antypas, C. Armpilia, F. Simopoulou, P. Karaiskos, V. Kouloulias, and A. Zygogianni. "ADAPTIVE RADIOTHERAPY: SUGGESTED ANATOMIC AND DOSIMETRIC THRESHOLDS BEFORE IMPLEMENTATION." Physica Medica 104 (December 2022): S30. http://dx.doi.org/10.1016/s1120-1797(22)03098-8.

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5

Yen, Allen, Chenyang Shen, and Kevin Albuquerque. "The New Kid on the Block: Online Adaptive Radiotherapy in the Treatment of Gynecologic Cancers." Current Oncology 30, no. 1 (January 8, 2023): 865–74. http://dx.doi.org/10.3390/curroncol30010066.

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Online adaptive radiation is a new and exciting modality of treatment for gynecologic cancers. Traditional radiation treatments deliver the same radiation plan to cancers with large margins. Improvements in imaging, technology, and artificial intelligence have made it possible to account for changes between treatments and improve the delivery of radiation. These advances can potentially lead to significant benefits in tumor coverage and normal tissue sparing. Gynecologic cancers can uniquely benefit from this technology due to the significant changes in bladder, bowel, and rectum between treatments as well as the changes in tumors commonly seen between treatments. Preliminary studies have shown that online adaptive radiation can maintain coverage of the tumor while sparing nearby organs. Given these potential benefits, numerous clinical trials are ongoing to investigate the clinical benefits of online adaptive radiotherapy. Despite the benefits, implementation of online adaptive radiotherapy requires significant clinical resources. Additionally, the timing and workflow for online adaptive radiotherapy is being optimized. In this review, we discuss the history and evolution of radiation techniques, the logistics and implementation of online adaptive radiation, and the potential benefits of online adaptive radiotherapy for gynecologic cancers.

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LEE, Victor Shing-Cheung, Giuseppe SchettIno, and Andrew Nisbet. "UK adaptive radiotherapy practices for head and neck cancer patients." BJR|Open 2, no. 1 (November 2020): 20200051. http://dx.doi.org/10.1259/bjro.20200051.

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Objective: To provide evidence on the extent and manner in which adaptive practices have been employed in the UK and identify the main barriers for the clinical implementation of adaptive radiotherapy (ART) in head and neck (HN) cancer cases. Methods: In December 2019, a Supplementary Material 1, of 23 questions, was sent to all UK radiotherapy centres (67). This covered general information to current ART practices and perceived barriers to implementation. Results: 31 centres responded (46%). 56% responding centres employed ART for between 10 and 20 patients/annum. 96% of respondents were using CBCT either alone or with other modalities for assessing “weight loss” and “shell gap,” which were the main reasons for ART. Adaptation usually occurs at week three or four during the radiotherapy treatment. 25 responding centres used an online image-guided radiotherapy (IGRT) approach and 20 used an offline ad hoc ART approach, either with or without protocol level. Nearly 70% of respondents required 2 to 3 days to create an adaptive plan and 95% used 3–5 mm adaptive planning target volume margins. All centres performed pre-treatment QA. “Limited staff resources” and “lack of clinical relevance” were identified as the two main barriers for ART implementation. Conclusion: There is no consensus in adaptive practice for HN cancer patients across the UK. For those centres not employing ART, similar clinical implementation barriers were identified. Advances in knowledge: An insight into contemporary UK practices of ART for HN cancer patients indicating national guidance for ART implementation for HN cancer patients may be required

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7

Noel, Camille E., Lakshmi Santanam, Parag J. Parikh, and Sasa Mutic. "Process-based quality management for clinical implementation of adaptive radiotherapy." Medical Physics 41, no. 8Part1 (July 30, 2014): 081717. http://dx.doi.org/10.1118/1.4890589.

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8

Khoo, V. "195 speaker VOLUMETRIC IMAGING: ADAPTIVE RADIOTHERAPY STRATEGIES FOR CLINICAL IMPLEMENTATION." Radiotherapy and Oncology 99 (May 2011): S77. http://dx.doi.org/10.1016/s0167-8140(11)70317-6.

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9

Cai, Bin, Olga L. Green, Rojano Kashani, Vivian L. Rodriguez, Sasa Mutic, and Deshan Yang. "A practical implementation of physics quality assurance for photon adaptive radiotherapy." Zeitschrift für Medizinische Physik 28, no. 3 (August 2018): 211–23. http://dx.doi.org/10.1016/j.zemedi.2018.02.002.

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10

Hoffmann, L., M. Knap, A. A. Khalil, M. H. Andersen, A. B. Rasmussen, M. K. Joergensen, and D. S. Moeller. "OC-0575: Large scale implementation of adaptive radiotherapy for lung cancer patients." Radiotherapy and Oncology 111 (2014): S225. http://dx.doi.org/10.1016/s0167-8140(15)30681-2.

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11

Mutic, Sasa. "MO-E-BRC-00: Online Adaptive Radiotherapy - Considerations for Practical Clinical Implementation." Medical Physics 43, no. 6Part30 (June 2016): 3707. http://dx.doi.org/10.1118/1.4957268.

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12

Brock, K., M. McCulloch, G. Cazoulat, A. Ohrt, P. Balter, H. Bahig, S. Ping, et al. "EP-2021 Commissioning and clinical implementation of dose accumulation and adaptive radiotherapy." Radiotherapy and Oncology 133 (April 2019): S1108. http://dx.doi.org/10.1016/s0167-8140(19)32441-7.

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13

Pozo, Gustavo, Maria Angeles Pérez-Escutia, Ana Ruíz, Alejandro Ferrando, Ana Milanés, Eduardo Cabello, Raul Díaz, Alejandro Prado, and Jose Fermin Pérez-Regadera. "Management of interruptions in radiotherapy treatments: Adaptive implementation in high workload sites." Reports of Practical Oncology & Radiotherapy 24, no. 2 (March 2019): 239–44. http://dx.doi.org/10.1016/j.rpor.2019.02.003.

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14

Nelissen, K. J., E. Versteijne, S. Senan, B. J. Slotman, and W. F. A. R. Verbakel. "Clinical Implementation of Single Visit Palliative Adaptive Radiotherapy without Prior CT Simulation." International Journal of Radiation Oncology*Biology*Physics 114, no. 3 (November 2022): e594-e595. http://dx.doi.org/10.1016/j.ijrobp.2022.07.2283.

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15

Peng, C., E. Ahunbay, S. Holmes, D. Wang, C. Lawton, and X. Li. "Clinical Implementation and Initial Experience of Online Adaptive Radiotherapy for Prostate Cancer." International Journal of Radiation Oncology*Biology*Physics 78, no. 3 (November 2010): S746—S747. http://dx.doi.org/10.1016/j.ijrobp.2010.07.1729.

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16

Surucu, Murat, Karan K. Shah, John C. Roeske, Mehee Choi, William Small, and Bahman Emami. "Adaptive Radiotherapy for Head and Neck Cancer." Technology in Cancer Research & Treatment 16, no. 2 (August 19, 2016): 218–23. http://dx.doi.org/10.1177/1533034616662165.

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Objective: To investigate the effects of adaptive radiotherapy on dosimetric, clinical, and toxicity outcomes for patients with head and neck cancer undergoing chemoradiotherapy with intensity-modulated radiotherapy. Methods: Fifty-one patients with advanced head and neck cancer underwent definitive chemoradiotherapy with the original plan optimized to deliver 70.2 Gy. All patients were resimulated at a median dose of 37.8 Gy (range, 27.0-48.6 Gy) due to changes in tumor volume and/or patient weight loss (>15% from baseline). Thirty-four patients underwent adaptive replanning for their boost planning (21.6 Gy). The dosimetric effects of the adaptive plan were compared to the original plan and the original plan copied on rescan computed tomography. Acute and late toxicities and tumor local control were assessed. Gross tumor volume reduction rate was calculated. Results: With adaptive replanning, the maximum dose to the spinal cord, brain stem, mean ipsilateral, and contralateral parotid had a median reduction of −4.5%, −3.0%, −6.2%, and −2.5%, respectively (median of 34 patients). Median gross tumor volume and boost planning target volume coverage improved by 0.8% and 0.5%, respectively. With a median follow-up time of 17.6 months, median disease-free survival and overall survival was 14.8 and 21.1 months, respectively. Median tumor volume reduction rate was 35.2%. For patients with tumor volume reduction rate ≤35.2%, median disease-free survival was 8.7 months, whereas it was 16.9 months for tumor volume reduction rate >35.2%. Four patients had residual disease after chemoradiotherapy, whereas 64.7% (20 of 34) of patients achieved locoregional control. Conclusion: Implementation of adaptive radiotherapy in head and neck cancer offers benefits including improvement in tumor coverage and decrease in dose to organs at risk. The tumor volume reduction rate during treatment was significantly correlated with disease-free survival and overall survival.

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17

Daal, D., L. Haverkate, L. ten Asbroek-Zwolsman, L. Zwart, E. van Dieren, and E. de Wit. "PD-0940 CBCT-guided online adaptive radiotherapy: implementation of an RTT-led workflow." Radiotherapy and Oncology 161 (August 2021): S783. http://dx.doi.org/10.1016/s0167-8140(21)07219-4.

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18

Baker, A., and H. Mcnair. "SP-0547 Role of the RTT in the clinical implementation of adaptive radiotherapy." Radiotherapy and Oncology 133 (April 2019): S288—S289. http://dx.doi.org/10.1016/s0167-8140(19)30967-3.

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19

Webster, A., S. Hafeez, E. Hall, V. Hansen, H. McNair, R. Lewis, and H. Robert. "OC-0634 Implementation of plan of the day adaptive radiotherapy: Compliance to guidelines." Radiotherapy and Oncology 133 (April 2019): S337—S338. http://dx.doi.org/10.1016/s0167-8140(19)31054-0.

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20

Olsen, J., P. Parikh, D. Yang, T. Zhao, H. Wooten, H. Li, V. Rodriguez, et al. "OC-0246: Clinical implementation of online MR-guided adaptive radiotherapy for abdominopelvic malignancies." Radiotherapy and Oncology 115 (April 2015): S124. http://dx.doi.org/10.1016/s0167-8140(15)40244-0.

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21

Tetar, Shyama U., Anna M. E. Bruynzeel, Frank J. Lagerwaard, Ben J. Slotman, Omar Bohoudi, and Miguel A. Palacios. "Clinical implementation of magnetic resonance imaging guided adaptive radiotherapy for localized prostate cancer." Physics and Imaging in Radiation Oncology 9 (January 2019): 69–76. http://dx.doi.org/10.1016/j.phro.2019.02.002.

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22

Naisbit, M., G. Ward, and J. Lilley. "EP-1813: Clinical implementation of an adaptive planning technique for lung VMAT radiotherapy." Radiotherapy and Oncology 119 (April 2016): S850—S851. http://dx.doi.org/10.1016/s0167-8140(16)33064-x.

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23

McDonald, F., S. Lalondrelle, H. Taylor, K. Warren-Oseni, V. Khoo, H. A. McNair, V. Harris, et al. "Clinical Implementation of Adaptive Hypofractionated Bladder Radiotherapy for Improvement in Normal Tissue Irradiation." Clinical Oncology 25, no. 9 (September 2013): 549–56. http://dx.doi.org/10.1016/j.clon.2013.06.001.

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24

Yan, Di, and Jian Liang. "Expected treatment dose construction and adaptive inverse planning optimization: Implementation for offline head and neck cancer adaptive radiotherapy." Medical Physics 40, no. 2 (January 29, 2013): 021719. http://dx.doi.org/10.1118/1.4788659.

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25

Yin, Chuou, Peng Yang, Shengyuan Zhang, Shaoxian Gu, Ningyu Wang, Fengjie Cui, Jinyou Hu, Xia Li, Zhangwen Wu, and Chengjun Gou. "A self-adaptive prescription dose optimization algorithm for radiotherapy." Open Physics 19, no. 1 (January 1, 2021): 146–51. http://dx.doi.org/10.1515/phys-2021-0012.

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Abstract Purpose The aim of this study is to investigate an implementation method and the results of a voxel-based self-adaptive prescription dose optimization algorithm for intensity-modulated radiotherapy. Materials and methods The self-adaptive prescription dose optimization algorithm used a quadratic objective function, and the optimization engine was implemented using the molecular dynamics. In the iterative optimization process, the optimization prescription dose changed with the relationship between the initial prescription dose and the calculated dose. If the calculated dose satisfied the initial prescription dose, the optimization prescription dose was equal to the calculated dose; otherwise, the optimization prescription dose was equal to the initial prescription dose. We assessed the performance of the self-adaptive prescription dose optimization algorithm with two cases: a mock head and neck case and a breast case. Isodose lines, dose–volume histogram, and dosimetric parameters were compared between the conventional molecular dynamics optimization algorithm and the self-adaptive prescription dose optimization algorithm. Results The self-adaptive prescription dose optimization algorithm produces the different optimization results compared with the conventional molecular dynamics optimization algorithm. For the mock head and neck case, the planning target volume (PTV) dose uniformity improves, and the dose to organs at risk is reduced, ranging from 1 to 4%. For the breast case, the use of self-adaptive prescription dose optimization algorithm also leads to improvements in the dose distribution, with the dose to organs at risk almost unchanged. Conclusion The self-adaptive prescription dose optimization algorithm can generate an ideal clinical plan more effectively, and it could be integrated into a treatment planning system after more cases are studied.

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26

Velec, M., and E. Forde. "SP-0705 Practicalities and Not Technical Uncertainties Limit the Clinical Implementation of Adaptive Radiotherapy." Radiotherapy and Oncology 133 (April 2019): S364. http://dx.doi.org/10.1016/s0167-8140(19)31125-9.

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27

Li, XA, E. Ahunbay, C. Peng, G. Chen, F. Liu, and C. Lawton. "TH-C-BRA-04: Online Adaptive Radiotherapy for Prostate Cancer: Clinical Implementation and Initial Experience." Medical Physics 38, no. 6Part34 (June 2011): 3852–53. http://dx.doi.org/10.1118/1.3613505.

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28

Baker, A., T. Hague, Y. Tsang, and P. J. Hoskin. "OC-0353: Implementation of RTT led ‘plan of the day’ adaptive radiotherapy in cervical cancer." Radiotherapy and Oncology 123 (May 2017): S187—S188. http://dx.doi.org/10.1016/s0167-8140(17)30795-8.

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29

Welsh, James S., Michael Lock, Paul M. Harari, Wolfgang A. Tomé, Jack Fowler, Thomas Rockwell Mackie, Mark Ritter, et al. "Clinical Implementation of Adaptive Helical Tomotherapy: A Unique Approach to Image-Guided Intensity Modulated Radiotherapy." Technology in Cancer Research & Treatment 5, no. 5 (October 2006): 465–79. http://dx.doi.org/10.1177/153303460600500503.

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30

Palm, Russell F., Kurt G. Eicher, Austin J. Sim, Susan Peneguy, Stephen A. Rosenberg, Stuart Wasserman, and Peter A. S. Johnstone. "Assessment of MRI-Linac Economics under the RO-APM." Journal of Clinical Medicine 10, no. 20 (October 14, 2021): 4706. http://dx.doi.org/10.3390/jcm10204706.

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The implementation of the radiation oncology alternative payment model (RO-APM) has raised concerns regarding the development of MRI-guided adaptive radiotherapy (MRgART). We sought to compare technical fee reimbursement under Fee-For-Service (FFS) to the proposed RO-APM for a typical MRI-Linac (MRL) patient load and distribution of 200 patients. In an exploratory aim, a modifier was added to the RO-APM (mRO-APM) to account for the resources necessary to provide this care. Traditional Medicare FFS reimbursement rates were compared to the diagnosis-based reimbursement in the RO-APM. Reimbursement for all selected diagnoses were lower in the RO-APM compared to FFS, with the largest differences in the adaptive treatments for lung cancer (−89%) and pancreatic cancer (−83%). The total annual reimbursement discrepancy amounted to −78%. Without implementation of adaptive replanning there was no difference in reimbursement in breast, colorectal and prostate cancer between RO-APM and mRO-APM. Accommodating online adaptive treatments in the mRO-APM would result in a reimbursement difference from the FFS model of −47% for lung cancer and −46% for pancreatic cancer, mitigating the overall annual reimbursement difference to −54%. Even with adjustment, the implementation of MRgART as a new treatment strategy is susceptible under the RO-APM.

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31

Palm, Russell F., Kurt G. Eicher, Austin J. Sim, Susan Peneguy, Stephen A. Rosenberg, Stuart Wasserman, and Peter A. S. Johnstone. "Assessment of MRI-Linac Economics under the RO-APM." Journal of Clinical Medicine 10, no. 20 (October 14, 2021): 4706. http://dx.doi.org/10.3390/jcm10204706.

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The implementation of the radiation oncology alternative payment model (RO-APM) has raised concerns regarding the development of MRI-guided adaptive radiotherapy (MRgART). We sought to compare technical fee reimbursement under Fee-For-Service (FFS) to the proposed RO-APM for a typical MRI-Linac (MRL) patient load and distribution of 200 patients. In an exploratory aim, a modifier was added to the RO-APM (mRO-APM) to account for the resources necessary to provide this care. Traditional Medicare FFS reimbursement rates were compared to the diagnosis-based reimbursement in the RO-APM. Reimbursement for all selected diagnoses were lower in the RO-APM compared to FFS, with the largest differences in the adaptive treatments for lung cancer (−89%) and pancreatic cancer (−83%). The total annual reimbursement discrepancy amounted to −78%. Without implementation of adaptive replanning there was no difference in reimbursement in breast, colorectal and prostate cancer between RO-APM and mRO-APM. Accommodating online adaptive treatments in the mRO-APM would result in a reimbursement difference from the FFS model of −47% for lung cancer and −46% for pancreatic cancer, mitigating the overall annual reimbursement difference to −54%. Even with adjustment, the implementation of MRgART as a new treatment strategy is susceptible under the RO-APM.

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32

Elter, A., S. Dorsch, M. Marot, C. Gillmann, W. Johnen, A. Runz, C. K. Spindeldreier, S. Klüter, C. P. Karger, and P. Mann. "RSC: Gel dosimetry as a tool for clinical implementation of image-guided radiotherapy." Journal of Physics: Conference Series 2167, no. 1 (January 1, 2022): 012020. http://dx.doi.org/10.1088/1742-6596/2167/1/012020.

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Abstract The implementation of new image-guided radiotherapy (IGRT) treatment techniques requires the development of new quality assurance (QA) methods including geometric and dosimetric validation of the applied dose in 3D. Polymer gels (PG) provide a promising tool to perform such tests. However, to be used in a large variety of clinical applications, the PG must be flexibly applicable. In this work, we present a variety of phantoms used in clinical routine to perform both hardware and workflow tests in IGRT. This includes the validation of isocenter accuracy in magnetic resonance (MR)-guided RT (MRgRT) and end-to-end tests of online adaptive treatment techniques for inter- and intra-fraction motion management in IGRT. The phantoms are equipped with one or more PG containers of different materials including 3D printed containers to allow for 3D dosimetry in arbitrarily shaped structures. The proposed measurement techniques and phantoms provide a flexible application and show a clear benefit of PG for 3D dosimetry in combination with end-to-end tests in many clinical QA applications.

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33

Webster, A., H. McNair, V. N. Hansen, S. Hafeez, R. Lewis, C. Griffin, E. Hall, and R. Huddart. "OC-0590: Multicentre dual-trial implementation of plan of the day (PoD) adaptive radiotherapy: lessons learnt." Radiotherapy and Oncology 152 (November 2020): S331—S332. http://dx.doi.org/10.1016/s0167-8140(21)00612-5.

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34

Keall, Paul J., Doan Trang Nguyen, Ricky O'Brien, Vincent Caillet, Emily Hewson, Per Rugaard Poulsen, Regina Bromley, et al. "The first clinical implementation of real-time image-guided adaptive radiotherapy using a standard linear accelerator." Radiotherapy and Oncology 127, no. 1 (April 2018): 6–11. http://dx.doi.org/10.1016/j.radonc.2018.01.001.

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35

Strolin, S., E. Mezzenga, A. Sarnelli, V. Bruzzaniti, S. Marzi, S. Ungania, G. Sanguineti, L. Marucci, M. Benassi, and L. Strigari. "Implementation of a new strategy for dose tracking and of novel radiobiological models for adaptive radiotherapy." Physica Medica 32 (February 2016): 64. http://dx.doi.org/10.1016/j.ejmp.2016.01.221.

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36

Velten, C., R. Boyd, K. Jeong, S. Kalnicki, M. K. Garg, and W. A. Tome. "Recommendations of Megavoltage Computed Tomography Settings for the Implementation of Adaptive Radiotherapy on Helical Tomotherapy Units." International Journal of Radiation Oncology*Biology*Physics 105, no. 1 (September 2019): E789. http://dx.doi.org/10.1016/j.ijrobp.2019.06.759.

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37

Velten, Christian, Robert Boyd, Kyoungkeun Jeong, Madhur K. Garg, and Wolfgang A. Tomé. "Recommendations of megavoltage computed tomography settings for the implementation of adaptive radiotherapy on helical tomotherapy units." Journal of Applied Clinical Medical Physics 21, no. 5 (March 26, 2020): 87–92. http://dx.doi.org/10.1002/acm2.12859.

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38

Li, Taoran, Xiaofeng Zhu, Danthai Thongphiew, W. Robert Lee, Zeljko Vujaskovic, Qiuwen Wu, Fang-Fang Yin, and Q. Jackie Wu. "On-Line Adaptive Radiation Therapy: Feasibility and Clinical Study." Journal of Oncology 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/407236.

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The purpose of this paper is to evaluate the feasibility and clinical dosimetric benefit of an on-line, that is, with the patient in the treatment position, Adaptive Radiation Therapy (ART) system for prostate cancer treatment based on daily cone-beam CT imaging and fast volumetric reoptimization of treatment plans. A fast intensity-modulated radiotherapy (IMRT) plan reoptimization algorithm is implemented and evaluated with clinical cases. The quality of these adapted plans is compared to the corresponding new plans generated by an experienced planner using a commercial treatment planning system and also evaluated by an in-house developed tool estimating achievable dose-volume histograms (DVHs) based on a database of existing treatment plans. In addition, a clinical implementation scheme for ART is designed and evaluated using clinical cases for its dosimetric qualities and efficiency.

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39

Klüter, Sebastian, Oliver Schrenk, Claudia Katharina Renkamp, Stefan Gliessmann, Melanie Kress, Jürgen Debus, and Juliane Hörner-Rieber. "A practical implementation of risk management for the clinical introduction of online adaptive Magnetic Resonance-guided radiotherapy." Physics and Imaging in Radiation Oncology 17 (January 2021): 53–57. http://dx.doi.org/10.1016/j.phro.2020.12.005.

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40

Darby, P., J. Fox, A. Bromiley, C. Burnett, N. Munn, N. Redgwell, J. McLellan, and R. Moleron. "PO-1860 Implementation of RTT-led workflow for CBCT-guided online adaptive radiotherapy in head and neck." Radiotherapy and Oncology 170 (May 2022): S1650. http://dx.doi.org/10.1016/s0167-8140(22)03823-3.

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41

Boyle, Patrick J., Elizabeth Huynh, Sara Boyle, Jennifer Campbell, Jessica Penney, Iquan Usta, Emily Neubauer Sugar, et al. "Use of a healthy volunteer imaging program to optimize clinical implementation of stereotactic MR-guided adaptive radiotherapy." Technical Innovations & Patient Support in Radiation Oncology 16 (December 2020): 70–76. http://dx.doi.org/10.1016/j.tipsro.2020.10.004.

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42

Tadic, Tony, Jennifer Croke, Jason Xie, Teodor Stanescu, Daniel Letourneau, Jean-Pierre Bissonnette, Stephen Breen, et al. "162 Clinical Implementation of an Integrated Mr-Guided Linear Accelerator for Offline Adaptive Radiotherapy for Cervical Cancer." Radiotherapy and Oncology 139 (October 2019): S69—S70. http://dx.doi.org/10.1016/s0167-8140(19)33218-9.

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43

Boyle, P. J., E. Huynh, E. Neubauer Sugar, F. L. Hacker, S. Boyle, I. Usta, J. Campbell, et al. "Impact of Healthy Volunteer MR-Linac Imaging on Clinical Implementation of Stereotactic MR-Guided Online Adaptive Radiotherapy." International Journal of Radiation Oncology*Biology*Physics 108, no. 3 (November 2020): S101. http://dx.doi.org/10.1016/j.ijrobp.2020.07.2277.

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44

Sibolt, Patrik, Lina M. Andersson, Lucie Calmels, David Sjöström, Ulf Bjelkengren, Poul Geertsen, and Claus F. Behrens. "Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region." Physics and Imaging in Radiation Oncology 17 (April 2021): 1–7. http://dx.doi.org/10.1016/j.phro.2020.12.004.

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45

Ophelie, Piron, Varfalvy Nicolas, and Archambault Louis. "Implementation of an adaptive radiotherapy method using EPID and gamma analysis for head and neck and lung cases." Physica Medica 32 (September 2016): 239. http://dx.doi.org/10.1016/j.ejmp.2016.07.499.

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46

Sleeman, W., N. Dogan, J. Siebers, M. Murphy, J. Williamson, and M. Fatyga. "SU-GG-T-388: Design and Implementation of a Computing Framework for An Image Guided Adaptive Radiotherapy Research Program." Medical Physics 35, no. 6Part15 (June 2008): 2814. http://dx.doi.org/10.1118/1.2962138.

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47

McDonald, F., S. Lalondrelle, H. Taylor, V. Harris, V. Hansen, V. Khoo, and R. A. Huddart. "Adaptive-predictive organ localization (a-POLO) in the clinic: Updated results in hypofractionated bladder radiotherapy." Journal of Clinical Oncology 29, no. 7_suppl (March 1, 2011): 282. http://dx.doi.org/10.1200/jco.2011.29.7_suppl.282.

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282 Background: The A-POLO strategy allows the optimal 'plan of the day' to be selected online for radiotherapy (RT) delivery. The methodology is implemented in a phase II study of patients with muscle-invasive bladder cancer who are not suitable for cystectomy/daily RT and are receiving hypofractionated RT. Methods: Planning scans were performed at 0 and 30 minutes post void (CT0 and CT30). Three conformal plans were created (small, intermediate, large) ( Table ). Patients were prescribed 6Gy weekly for 5–6 weeks. A pre-RT cone beam CT (CBCT) scan and online set-up correction were performed. The plan giving the optimal target coverage was selected by 2 observers. Offline plan selection was also carried out by an independent observer. A post-RT CBCT was acquired to calculate the percentage of the CTV covered by 95% of the dose (V95). The mean A- POLO volume was compared to our previous institutional standard PTV (1.5cm isotropic margins) (PTViso). Outcome data were collected. Results: A total of 77 RT fractions were delivered to 14 patients. The small plan was delivered for 38 (49%) fractions and the large for 6 (8%) fractions. The concordance rate between online and offline plan selection was 71/77 (92%). The mean CTV V95 was 99% (patient mean range 97–100%). The mean time between the pre- and post-treatment CBCT was 15 minutes. The mean reduction between PTViso and mean A-POLO PTV was 42% (range 16– 59%). 2 patients had grade (G) 3 (CTCv3.0) treatment-related acute toxicity. There have been no treatment-related G4 acute or G3 late toxicities. With a median follow-up of 7.3 months 10 patients are alive with 8 disease free (1 local and 1 distant relapse). Conclusions: Implementation of A-POLO RT is feasible, well tolerated, and associated with good concordance in 'plan of the day' selection. An individualized treatment plan can be delivered with each fraction to achieve a reduction in PTV compared to PTViso with maintenance of target coverage. [Table: see text] No significant financial relationships to disclose.

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O'Brien, Ricky T., Owen Dillon, Benjamin Lau, Armia George, Sandie Smith, Andrew Wallis, Jan-Jakob Sonke, Paul J. Keall, and Shalini K. Vinod. "The first-in-human implementation of adaptive 4D cone beam CT for lung cancer radiotherapy: 4DCBCT in less time with less dose." Radiotherapy and Oncology 161 (August 2021): 29–34. http://dx.doi.org/10.1016/j.radonc.2021.05.021.

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Intven, M. P. W., S. R. de Mol van Otterloo, S. Mook, P. A. H. Doornaert, E. N. de Groot-van Breugel, G. G. Sikkes, M. E. Willemsen-Bosman, H. M. van Zijp, and R. H. N. Tijssen. "Online adaptive MR-guided radiotherapy for rectal cancer; feasibility of the workflow on a 1.5T MR-linac: clinical implementation and initial experience." Radiotherapy and Oncology 154 (January 2021): 172–78. http://dx.doi.org/10.1016/j.radonc.2020.09.024.

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Ricci, Jacob C., Justin Rineer, Amish P. Shah, Sanford L. Meeks, and Patrick Kelly. "Proposal and Evaluation of a Physician-Free, Real-Time On-Table Adaptive Radiotherapy (PF-ROAR) Workflow for the MRIdian MR-Guided LINAC." Journal of Clinical Medicine 11, no. 5 (February 23, 2022): 1189. http://dx.doi.org/10.3390/jcm11051189.

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With the implementation of MR-LINACs, real-time adaptive radiotherapy has become a possibility within the clinic. However, the process of adapting a patient’s plan is time consuming and often requires input from the entire clinical team, which translates to decreased throughput and limited patient access. In this study, the authors propose and simulate a workflow to address these inefficiencies in staffing and patient throughput. Two physicians, three radiation therapists (RTT), and a research fellow each adapted bladder and bowel contours for 20 fractions from 10 representative patient plans. Contouring ability was compared via calculation of a Dice Similarity Index (DSI). The DSI for bladder and bowel based on each potential physician–therapist pair, as well as an inter-physician comparison, exhibited good overlap amongst all comparisons (p = 0.868). Plan quality was compared through calculation of the conformity index (CI), as well as an evaluation of the plan’s dose to a ‘gold standard’ set of structures. Overall, non-physician plans passed 91.2% of the time. Of the eight non-physician plans that failed their clinical evaluation, six also failed their evaluation against the ‘gold standard’. Another two plans that passed their clinical evaluation subsequently failed in their evaluation against the ‘gold standard’. Thus, the PF-ROAR process has a success rate of 97.5%, with 78/80 plans correctly adapted to the gold standard or halted at treatment. These findings suggest that a physician-free workflow can be well tolerated provided RTTs continue to develop knowledge of MR anatomy and careful attention is given to understanding the complexity of the plan prior to treatment.

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