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McCullum LB, Karagoz A, Dede C, Garcia R, Nosrat F, Hemmati M, Hosseinian S, Schaefer AJ, Fuller CD. Markov models for clinical decision-making in radiation oncology: A systematic review. J Med Imaging Radiat Oncol 2024. [PMID: 38766899 DOI: 10.1111/1754-9485.13656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/03/2024] [Indexed: 05/22/2024]
Abstract
The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.
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Affiliation(s)
- Lucas B McCullum
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Aysenur Karagoz
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Cem Dede
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Raul Garcia
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Fatemeh Nosrat
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Mehdi Hemmati
- School of Industrial and Systems Engineering, The University of Oklahoma, Norman, Oklahoma, USA
| | | | - Andrew J Schaefer
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas, USA
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Gan Y, Langendijk JA, Oldehinkel E, Lin Z, Both S, Brouwer CL. Optimal timing of re-planning for head and neck adaptive radiotherapy. Radiother Oncol 2024; 194:110145. [PMID: 38341093 DOI: 10.1016/j.radonc.2024.110145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND AND PURPOSE Adaptive radiotherapy (ART) relies on re-planning to correct treatment variations, but the optimal timing of re-planning to account for dose changes in head and neck organs at risk (OARs) is still under investigation. We aimed to find out the optimal timing of re-planning in head and neck ART. MATERIALS AND METHODS A total of 110 head and neck cancer patients were retrospectively enrolled. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. The K-nearest-neighbour method was used for missing data imputation of weekly Dmean. A dose deviation map was built using the planning Dmean and weekly Dmean values and then used to simulate different ART scenarios consisting of 1 to 6 re-plannings. The difference between accumulated Dmean and planning Dmean before re-planning (ΔDmean_acc_noART) and after re-planning (ΔDmean_acc_ART) were evaluated and compared. RESULTS Among all the OARs, supraglottic showed the largest ΔDmean_acc_noART (1.23 ± 3.13 Gy) and most cases of ΔDmean_acc_noART > 3 Gy (26 patients). The 3rd week is suggested in the optimal timing of re-planning for 10 OARs. For all the organs except arytenoid, 2 re-plannings were able to guarantee the ΔDmean_acc_ART below 3 Gy while the average |ΔDmean_acc_ART| was below 1 Gy. ART scenarios of 2_4, 3_4, 3_5 (week of re-planning separated with "_") were able to guarantee ΔDmean_acc_ART of 99 % of patients below 3 Gy simultaneously for 19 OARs. CONCLUSIONS The optimal timing of re-planning was suggested for different organs at risk in head and neck adaptive radiotherapy. Generic scenarios of timing and frequency for re-planning can be applied to guarantee the increase of accumulated mean dose within 3 Gy simultaneously for multiple organs.
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Affiliation(s)
- Yong Gan
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands; Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China.
| | - Johannes A Langendijk
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
| | - Edwin Oldehinkel
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
| | - Zhixiong Lin
- Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China
| | - Stefan Both
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
| | - Charlotte L Brouwer
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands
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McDonald BA, Cardenas CE, O'Connell N, Ahmed S, Naser MA, Wahid KA, Xu J, Thill D, Zuhour RJ, Mesko S, Augustyn A, Buszek SM, Grant S, Chapman BV, Bagley AF, He R, Mohamed ASR, Christodouleas J, Brock KK, Fuller CD. Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers. Med Phys 2024; 51:278-291. [PMID: 37475466 PMCID: PMC10799175 DOI: 10.1002/mp.16582] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. PURPOSE In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. METHODS Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. RESULTS DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). CONCLUSIONS The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.
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Affiliation(s)
- Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Sara Ahmed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mohamed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | | | - Raed J Zuhour
- Department of Radiation Oncology, The University of Texas Medical Branch, Galveston, Texas, USA
| | - Shane Mesko
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alexander Augustyn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Samantha M Buszek
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stephen Grant
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bhavana V Chapman
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alexander F Bagley
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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All S, Zhong X, Choi B, Kim JS, Zhuang T, Avkshtol V, Sher D, Lin MH, Moon DH. In Silico Analysis of Adjuvant Head and Neck Online Adaptive Radiation Therapy. Adv Radiat Oncol 2024; 9:101319. [PMID: 38260220 PMCID: PMC10801641 DOI: 10.1016/j.adro.2023.101319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/13/2023] [Indexed: 01/24/2024] Open
Abstract
Purpose Recently developed online adaptive radiation therapy (OnART) systems enable frequent treatment plan adaptation, but data supporting a dosimetric benefit in postoperative head and neck radiation therapy (RT) are sparse. We performed an in silico dosimetric study to assess the potential benefits of a single versus weekly OnART in the treatment of patients with head and neck squamous cell carcinoma in the adjuvant setting. Methods and Materials Twelve patients receiving conventionally fractionated RT over 6 weeks and 12 patients receiving hypofractionated RT over 3 weeks on a clinical trial were analyzed. The OnART emulator was used to virtually adapt either once midtreatment or weekly based on the patient's routinely performed cone beam computed tomography. The planning target volume (PTV) coverage, dose heterogeneity, and cumulative dose to the organs at risk for these 2 adaptive approaches were compared with the nonadapted plan. Results In total, 13, 8, and 3 patients had oral cavity, oropharynx, and larynx primaries, respectively. In the conventionally fractionated RT cohort, weekly OnART led to a significant improvement in PTV V100% coverage (6.2%), hot spot (-1.2 Gy), and maximum cord dose (-3.1 Gy), whereas the mean ipsilateral parotid dose increased modestly (1.8 Gy) versus the nonadapted plan. When adapting once midtreatment, PTV coverage improved with a smaller magnitude (0.2%-2.5%), whereas dose increased to the ipsilateral parotid (1.0-1.1 Gy) and mandible (0.2-0.7 Gy). For the hypofractionated RT cohort, similar benefit was observed with weekly OnART, including significant improvement in PTV coverage, hot spot, and maximum cord dose, whereas no consistent dosimetric advantage was seen when adapting once midtreatment. Conclusions For head and neck squamous cell carcinoma adjuvant RT, there was a limited benefit of single OnART, but weekly adaptations meaningfully improved the dosimetric criteria, predominantly PTV coverage and dose heterogeneity. A prospective study is ongoing to determine the clinical benefit of OnART in this setting.
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Affiliation(s)
- Sean All
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Xinran Zhong
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Byongsu Choi
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Tingliang Zhuang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Vladimir Avkshtol
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - David Sher
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Dominic H. Moon
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
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Gan Y, Langendijk JA, van der Schaaf A, van den Bosch L, Oldehinkel E, Lin Z, Both S, Brouwer CL. An efficient strategy to select head and neck cancer patients for adaptive radiotherapy. Radiother Oncol 2023; 186:109763. [PMID: 37353058 DOI: 10.1016/j.radonc.2023.109763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND AND PURPOSE Adaptive radiotherapy (ART) is workload intensive but only benefits a subgroup of patients. We aimed to develop an efficient strategy to select candidates for ART in the first two weeks of head and neck cancer (HNC) radiotherapy. MATERIALS AND METHODS This study retrospectively enrolled 110 HNC patients who underwent modern photon radiotherapy with at least 5 weekly in-treatment re-scan CTs. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of Dmean (ΔDmean) and dichotomized difference of clinical relevance (BIOΔNTCP) were used for modelling to determine the cut-off maximum ΔDmean of OARs in week 1 and 2 (maxΔDmean_1 and maxΔDmean_2). Four strategies to select candidates for ART, using cut-off maxΔDmean were compared. RESULTS The Spearman's rank correlation test showed significant positive correlation between maxΔDmean and BIOΔNTCP (p-value <0.001). For major BIOΔNTCP (>5%) of acute and late toxicity, 10.9% and 4.5% of the patients were true candidates for ART. Strategy C using both cut-off maxΔDmean_1 (3.01 and 5.14 Gy) and cut-off maxΔDmean_2 (3.41 and 5.30 Gy) showed the best sensitivity, specificity, positive and negative predictive values (0.92, 0.82, 0.38, 0.99 for acute toxicity and 1.00, 0.92, 0.38, 1.00 for late toxicity, respectively). CONCLUSIONS We propose an efficient selection strategy for ART that is able to classify the subgroup of patients with >5% BIOΔNTCP for late toxicity using imaging in the first two treatment weeks.
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Affiliation(s)
- Yong Gan
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands; Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China.
| | - Johannes A Langendijk
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Arjen van der Schaaf
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Lisa van den Bosch
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Edwin Oldehinkel
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Zhixiong Lin
- Shantou University, Cancer Hospital of Shantou University Medical College, Department of Radiotherapy, China
| | - Stefan Both
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Charlotte L Brouwer
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
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Uh J, Jordan JA, Pappo AS, Krasin MJ, Hua C. Adaptive Proton Therapy for Pediatric Parameningeal Rhabdomyosarcoma: On-Treatment Anatomic Changes and Timing to Replanning. Clin Oncol (R Coll Radiol) 2023; 35:245-254. [PMID: 36764878 PMCID: PMC10783810 DOI: 10.1016/j.clon.2023.01.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/16/2022] [Accepted: 01/18/2023] [Indexed: 01/26/2023]
Abstract
PURPOSE To characterize on-treatment changes in GTV morphology in children with parameningeal rhabdomyosarcoma receiving upfront proton therapy with concurrent chemotherapy and thereby provide guidance on the timing of on-treatment imaging and adaptive replanning. METHODS AND MATERIALS GTV was delineated on 86 simulation and weekly MR images of 15 prospectively enrolled patients (aged 1-21 years). Temporal changes from baseline in volume and surface (95% Hausdorff distance) were analyzed in relation to the need for plan verification and the resultant doses with hypothetical no treatment adaptation. RESULTS The median time was 6 days from the initiation of chemotherapy to CT+MR simulation and 15 days from the simulation to the start of radiotherapy. All but 1 patient showed a continuous decrease in GTV (0.16-1.52%/day) after simulation. At 3 weeks from simulation, 10 of 15 patients exhibited a significant reduction in volume (median, 20%; range, 6-29%). Without replanning, these changes could lead to a reduction in CTV V95 by 7-14% (n = 2) and/or an increase in D0.01 cc/Dmean of adjacent organs at risk by 6-21% of the prescribed target dose (n = 7). Significant dosimetric consequences occurred in cases with (1) a considerable weight gain, (2) shrinkage of the skin surface, or (3) tumor regression in the oral or nasal cavity and sinus that altered air-tissue components in the beam path. The subsequent GTV and dosimetry after 3 weeks from simulation (4 weeks from chemotherapy initiation) demonstrated a relatively stable trend. CONCLUSIONS On-treatment imaging at 3 weeks after simulation is recommended, if the simulation is performed at 1 week after the initiation of chemotherapy, to detect significant anatomic changes that could result in >5% deviation from planned target coverage and/or organ doses in pediatric patients with parameningeal rhabdomyosarcoma receiving early proton therapy.
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Affiliation(s)
- J Uh
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
| | - J A Jordan
- College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA; Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - A S Pappo
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - M J Krasin
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - C Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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McDonald BA, Zachiu C, Christodouleas J, Naser MA, Ruschin M, Sonke JJ, Thorwarth D, Létourneau D, Tyagi N, Tadic T, Yang J, Li XA, Bernchou U, Hyer DE, Snyder JE, Bubula-Rehm E, Fuller CD, Brock KK. Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation. Front Oncol 2023; 12:1086258. [PMID: 36776378 PMCID: PMC9909539 DOI: 10.3389/fonc.2022.1086258] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/21/2022] [Indexed: 01/27/2023] Open
Abstract
MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.
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Affiliation(s)
- Brigid A. McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Cornel Zachiu
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark Ruschin
- Department of Radiation Oncology, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Tuebingen, Germany
| | - Daniel Létourneau
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Uffe Bernchou
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Daniel E. Hyer
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | - Jeffrey E. Snyder
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | | | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kristy K. Brock
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Teuwen J, Gouw ZA, Sonke JJ. Artificial Intelligence for Image Registration in Radiation Oncology. Semin Radiat Oncol 2022; 32:330-342. [DOI: 10.1016/j.semradonc.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, Matsopoulos GK. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers (Basel) 2022; 14:cancers14153573. [PMID: 35892831 PMCID: PMC9331795 DOI: 10.3390/cancers14153573] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Correspondence: ; Tel.: +30-21-0772-3577
| | - Ioannis Kakkos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Department of Biomedical Engineering, University of West Attica, 122 43 Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece;
| | - Vassilis Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Anna Zygogianni
- 1st Department of Radiology, Radiotherapy Unit, ARETAIEION University Hospital, 115 28 Athens, Greece;
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
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Zhang Y, McGowan Holloway S, Zoë Wilson M, Alshaikhi J, Tan W, Royle G, Bär E. DIR-based models to predict weekly anatomical changes in head and neck cancer proton therapy. Phys Med Biol 2022; 67:095001. [PMID: 35316795 PMCID: PMC10437002 DOI: 10.1088/1361-6560/ac5fe2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 03/17/2022] [Accepted: 03/22/2022] [Indexed: 11/12/2022]
Abstract
Objective. We proposed two anatomical models for head and neck patients to predict anatomical changes during the course of radiotherapy.Approach. Deformable image registration was used to build two anatomical models: (1) the average model (AM) simulated systematic progressive changes across the patient cohort; (2) the refined individual model (RIM) used a patient's CT images acquired during treatment to update the prediction for each individual patient. Planning CTs and weekly CTs were used from 20 nasopharynx patients. This dataset included 15 training patients and 5 test patients. For each test patient, a spot scanning proton plan was created. Models were evaluated using CT number differences, contours, proton spot location deviations and dose distributions.Main results. If no model was used, the CT number difference between the planning CT and the repeat CT at week 6 of treatment was on average 128.9 Hounsfield Units (HU) over the test population. This can be reduced to 115.5 HU using the AM, and to 110.5 HU using the RIM3(RIM, updated at week (3). When the predicted contours from the models were used, the average mean surface distance of parotid glands can be reduced from 1.98 (no model) to 1.16 mm (AM) and 1.19 mm (RIM3) at week 6. Using the proton spot range, the average anatomical uncertainty over the test population reduced from 4.47 ± 1.23 (no model) to 2.41 ± 1.12 mm (AM), and 1.89 ± 0.96 mm (RIM3). Based on the gamma analysis, the average gamma index over the test patients was improved from 93.87 ± 2.48 % (no model) to 96.16 ± 1.84% (RIM3) at week 6.Significance. The AM and the RIM both demonstrated the ability to predict anatomical changes during the treatment. The RIM can gradually refine the prediction of anatomical changes based on the AM. The proton beam spots provided an accurate and effective way for uncertainty evaluation.
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Affiliation(s)
- Ying Zhang
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Stacey McGowan Holloway
- CRUK RadNet Glasgow, University of Glasgow, Beatson West of Scotland Cancer Centre, Radiotherapy Physics, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom
| | - Megan Zoë Wilson
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Jailan Alshaikhi
- Saudi Proton Therapy Center, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Wenyong Tan
- Department of Oncology, Shenzhen Hospital of Southern Medical University Shenzhen 518101, People's Republic of China
| | - Gary Royle
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Esther Bär
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, United Kingdom
- University College London Hospitals NHS Foundation Trust, Radiotherapy Physics, 250 Euston Road, London NW1 2PG, United Kingdom
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11
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MR-Guided Adaptive Radiotherapy for Head and Neck Cancer: Prospective Evaluation of Migration and Anatomical Changes of the Major Salivary Glands. Cancers (Basel) 2021; 13:cancers13215404. [PMID: 34771567 PMCID: PMC8582485 DOI: 10.3390/cancers13215404] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/13/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of this study was to quantify anatomical changes of parotids and submandibular glands and evaluate potential dosimetric advantages during weekly adaptive MR-guided radiotherapy (MRgRT) for the definitive treatment of head and neck cancer (HNC). The data and plans of 12 patients treated with bilateral intensity-modulated radiotherapy for HNC using MR-linac, with weekly offline adaptations, were prospectively evaluated. The positional and volumetric changes of the salivary glands were analyzed by manual segmentation in weekly MRI images and the dosimetric impact of these anatomical changes on the adapted treatment plans was assessed. The mean volume change in parotid and submandibular gland volume was -31.9% (p < 0.0001) and -29.7% (p < 0.0001) after five weeks, respectively. The volume change was significantly correlated with the cumulative dose for the respective gland at the time of volume measurement. Inter-parotid distance changed by -5.4% (6.5 mm) on average after five weeks (p = 0.0005). The distance became significantly smaller only in the left-right direction. The inter-submandibular gland distance changed by 0.7 mm (p = 0.38). This study demonstrated significant changes in salivary gland volumes and position following daily MR guidance and weekly plan adaptation. Ongoing clinical trials will provide data on the clinical impact of these changes and novel MR-based adaptation strategies.
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12
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Kanehira T, van Kranen S, Jansen T, Hamming-Vrieze O, Al-Mamgani A, Sonke JJ. Comparisons of normal tissue complication probability models derived from planned and delivered dose for head and neck cancer patients. Radiother Oncol 2021; 164:209-215. [PMID: 34619234 DOI: 10.1016/j.radonc.2021.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/24/2021] [Accepted: 09/18/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND AND PURPOSE Normal tissue complication probability (NTCP) models are typically derived from the planned dose distribution, which can deviate from the delivered dose due to anatomical day-to-day variations. The aim of this study was to compare NTCP models derived from the planned and the delivered dose for head and neck cancer (HNC) patients. MATERIAL AND METHOD 322 HNC patients who received radiotherapy with daily CBCT guidance were included in this retrospective study. The delivered dose was estimated by deformably accumulating dose from daily CBCT to planning anatomy. We used a Lyman-Kutcher-Burman NTCP model, to relate the equivalent uniform dose (EUD) of organs at risk (OAR) with oral mucositis, xerostomia and dysphagia respectively. We compared the model parameters and performances. RESULTS The median differences between planned and delivered EUD to the OARs were significantly larger for patients with toxicity than without for acute dysphagia (≥G2 and ≥G3) and late dysphagia (≥G3) (p < 0.05). Those differences resulted in small differences in steepness and agreement to the data between delivered- and planned-fitted NTCP curves, and the differences were not significant. The differences in AUC were less than 0.01. CONCLUSION Differences between delivered and planned dose did not lead to significant differences in NTCP curves. The additional clinical relevance of NTCP models using accumulated dose for oral mucositis, xerostomia and dysphagia in HNC radiotherapy is likely to be limited.
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Affiliation(s)
- Takahiro Kanehira
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Simon van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Tomas Jansen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Olga Hamming-Vrieze
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Abrahim Al-Mamgani
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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13
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Iliadou V, Economopoulos TL, Karaiskos P, Kouloulias V, Platoni K, Matsopoulos GK. Deformable image registration to assist clinical decision for radiotherapy treatment adaptation for head and neck cancer patients. Biomed Phys Eng Express 2021; 7. [PMID: 34265756 DOI: 10.1088/2057-1976/ac14d1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/15/2021] [Indexed: 11/12/2022]
Abstract
Head and neck (H&N) cancer patients often present anatomical and geometrical changes in tumors and organs at risk (OARs) during radiotherapy treatment. These changes may result in the need to adapt the existing treatment planning, using an expert's subjective opinion, for offline adaptive radiotherapy and a new treatment planning before each treatment, for online adaptive radiotherapy. In the present study, a fast methodology is proposed to assist in planning adaptation clinical decision using tumor and parotid glands percentage volume changes during treatment. The proposed approach was applied to 40 Η&Ν cases, with one planning Computed Tomography (pCT) image and CBCT scans for 6 weeks of treatment per case. Deformable registration was used for each patient's pCT image alignment to its weekly CBCT. The calculated transformations were used to align each patient's anatomical structures to the weekly anatomy. Clinical target volume (CTV) and parotid gland volume percentage changes were calculated in each case. The accuracy of the achieved image alignment was validated qualitatively and quantitatively. Furthermore, statistical analysis was performed to test if there is a statistically significant correlation between CTV and parotid glands volume percentage changes. Average MDA for CTV and parotid glands between corresponding structures defined by an expert in CBCTs and automatically calculated through registration was 1.4 ± 0.1 mm and 1.5 ± 0.1 mm, respectively. The mean registration time of the first CBCT image registration for 40 cases was lower than 3.4 min. Five patients show more than 20% tumor volume change. Six patients show more than 30% parotid glands volume change. Ten out of 40 patients proposed for planning adaptation. All the statistical tests performed showed no correlation between CTV/parotid glands percentage volume changes. The aim to assist in clinical decision making on a fast and automatic way was achieved using the proposed methodology, thereby reducing workload in clinical practice.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Theodore L Economopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Vasileios Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, Athens, Greece
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, Athens, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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14
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Washio H, Ohira S, Funama Y, Ueda Y, Isono M, Inui S, Miyazaki M, Teshima T. Accuracy of dose calculation on iterative CBCT for head and neck radiotherapy. Phys Med 2021; 86:106-112. [PMID: 34102546 DOI: 10.1016/j.ejmp.2021.05.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 05/15/2021] [Accepted: 05/19/2021] [Indexed: 10/21/2022] Open
Abstract
PURPOSE To evaluate the feasibility of the use of iterative cone-beam computed tomography (CBCT) for dose calculation in the head and neck region. METHODS This study includes phantom and clinical studies. All acquired CBCT images were reconstructed with Feldkamp-Davis-Kress algorithm-based CBCT (FDK-CBCT) and iterative CBCT (iCBCT) algorithm. The Hounsfield unit (HU) consistency between the head and body phantoms was determined in both reconstruction techniques. Volumetric modulated arc therapy (VMAT) plans were generated for 16 head and neck patients on a planning CT scan, and the doses were recalculated on FDK-CBCT and iCBCT with Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB). As a comparison of the accuracy of dose calculations, the absolute dosimetric difference and 1%/1 mm gamma passing rate analysis were analyzed. RESULTS The difference in the mean HU values between the head and body phantoms was larger for FDK-CBCT (max value: 449.1 HU) than iCBCT (260.0 HU). The median dosimetric difference from the planning CT were <1.0% for both FDK-CBCT and iCBCT but smaller differences were found with iCBCT (planning target volume D50%: 0.38% (0.15-0.59%) for FDK-CBCT, 0.28% (0.13-0.49%) for iCBCT, AAA; 0.14% (0.04-0.19%) for FDK-CBCT, 0.07% (0.02-0.20%) for iCBCT). The mean gamma passing rate was significantly better in iCBCT than FDK-CBCT (AAA: 98.7% for FDK-CBCT, 99.4% for iCBCT; AXB: 96.8% for FDK_CBCT, 97.5% for iCBCT). CONCLUSION The iCBCT-based dose calculation in VMAT for head and neck cancer was accurate compared to FDK-CBCT.
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Affiliation(s)
- Hayate Washio
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Science, Kumamoto University, Kumamoto, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masaru Isono
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
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15
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McDonald BA, Vedam S, Yang J, Wang J, Castillo P, Lee B, Sobremonte A, Ahmed S, Ding Y, Mohamed ASR, Balter P, Hughes N, Thorwarth D, Nachbar M, Philippens MEP, Terhaard CHJ, Zips D, Böke S, Awan MJ, Christodouleas J, Fuller CD. Initial Feasibility and Clinical Implementation of Daily MR-Guided Adaptive Head and Neck Cancer Radiation Therapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation. Int J Radiat Oncol Biol Phys 2021; 109:1606-1618. [PMID: 33340604 PMCID: PMC7965360 DOI: 10.1016/j.ijrobp.2020.12.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 11/04/2020] [Accepted: 12/11/2020] [Indexed: 01/20/2023]
Abstract
PURPOSE This prospective study is, to our knowledge, the first report of daily adaptive radiation therapy (ART) for head and neck cancer (HNC) using a 1.5T magnetic resonance imaging-linear accelerator (MR-linac) with particular focus on safety and feasibility and dosimetric results of an online rigid registration-based adapt to position (ATP) workflow. METHODS AND MATERIALS Ten patients with HNC received daily ART on a 1.5T/7MV MR-linac, 6 using ATP only and 4 using ATP with 1 offline adapt-to-shape replan. Setup variability with custom immobilization masks was assessed by calculating the mean systematic error (M), standard deviation of the systematic error (Σ), and standard deviation of the random error (σ) of the isocenter shifts. Quality assurance was performed with a cylindrical diode array using 3%/3 mm γ criteria. Adaptive treatment plans were summed for each patient to compare the delivered dose with the planned dose from the reference plan. The impact of dosimetric variability between adaptive fractions on the summation plan doses was assessed by tracking the number of optimization constraint violations at each individual fraction. RESULTS The random errors (mm) for the x, y, and z isocenter shifts, respectively, were M = -0.3, 0.7, 0.1; Σ = 3.3, 2.6, 1.4; and σ = 1.7, 2.9, 1.0. The median (range) γ pass rate was 99.9% (90.9%-100%). The differences between the reference and summation plan doses were -0.61% to 1.78% for the clinical target volume and -11.74% to 8.11% for organs at risk (OARs), although an increase greater than 2% in OAR dose only occurred in 3 cases, each for a single OAR. All cases had at least 2 fractions with 1 or more constraint violations. However, in nearly all instances, constraints were still met in the summation plan despite multiple single-fraction violations. CONCLUSIONS Daily ART on a 1.5T MR-linac using an online ATP workflow is safe and clinically feasible for HNC and results in delivered doses consistent with planned doses.
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Affiliation(s)
- Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, Texas
| | - Sastry Vedam
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Pamela Castillo
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Belinda Lee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Angela Sobremonte
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Sara Ahmed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Yao Ding
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, Texas
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Neil Hughes
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Marcel Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | | | - Chris H J Terhaard
- Department of Radiotherapy, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Daniel Zips
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Simon Böke
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Musaddiq J Awan
- Department of Radiation Oncology, Medical College of Wisconsin, Wauwatosa, Wisconsin
| | - John Christodouleas
- Elekta, Inc., Stockholm, Sweden; Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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16
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Kearney M, Coffey M, Rossi M, Tsang Y. Future-proof Radiation therapist (RTT) practice in a pandemic - Lessons learnt from COVID-19. Tech Innov Patient Support Radiat Oncol 2021; 17:18-24. [PMID: 33564723 PMCID: PMC7862908 DOI: 10.1016/j.tipsro.2021.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/05/2021] [Accepted: 02/01/2021] [Indexed: 12/20/2022] Open
Abstract
RT is an essential service that must continue despite the challenges posed by COVID-19. Our study suggests changes were implemented into RTT practice in response to COVID-19. Proactive measures are needed to protect both RTTs and patients in future Covid surges.
Background and Purpose The European SocieTy for Radiotherapy and Oncology Radiation Therapist Committee (ESTRO RTTC) published a guidance document and infographic providing recommendations to minimise risk of COVID-19 transmission in radiotherapy (RT) departments. The purpose of this study was to investigate the changes embedded in RT practice in the COVID-19 era and to recommend proactive measures to protect RT practice in future pandemics. Materials and Methods The study was initiated by the ESTRO Radiation Oncology Safety and Quality Committee (ROSQC). A survey consisting of multiple choice, open ended and Likert scale questions was created to analyse the extent of changes embedded in RT practice in response to the COVID-19 pandemic under the four domains: patient care, RTT workflow, remote working and RT practice. This online survey was distributed globally in May 2020. Results 229 respondents across 27 countries completed the survey. 60% of respondents reported continuing/commencing RT in COVID-19 patients. Routine testing of patients and RTTs was not common. Split teams' procedures, hot linacs and separate entrances were implemented by 50% of respondents. Remote working was implemented for RT team members where face to face patient contact was not essential. Lack of staff, connectivity issues and lack of confirmed positive cases in the department were the main reasons cited for not implementing recommended measures. Conclusion It is suggested that RT departments have responded to the COVID-19 pandemic and implemented certain changes in RT practice. RT departments should act now to implement recommended proactive measures to protect patients and RTTs – frontline healthcare workers.
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Affiliation(s)
- Maeve Kearney
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity College, Dublin 2, Ireland
| | - Mary Coffey
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity College, Dublin 2, Ireland
| | - Maddalena Rossi
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Yat Tsang
- Radiotherapy Department, Mount Vernon Cancer Centre, Northwood, Middlesex, United Kingdom
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Kanehira T, Svensson S, van Kranen S, Sonke JJ. Accurate estimation of daily delivered radiotherapy dose with an external treatment planning system. Phys Imaging Radiat Oncol 2020; 14:39-42. [PMID: 33458312 PMCID: PMC7807587 DOI: 10.1016/j.phro.2020.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 05/16/2020] [Accepted: 05/18/2020] [Indexed: 11/28/2022] Open
Abstract
Accurate estimation of the daily radiotherapy dose is challenging in a multi-institutional collaboration when the institution specific treatment planning system (TPS) is not available. We developed and evaluated a method to tackle this problem. Residual errors in daily estimations were minimized with single correction based on the planned dose. For nine patients, medians of the absolute estimation errors for targets and OARs were less than 0.2 Gy (Dmean), 0.3 Gy (D1), and 0.1 Gy (D99). In general, mimicking errors were significantly smaller than dose differences caused by anatomical changes. The demonstrated accuracy may facilitate dose accumulation in a multi-institutional/multi-vendor setting.
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Affiliation(s)
- Takahiro Kanehira
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | | | - Simon van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
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