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Bryant JM, Cruz-Chamorro RJ, Gan A, Liveringhouse C, Weygand J, Nguyen A, Keit E, Sandoval ML, Sim AJ, Perez BA, Dilling TJ, Redler G, Andreozzi J, Nardella L, Naghavi AO, Feygelman V, Latifi K, Rosenberg SA. Structure-specific rigid dose accumulation dosimetric analysis of ablative stereotactic MRI-guided adaptive radiation therapy in ultracentral lung lesions. COMMUNICATIONS MEDICINE 2024; 4:96. [PMID: 38778215 PMCID: PMC11111790 DOI: 10.1038/s43856-024-00526-7] [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: 09/05/2023] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Definitive local therapy with stereotactic ablative radiation therapy (SABR) for ultracentral lung lesions is associated with a high risk of toxicity, including treatment related death. Stereotactic MR-guided adaptive radiation therapy (SMART) can overcome many of the challenges associated with SABR treatment of ultracentral lesions. METHODS We retrospectively identified 14 consecutive patients who received SMART to ultracentral lung lesions from 10/2019 to 01/2021. Patients had a median distance from the proximal bronchial tree (PBT) of 0.38 cm. Tumors were most often lung primary (64.3%) and HILUS group A (85.7%). A structure-specific rigid registration approach was used for cumulative dose analysis. Kaplan-Meier log-rank analysis was used for clinical outcome data and the Wilcoxon Signed Rank test was used for dosimetric data. RESULTS Here we show that SMART dosimetric improvements in favor of delivered plans over predicted non-adapted plans for PBT, with improvements in proximal bronchial tree DMax of 5.7 Gy (p = 0.002) and gross tumor 100% prescription coverage of 7.3% (p = 0.002). The mean estimated follow-up is 17.2 months and 2-year local control and local failure free survival rates are 92.9% and 85.7%, respectively. There are no grade ≥ 3 toxicities. CONCLUSIONS SMART has dosimetric advantages and excellent clinical outcomes for ultracentral lung tumors. Daily plan adaptation reliably improves target coverage while simultaneously reducing doses to the proximal airways. These results further characterize the therapeutic window improvements for SMART. Structure-specific rigid dose accumulation dosimetric analysis provides insights that elucidate the dosimetric advantages of SMART more so than per fractional analysis alone.
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Affiliation(s)
- J M Bryant
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | - Ruben J Cruz-Chamorro
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alberic Gan
- University of South Florida Health Morsani College of Medicine, Tampa, FL, USA
| | - Casey Liveringhouse
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Joseph Weygand
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Ann Nguyen
- University of South Florida Health Morsani College of Medicine, Tampa, FL, USA
| | - Emily Keit
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Maria L Sandoval
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Austin J Sim
- Department of Radiation Oncology; James Cancer Hospital, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Bradford A Perez
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Thomas J Dilling
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Gage Redler
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Jacqueline Andreozzi
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Louis Nardella
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Arash O Naghavi
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Vladimir Feygelman
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Kujtim Latifi
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Stephen A Rosenberg
- Department of Radiation Oncology; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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Torchia J, Velec M. Deformable image registration for composite planned doses during adaptive radiation therapy. J Med Imaging Radiat Sci 2024; 55:82-90. [PMID: 38218679 DOI: 10.1016/j.jmir.2023.12.009] [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: 10/23/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 01/15/2024]
Abstract
INTRODUCTION Some patients have significant anatomic changes during radiotherapy, necessitating an adaptive repeat CT-simulation and re-planning. This yields two unique planning datasets that introduce uncertainty into total dose records. This study explored the impact of using deformable image registration (DIR) to spatially align repeat CT-simulation images and calculate total planned dose distributions. MATERIALS & METHODS Data from 5 head-and-neck, 5 lung, and 5 sarcoma patients who had unanticipated re-planning during radiotherapy were analyzed in a treatment planning system (RayStation v6.1 RaySearch Laboratories). Total planned doses to normal tissues were calculated using two methods and the previously generated manual contours defined on each CT. The first method, termed 'parameter addition', simply sums the relevant DVH metrics from the initial and re-planned distributions without spatially registering the CTs. The second, termed 'dose accumulation', uses a validated hybrid contour/intensity-based DIR algorithm to deform initial CT and dose distribution onto the repeat CT and re-planning dose distribution. DVH metrics from the summed distribution on the repeat CT are then calculated. Dose differences for organs-at-risk between parameter addition and dose accumulation ≥100 cGy were assumed to be clinically relevant. To elucidate whether relevant differences were due to registration accuracy or contouring variability between CTs, the analysis was repeated using contours on the first CT and the same contours deformed to the repeat CT with DIR. RESULTS For all patients, high overall DIR accuracy was verified visually (qualitatively) and numerically (quantitatively) using image similarity and contour-based metrics. All head-and-neck and lung patients, and one sarcoma patient (11 of 15 total) had dose differences between parameter addition and dose accumulation ≥100 cGy, with absolute mean differences of 160 cGy (range 101-436 cGy) seen in 41 of 205 total DVH criteria. In 22 of these 41 criteria, these differences were attributed to contouring variability between CTs. After correcting for contouring variations using DIR, the mean absolute differences in 7 of these 22 criteria with a relevant result (across 6 patients) was 146 cGy (range 100-502 cGy). In only 4 DVH criteria, the DIR mapped contours had higher variations than the original contours. One lung patient had a DVH criteria exceeding the clinical dose constraint by 125 cGy with parameter addition, and with accurate DIR and dose accumulation, the criteria was actually 97 cGy lower than the constraint. CONCLUSIONS The use of DIR to generate total planned dose records revealed substantial dose differences in most cases compared to commonly used clinical methods (i.e. parameter addition), and altered the planned acceptance criteria in a minority. DIR is recommended to be used for future adaptive re-plans to generate total planned dose records and facilitate accurate re-contouring. More accurate dose records may also improve our understanding of clinical outcomes.
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Affiliation(s)
- Joshua Torchia
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Michael Velec
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
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Nenoff L, Amstutz F, Murr M, Archibald-Heeren B, Fusella M, Hussein M, Lechner W, Zhang Y, Sharp G, Vasquez Osorio E. Review and recommendations on deformable image registration uncertainties for radiotherapy applications. Phys Med Biol 2023; 68:24TR01. [PMID: 37972540 PMCID: PMC10725576 DOI: 10.1088/1361-6560/ad0d8a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.
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Affiliation(s)
- Lena Nenoff
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, Dresden Germany
- Helmholtz-Zentrum Dresden—Rossendorf, Institute of Radiooncology—OncoRay, Dresden, Germany
| | - Florian Amstutz
- Department of Physics, ETH Zurich, Switzerland
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Mohammad Hussein
- Metrology for Medical Physics, National Physical Laboratory, Teddington, United Kingdom
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Greg Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
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Allen C, Yeo AU, Hardcastle N, Franich RD. Evaluating synthetic computed tomography images for adaptive radiotherapy decision making in head and neck cancer. Phys Imaging Radiat Oncol 2023; 27:100478. [PMID: 37655123 PMCID: PMC10465931 DOI: 10.1016/j.phro.2023.100478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 09/02/2023] Open
Abstract
Background and purpose Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes. This study evaluated a dosimetry-based clinical decision workflow for ART utilizing deformable registration of the original planning computed tomography (CT) image to the daily Cone Beam CT (CBCT) to replace the need for a replan CT for dose estimation. Materials and methods We used 12 retrospective Head & Neck patient cases having a ground truth - a replan CT (rCT) in response to anatomical changes apparent in the daily CBCT - to evaluate the accuracy of dosimetric assessment conducted on synthetic CTs (sCT) generated by deforming the original planning CT Hounsfield Units to the daily CBCT anatomy.The original plan was applied to the sCT and dosimetric accuracy of the sCT was assessed by analyzing plan objectives for targets and organs-at-risk compared to calculations on the ground-truth rCT. Three commercial DIR algorithms were compared. Results For the best-performing algorithms, the majority of dose metrics calculated on the sCTs differed by less than 4 Gy (5.7% of 70 Gy prescription dose). An uncertainty of ±2.5 Gy (3.6% of 70 Gy prescription) is recommended as a conservative tolerance when evaluating dose metrics on sCTs for head and neck. Conclusions Synthetic CTs present a valuable addition to the adaptive radiotherapy workflow, and synthetic CT dose estimates can be effectively used in addition to the current practice of visually inspecting the overlay of the planning CT and CBCT to assess the significance of anatomical change.
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Affiliation(s)
- Caitlin Allen
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Adam U. Yeo
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Centre for Medical Radiation Physics, University of Wollongong, NSW, Australia
| | - Rick D. Franich
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Science, RMIT University, Melbourne, Victoria, Australia
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Jassim H, Nedaei HA, Geraily G, Banaee N, Kazemian A. The geometric and dosimetric accuracy of kilovoltage cone beam computed tomography images for adaptive treatment: a systematic review. BJR Open 2023; 5:20220062. [PMID: 37389008 PMCID: PMC10301728 DOI: 10.1259/bjro.20220062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/24/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives To provide an overview and meta-analysis of different techniques adopted to accomplish kVCBCT for dose calculation and automated segmentation. Methods A systematic review and meta-analysis were performed on eligible studies demonstrating kVCBCT-based dose calculation and automated contouring of different tumor features. Meta-analysis of the performance was accomplished on the reported γ analysis and dice similarity coefficient (DSC) score of both collected results as three subgroups (head and neck, chest, and abdomen). Results After the literature scrutinization (n = 1008), 52 papers were recognized for the systematic review. Nine studies of dosimtric studies and eleven studies of geometric analysis were suitable for inclusion in meta-analysis. Using kVCBCT for treatment replanning depends on a method used. Deformable Image Registration (DIR) methods yielded small dosimetric error (≤2%), γ pass rate (≥90%) and DSC (≥0.8). Hounsfield Unit (HU) override and calibration curve-based methods also achieved satisfactory yielded small dosimetric error (≤2%) and γ pass rate ((≥90%), but they are prone to error due to their sensitivity to a vendor-specific variation in kVCBCT image quality. Conclusions Large cohorts of patients ought to be undertaken to validate methods achieving low levels of dosimetric and geometric errors. Quality guidelines should be established when reporting on kVCBCT, which include agreed metrics for reporting on the quality of corrected kVCBCT and defines protocols of new site-specific standardized imaging used when obtaining kVCBCT images for adaptive radiotherapy. Advances in knowledge This review gives useful knowledge about methods making kVCBCT feasible for kVCBCT-based adaptive radiotherapy, simplifying patient pathway and reducing concomitant imaging dose to the patient.
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Affiliation(s)
| | | | | | - Nooshin Banaee
- Medical Radiation Research Center, Islamic Azad University, Tehran, Iran
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Murr M, Brock KK, Fusella M, Hardcastle N, Hussein M, Jameson MG, Wahlstedt I, Yuen J, McClelland JR, Vasquez Osorio E. Applicability and usage of dose mapping/accumulation in radiotherapy. Radiother Oncol 2023; 182:109527. [PMID: 36773825 DOI: 10.1016/j.radonc.2023.109527] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/26/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023]
Abstract
Dose mapping/accumulation (DMA) is a topic in radiotherapy (RT) for years, but has not yet found its widespread way into clinical RT routine. During the ESTRO Physics workshop 2021 on "commissioning and quality assurance of deformable image registration (DIR) for current and future RT applications", we built a working group on DMA from which we present the results of our discussions in this article. Our aim in this manuscript is to shed light on the current situation of DMA in RT and to highlight the issues that hinder consciously integrating it into clinical RT routine. As a first outcome of our discussions, we present a scheme where representative RT use cases are positioned, considering expected anatomical variations and the impact of dose mapping uncertainties on patient safety, which we have named the DMA landscape (DMAL). This tool is useful for future reference when DMA applications get closer to clinical day-to-day use. Secondly, we discussed current challenges, lightly touching on first-order effects (related to the impact of DIR uncertainties in dose mapping), and focusing in detail on second-order effects often dismissed in the current literature (as resampling and interpolation, quality assurance considerations, and radiobiological issues). Finally, we developed recommendations, and guidelines for vendors and users. Our main point include: Strive for context-driven DIR (by considering their impact on clinical decisions/judgements) rather than perfect DIR; be conscious of the limitations of the implemented DIR algorithm; and consider when dose mapping (with properly quantified uncertainties) is a better alternative than no mapping.
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Affiliation(s)
- Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany.
| | - Kristy K Brock
- Department of Imaging Physics and Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, USA
| | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre & Sir Peter MacCallum Department of Oncology, University of Melbourne, Australia
| | - Mohammad Hussein
- Metrology for Medical Physics Centre, National Physical Laboratory, Teddington, United Kingdom
| | - Michael G Jameson
- GenesisCare New South Wales, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Australia
| | - Isak Wahlstedt
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, Bygning 101A, 2800 Kongens Lyngby, Denmark; Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark
| | - Johnson Yuen
- St George Hospital Cancer Care Centre, Kogarah, NSW 2217, Australia; South Western Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Jamie R McClelland
- Centre for Medical Image Computing and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Dept of Medical Physics and Biomedical Engineering, UCL, United Kingdom
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, M20 4BX Manchester, United Kingdom
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Robbins J, van Herk M, Eiben B, Green A, Vásquez Osorio E. Probabilistic evaluation of plan quality for time-dependent anatomical deformations in head and neck cancer patients. Phys Med 2023; 109:102579. [PMID: 37068428 DOI: 10.1016/j.ejmp.2023.102579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/14/2023] [Accepted: 04/05/2023] [Indexed: 04/19/2023] Open
Abstract
PURPOSE In addition to patient set-up uncertainties, anatomical deformations, e.g., weight loss, lead to time-dependent differences between the planned and delivered dose in a radiotherapy course that currently cannot easily be predicted. The aim of this study was to create time-varying prediction models to describe both the average and residual anatomical deformations. METHODS Weekly population-based principal component analysis models were generated from on-treatment cone-beam CT scans (CBCTs) of 30 head and neck cancer patients, with additional data of 35 patients used as a validation cohort. We simulated treatment courses accounting for a) anatomical deformations, b) set-up uncertainties and c) a combination of both. The dosimetric effects of the simulated deformations were compared to a direct dose accumulation based on deformable registration of the CBCT data. RESULTS Set-up uncertainties were seen to have a larger effect on the organ at risk (OAR) doses than anatomical deformations for all OARs except the larynx and the primary CTV. Distributions from simulation results were in good agreement with those of the accumulated dose. CONCLUSIONS We present a novel method of modelling time-varying organ deformations in head and neck cancer. The effect on the OAR doses from these deformations are smaller than the effect of set-up uncertainties for most OARs. These models can, for instance, be used to predict which patients could benefit from adaptive radiotherapy, prior to commencing treatment.
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Affiliation(s)
- Jennifer Robbins
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom.
| | - Marcel van Herk
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom
| | - Björn Eiben
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom; Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom
| | - Andrew Green
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom
| | - Eliana Vásquez Osorio
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom.
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Feasibility of delivered dose reconstruction for MR-guided SBRT of pancreatic tumors with fast, real-time 3D cine MRI. Radiother Oncol 2023; 182:109506. [PMID: 36736589 DOI: 10.1016/j.radonc.2023.109506] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND PURPOSE In MR-guided SBRT of pancreatic cancer, intrafraction motion is typically monitored with (interleaved) 2D cine MRI. However, tumor surroundings are often not fully captured in these images, and motion might be distorted by through-plane movement. In this study, the feasibility of highly accelerated 3D cine MRI to reconstruct the delivered dose during MR-guided SBRT was assessed. MATERIALS AND METHODS A 3D cine MRI sequence was developed for fast, time-resolved 4D imaging, featuring a low spatial resolution that allows for rapid volumetric imaging at 430 ms. The 3D cines were acquired during the entire beam-on time of 23 fractions of online adaptive MR-guided SBRT for pancreatic tumors on a 1.5 T MR-Linac. A 3D deformation vector field (DVF) was extracted for every cine dynamic using deformable image registration. Next, these DVFs were used to warp the partial dose delivered in the time interval between consecutive cine acquisitions. The warped dose plans were summed to obtain a total delivered dose. The delivered dose was also calculated under various motion correction strategies. Key DVH parameters of the GTV, duodenum, small bowel and stomach were extracted from the delivered dose and compared to the planned dose. The uncertainty of the calculated DVFs was determined with the inverse consistency error (ICE) in the high-dose regions. RESULTS The mean (SD) relative (ratio delivered/planned) D99% of the GTV was 0.94 (0.06), and the mean (SD) relative D0.5cc of the duodenum, small bowel, and stomach were respectively 0.98 (0.04), 1.00 (0.07), and 0.98 (0.06). In the fractions with the lowest delivered tumor coverage, it was found that significant lateral drifts had occurred. The DVFs used for dose warping had a low uncertainty with a mean (SD) ICE of 0.65 (0.07) mm. CONCLUSION We employed a fast, real-time 3D cine MRI sequence for dose reconstruction in the upper abdomen, and demonstrated that accurate DVFs, acquired directly from these images, can be used for dose warping. The reconstructed delivered dose showed only a modest degradation of tumor coverage, mostly attainable to baseline drifts. This emphasizes the need for motion monitoring and development of intrafraction treatment adaptation solutions, such as baseline drift corrections.
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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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10
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Wahlstedt I, George Smith A, Andersen CE, Behrens CP, Nørring Bekke S, Boye K, van Overeem Felter M, Josipovic M, Petersen J, Risumlund SL, Tascón-Vidarte JD, van Timmeren JE, Vogelius IR. Interfractional dose accumulation for MR-guided liver SBRT: Variation among algorithms is highly patient- and fraction-dependent. Radiother Oncol 2022; 182:109448. [PMID: 36566988 DOI: 10.1016/j.radonc.2022.109448] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/22/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND PURPOSE Daily plan adaptations could take the dose delivered in previous fractions into account. Due to high dose delivered per fraction, low number of fractions, steep dose gradients, and large interfractional organ deformations, this might be particularly important for liver SBRT. This study investigates inter-algorithm variation of interfractional dose accumulation for MR-guided liver SBRT. MATERIALS AND METHODS We assessed 27 consecutive MR-guided liver SBRT treatments of 67.5 Gy in three (n = 15) or 50 Gy in five fractions (n = 12), both prescribed to the GTV. We calculated fraction doses on daily patient anatomy, warped these doses to the simulation MRI using seven different algorithms, and accumulated the warped doses. Thus, we obtained differences in planned doses and warped or accumulated doses for each algorithm. This enabled us to calculate the inter-algorithm variations in warped doses per fraction and in accumulated doses per treatment course. RESULTS The four intensity-based algorithms were more consistent with planned PTV dose than affine or contour-based algorithms. The mean (range) variation of the dose difference for PTV D95% due to dose warping by these intensity-based algorithms was 10.4 percentage points (0.3 to 43.7) between fractions and 8.6 (0.3 to 24.9) between accumulated treatment doses. As seen by these ranges, the variation was very dependent on the patient and the fraction being analyzed. Nevertheless, no correlations between patient or plan characteristics on the one hand and inter-algorithm dose warping variation on the other hand was found. CONCLUSION Inter-algorithm dose accumulation variation is highly patient- and fraction-dependent for MR-guided liver SBRT. We advise against trusting a single algorithm for dose accumulation in liver SBRT.
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Affiliation(s)
- Isak Wahlstedt
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, Bygning 101A, 2800 Kongens Lyngby, Denmark; Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark.
| | - Abraham George Smith
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
| | - Claus Erik Andersen
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, Bygning 101A, 2800 Kongens Lyngby, Denmark
| | - Claus Preibisch Behrens
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, Bygning 101A, 2800 Kongens Lyngby, Denmark; Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark
| | - Susanne Nørring Bekke
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark
| | - Kristian Boye
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Mette van Overeem Felter
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark
| | - Mirjana Josipovic
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Jens Petersen
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
| | - Signe Lenora Risumlund
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - José David Tascón-Vidarte
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
| | | | - Ivan Richter Vogelius
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
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11
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Huesa-Berral C, Juan-Cruz C, van Kranen S, Rossi M, Belderbos J, Diego Azcona J, Burguete J, Sonke JJ. Detailed dosimetric evaluation of inter-fraction and respiratory motion in lung stereotactic body radiation therapy based on daily 4D cone beam CT images. Phys Med Biol 2022; 68. [PMID: 36538287 DOI: 10.1088/1361-6560/aca94d] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022]
Abstract
Objective. Periodic respiratory motion and inter-fraction variations are sources of geometric uncertainty in stereotactic body radiation therapy (SBRT) of pulmonary lesions. This study extensively evaluates and validates the separate and combined dosimetric effect of both factors using 4D-CT and daily 4D-cone beam CT (CBCT) dose accumulation scenarios.Approach. A first cohort of twenty early stage or metastatic disease lung cancer patients were retrospectively selected to evaluate each scenario. The planned-dose (3DRef) was optimized on a 3D mid-position CT. To estimate the dosimetric impact of respiratory motion (4DRef), inter-fractional variations (3DAcc) and the combined effect of both factors (4DAcc), three dose accumulation scenarios based on 4D-CT, daily mid-cone beam CT (CBCT) position and 4D-CBCT were implemented via CT-CT/CT-CBCT deformable image registration (DIR) techniques. Each scenario was compared to 3DRef.A separate cohort of ten lung SBRT patients was selected to validate DIR techniques. The distance discordance metric (DDM) was implemented per voxel and per patient for tumor and organs at risk (OARs), and the dosimetric impact for CT-CBCT DIR geometric errors was calculated.Main results.Median and interquartile range (IQR) of the dose difference per voxel were 0.05/2.69 Gy and -0.12/2.68 Gy for3DAcc-3DRefand4DAcc-3DRef.For4DRef-3DRefthe IQR was considerably smaller -0.15/0.78 Gy. These findings were confirmed by dose volume histogram parameters calculated in tumor and OARs. For CT-CT/CT-CBCT DIR validation, DDM (95th percentile) was highest for heart (6.26 mm)/spinal cord (8.00 mm), and below 3 mm for tumor and the rest of OARs. The dosimetric impact of CT-CBCT DIR errors was below 2 Gy for tumor and OARs.Significance. The dosimetric impact of inter-fraction variations were shown to dominate those of periodic respiration in SBRT for pulmonary lesions. Therefore, treatment evaluation and dose-effect studies would benefit more from dose accumulation focusing on day-to-day changes then those that focus on respiratory motion.
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Affiliation(s)
- Carlos Huesa-Berral
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.,Physics and Applied Mathematics, School of Science, University of Navarra, E-31008 Pamplona, Navarra, Spain.,Service of Radiation Physics and Radiation Protection, University of Navarra Clinic, E-31008 Pamplona, Navarra, Spain
| | - Celia Juan-Cruz
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Simon van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Maddalena Rossi
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - José Belderbos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Juan Diego Azcona
- Service of Radiation Physics and Radiation Protection, University of Navarra Clinic, E-31008 Pamplona, Navarra, Spain
| | - Javier Burguete
- Physics and Applied Mathematics, School of Science, University of Navarra, E-31008 Pamplona, Navarra, Spain
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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12
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Suitability of propagated contours for adaptive replanning for head and neck radiotherapy. Phys Med 2022; 102:66-72. [DOI: 10.1016/j.ejmp.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 08/19/2022] [Accepted: 09/12/2022] [Indexed: 11/20/2022] Open
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13
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Zhang Y, Alshaikhi J, Amos RA, Lowe M, Tan W, Bär E, Royle G. Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept. Radiother Oncol 2022; 173:93-101. [PMID: 35667573 DOI: 10.1016/j.radonc.2022.05.036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE To demonstrate predictive anatomical modelling for improving the clinical workflow of adaptive intensity-modulated proton therapy (IMPT) for head and neck cancer. METHODS 10 radiotherapy patients with nasopharyngeal cancer were included in this retrospective study. Each patient had a planning CT, weekly verification CTs during radiotherapy and predicted weekly CTs from our anatomical model. Predicted CTs were used to create predicted adaptive plans in advance with the aim of maintaining clinically acceptable dosimetry. Adaption was triggered when the increase in mean dose (Dmean) to the parotid glands exceeded 3 Gy(RBE). We compared the accumulated dose of two adaptive IMPT strategies: 1) Predicted plan adaption: One adaptive plan per patient was optimised on a predicted CT triggered by replan criteria. 2) Standard replan: One adaptive plan was created reactively in response to the triggering weekly CT. RESULTS Statistical analysis demonstrates that the accumulated dose differences between two adaptive strategies are not significant (p > 0.05) for CTVs and OARs. We observed no meaningful differences in D95 between the accumulated dose and the planned dose for the CTVs, with mean differences to the high-risk CTV of -1.20 %, -1.23 % and -1.25 % for no adaption, standard and predicted plan adaption, respectively. The accumulated parotid Dmean using predicted plan adaption is within 3 Gy(RBE) of the planned dose and 0.31 Gy(RBE) lower than the standard replan approach on average. CONCLUSION Prediction-based replanning could potentially enable adaptive therapy to be delivered without treatment gaps or sub-optimal fractions, as can occur during a standard replanning strategy, though the benefit of using predicted plan adaption over the standard replan was not shown to be statistically significant with respect to accumulated dose in this study. Nonetheless, a predictive replan approach can offer advantages in improving clinical workflow efficiency.
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Affiliation(s)
- Ying Zhang
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom.
| | - Jailan Alshaikhi
- Saudi Proton Therapy Center, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Richard A Amos
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
| | - Matthew Lowe
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Wenyong Tan
- Department of Oncology, Shenzhen Hospital of Southern Medical University, China
| | - Esther Bär
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; University College London Hospitals NHS Foundation Trust, United Kingdom
| | - Gary Royle
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
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14
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Lowther N, Louwe R, Yuen J, Hardcastle N, Yeo A, Jameson M. MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy. Phys Eng Sci Med 2022; 45:421-428. [PMID: 35522369 PMCID: PMC9239966 DOI: 10.1007/s13246-022-01125-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2022] [Indexed: 12/12/2022]
Abstract
The report of the American Association of Physicists in Medicine (AAPM) Task Group No. 132 published in 2017 reviewed rigid image registration and deformable image registration (DIR) approaches and solutions to provide recommendations for quality assurance and quality control of clinical image registration and fusion techniques in radiotherapy. However, that report did not include the use of DIR for advanced applications such as dose warping or warping of other matrices of interest. Considering that DIR warping tools are now readily available, discussions were hosted by the Medical Image Registration Special Interest Group (MIRSIG) of the Australasian College of Physical Scientists & Engineers in Medicine in 2018 to form a consensus on best practice guidelines. This position statement authored by MIRSIG endorses the recommendations of the report of AAPM task group 132 and expands on the best practice advice from the ‘Deforming to Best Practice’ MIRSIG publication to provide guidelines on the use of DIR for advanced applications.
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Affiliation(s)
- Nicholas Lowther
- Department of Radiation Oncology, Wellington Blood and Cancer Centre, Wellington, New Zealand
| | - Rob Louwe
- Holland Proton Therapy Centre, Delft, Netherlands
| | - Johnson Yuen
- St George Hospital Cancer Care Centre, Kogarah, New South Wales, 2217, Australia.,South Western Clinical School, University of New South Wales, Sydney, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia.,The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Adam Yeo
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,School of Applied Sciences, RMIT University, Melbourne, VIC, Australia
| | - Michael Jameson
- GenesisCare, Sydney, NSW, 2015, Australia. .,St Vincent's Clinical School, University of New South Wales, Sydney, Australia.
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15
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Argota-Perez R, Robbins J, Green A, Herk MV, Korreman S, Vásquez-Osorio E. Evaluating principal component analysis models for representing anatomical changes in head and neck radiotherapy. Phys Imaging Radiat Oncol 2022; 22:13-19. [PMID: 35493853 PMCID: PMC9038571 DOI: 10.1016/j.phro.2022.04.002] [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: 12/15/2021] [Revised: 04/08/2022] [Accepted: 04/08/2022] [Indexed: 11/19/2022] Open
Abstract
Background and purpose Anatomical changes during radiotherapy pose a challenge to robustness of plans. Principal component analysis (PCA) is commonly used to model such changes. We propose a toolbox to evaluate how closely a given PCA model can represent actual deformations seen in the patient and highlight regions where the model struggles to capture these changes. Materials and methods We propose to calculate a residual error map from the difference between an actual displacement vector field (DVF) and the closest DVF that the PCA model can produce. This was done by taking the inner product of the DVF with the PCA components from the model. As a global measure of error, the 90th percentile of the residual errors (Mres90) across the whole scan was used. As proof of principle, we demonstrated this approach on both patient-specific cases and a population-based PCA in head and neck (H&N) cancer patients. These models were created using deformation data from deformable registrations between the planning computed tomography and cone-beam computed tomography (CBCTs), and were evaluated against DVFs from registrations of CBCTs not used to create the model. Results For our example cases, the oropharyngeal and the nasal cavity regions showed the largest local residual error, indicating the PCA models struggle to predict deformations seen in these regions. Mres90 ranged from 0.4 mm to 6.3 mm across the different models. Conclusions A method to quantitatively evaluate how well PCA models represent observed anatomical changes was proposed. We demonstrated our approach on H&N PCA models, but it can be applied to other sites.
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Affiliation(s)
- Raul Argota-Perez
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
- Corresponding authors at: Department of Oncology, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, DK-8200 Aarhus N, Denmark (Raúl Argota-Pérez). Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Oncology, Aarhus University Hospital, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark (Stine Korreman).
| | - Jennifer Robbins
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom
| | - Andrew Green
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom
| | - Marcel van Herk
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom
| | - Stine Korreman
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Corresponding authors at: Department of Oncology, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, DK-8200 Aarhus N, Denmark (Raúl Argota-Pérez). Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Oncology, Aarhus University Hospital, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark (Stine Korreman).
| | - Eliana Vásquez-Osorio
- The University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom
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Scandurra D, Meijer T, Free J, van den Hoek J, Kelder L, Oldehinkel E, Steenbakkers R, Both S, Langendijk J. Evaluation of robustly optimised intensity modulated proton therapy for nasopharyngeal carcinoma. Radiother Oncol 2022; 168:221-228. [DOI: 10.1016/j.radonc.2022.01.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/31/2022] [Accepted: 01/31/2022] [Indexed: 02/08/2023]
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17
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Evaluation of the Dose Delivery Consistency and Its Dependence on Imaging Modality and Deformable Image Registration Algorithm in Prostate Cancer Patients. J Med Biol Eng 2021. [DOI: 10.1007/s40846-021-00673-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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18
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Paganetti H, Botas P, Sharp GC, Winey B. Adaptive proton therapy. Phys Med Biol 2021; 66:10.1088/1361-6560/ac344f. [PMID: 34710858 PMCID: PMC8628198 DOI: 10.1088/1361-6560/ac344f] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 10/28/2021] [Indexed: 12/25/2022]
Abstract
Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.
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Affiliation(s)
- Harald Paganetti
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Pablo Botas
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Foundation 29 of February, Pozuelo de Alarcón, Madrid, Spain
| | - Gregory C Sharp
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Brian Winey
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
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Chen W, Li Y, Yuan N, Qi J, Dyer BA, Sensoy L, Benedict SH, Shang L, Rao S, Rong Y. Clinical Enhancement in AI-Based Post-processed Fast-Scan Low-Dose CBCT for Head and Neck Adaptive Radiotherapy. Front Artif Intell 2021; 3:614384. [PMID: 33733226 PMCID: PMC7904899 DOI: 10.3389/frai.2020.614384] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/28/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose: To assess image quality and uncertainty in organ-at-risk segmentation on cone beam computed tomography (CBCT) enhanced by deep-learning convolutional neural network (DCNN) for head and neck cancer. Methods: An in-house DCNN was trained using forty post-operative head and neck cancer patients with their planning CT and first-fraction CBCT images. Additional fifteen patients with repeat simulation CT (rCT) and CBCT scan taken on the same day (oCBCT) were used for validation and clinical utility assessment. Enhanced CBCT (eCBCT) images were generated from the oCBCT using the in-house DCNN. Quantitative imaging quality improvement was evaluated using HU accuracy, signal-to-noise-ratio (SNR), and structural similarity index measure (SSIM). Organs-at-risk (OARs) were delineated on o/eCBCT and compared with manual structures on the same day rCT. Contour accuracy was assessed using dice similarity coefficient (DSC), Hausdorff distance (HD), and center of mass (COM) displacement. Qualitative assessment of users’ confidence in manual segmenting OARs was performed on both eCBCT and oCBCT by visual scoring. Results: eCBCT organs-at-risk had significant improvement on mean pixel values, SNR (p < 0.05), and SSIM (p < 0.05) compared to oCBCT images. Mean DSC of eCBCT-to-rCT (0.83 ± 0.06) was higher than oCBCT-to-rCT (0.70 ± 0.13). Improvement was observed for mean HD of eCBCT-to-rCT (0.42 ± 0.13 cm) vs. oCBCT-to-rCT (0.72 ± 0.25 cm). Mean COM was less for eCBCT-to-rCT (0.28 ± 0.19 cm) comparing to oCBCT-to-rCT (0.44 ± 0.22 cm). Visual scores showed OAR segmentation was more accessible on eCBCT than oCBCT images. Conclusion: DCNN improved fast-scan low-dose CBCT in terms of the HU accuracy, image contrast, and OAR delineation accuracy, presenting potential of eCBCT for adaptive radiotherapy.
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Affiliation(s)
- Wen Chen
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, China.,Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Yimin Li
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.,Department of Radiation Oncology, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Nimu Yuan
- Department of Biomedical Engineering, University of California, Davis, CA, United States
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA, United States
| | - Brandon A Dyer
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - Levent Sensoy
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Lu Shang
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Shyam Rao
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.,Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, United States
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20
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Dai X, Lei Y, Wang T, Dhabaan AH, McDonald M, Beitler JJ, Curran WJ, Zhou J, Liu T, Yang X. Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy. Phys Med Biol 2021; 66:045021. [PMID: 33412527 DOI: 10.1088/1361-6560/abd953] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.
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Affiliation(s)
- Xianjin Dai
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
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Lee D, Zhang P, Nadeem S, Alam S, Jiang J, Caringi A, Allgood N, Aristophanous M, Mechalakos J, Hu YC. Predictive dose accumulation for HN adaptive radiotherapy. Phys Med Biol 2020; 65:235011. [PMID: 33007769 DOI: 10.1088/1361-6560/abbdb8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1-3 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81 [Formula: see text] 0.07/0.81 [Formula: see text] 0.04 (week 4), 0.79 [Formula: see text] 0.06/0.81 [Formula: see text] 0.05 (week 5) and 0.78 [Formula: see text] 0.06/0.82 [Formula: see text] (week 6). The DICE with the Demons model were 0.78 [Formula: see text] 0.08/0.82 [Formula: see text] 0.03 (week 4), 0.77 [Formula: see text] 0.07/0.82 [Formula: see text] 0.04 (week 5) and 0.75 [Formula: see text] 0.07/0.82 [Formula: see text] 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center New York, NY, United States of America
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Glide-Hurst CK, Lee P, Yock AD, Olsen JR, Cao M, Siddiqui F, Parker W, Doemer A, Rong Y, Kishan AU, Benedict SH, Li XA, Erickson BA, Sohn JW, Xiao Y, Wuthrick E. Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology. Int J Radiat Oncol Biol Phys 2020; 109:1054-1075. [PMID: 33470210 DOI: 10.1016/j.ijrobp.2020.10.021] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 10/08/2020] [Accepted: 10/19/2020] [Indexed: 12/21/2022]
Abstract
The integration of adaptive radiation therapy (ART), or modifying the treatment plan during the treatment course, is becoming more widely available in clinical practice. ART offers strong potential for minimizing treatment-related toxicity while escalating or de-escalating target doses based on the dose to organs at risk. Yet, ART workflows add complexity into the radiation therapy planning and delivery process that may introduce additional uncertainties. This work sought to review presently available ART workflows and technological considerations such as image quality, deformable image registration, and dose accumulation. Quality assurance considerations for ART components and minimum recommendations are described. Personnel and workflow efficiency recommendations are provided, as is a summary of currently available clinical evidence supporting the implementation of ART. Finally, to guide future clinical trial protocols, an example ART physician directive and a physics template following standard NRG Oncology protocol is provided.
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Affiliation(s)
- Carri K Glide-Hurst
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin.
| | - Percy Lee
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Adam D Yock
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jeffrey R Olsen
- Department of Radiation Oncology, University of Colorado- Denver, Denver, Colorado
| | - Minsong Cao
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - William Parker
- Department of Radiation Oncology, McGill University, Montreal, Quebec, Canada
| | - Anthony Doemer
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - Yi Rong
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Amar U Kishan
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Beth A Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Evan Wuthrick
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
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Eiben B, Bertholet J, Menten MJ, Nill S, Oelfke U, McClelland JR. Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom. Phys Med Biol 2020; 65:165005. [PMID: 32235043 DOI: 10.1088/1361-6560/ab8533] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Breathing motion is challenging for radiotherapy planning and delivery. This requires advanced four-dimensional (4D) imaging and motion mitigation strategies and associated validation tools with known deformations. Numerical phantoms such as the XCAT provide reproducible and realistic data for simulation-based validation. However, the XCAT generates partially inconsistent and non-invertible deformations where tumours remain rigid and structures can move through each other. We address these limitations by post-processing the XCAT deformation vector fields (DVF) to generate a breathing phantom with realistic motion and quantifiable deformation. An open-source post-processing framework was developed that corrects and inverts the XCAT-DVFs while preserving sliding motion between organs. Those post-processed DVFs are used to warp the first XCAT-generated image to consecutive time points providing a 4D phantom with a tumour that moves consistently with the anatomy, the ability to scale lung density as well as consistent and invertible DVFs. For a regularly breathing case, the inverse consistency of the DVFs was verified and the tumour motion was compared to the original XCAT. The generated phantom and DVFs were used to validate a motion-including dose reconstruction (MIDR) method using isocenter shifts to emulate rigid motion. Differences between the reconstructed doses with and without lung density scaling were evaluated. The post-processing framework produced DVFs with a maximum [Formula: see text]-percentile inverse-consistency error of 0.02 mm. The generated phantom preserved the dominant sliding motion between the chest wall and inner organs. The tumour of the original XCAT phantom preserved its trajectory while deforming consistently with the underlying tissue. The MIDR was compared to the ground truth dose reconstruction illustrating its limitations. MIDR with and without lung density scaling resulted in small dose differences up to 1 Gy (prescription 54 Gy). The proposed open-source post-processing framework overcomes important limitations of the original XCAT phantom and makes it applicable to a wider range of validation applications within radiotherapy.
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Affiliation(s)
- Björn Eiben
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom of Great Britain and Northern Ireland
- Authors contributed equally
| | - Jenny Bertholet
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
- Authors contributed equally
| | - Martin J Menten
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom of Great Britain and Northern Ireland
| | - Simeon Nill
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
| | - Uwe Oelfke
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
| | - Jamie R McClelland
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom of Great Britain and Northern Ireland
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Giacometti V, Hounsell AR, McGarry CK. A review of dose calculation approaches with cone beam CT in photon and proton therapy. Phys Med 2020; 76:243-276. [DOI: 10.1016/j.ejmp.2020.06.017] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 06/04/2020] [Accepted: 06/22/2020] [Indexed: 01/12/2023] Open
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Hay LK, Paterson C, McLoone P, Miguel-Chumacero E, Valentine R, Currie S, Grose D, Schipani S, Wilson C, Nixon I, James A, Duffton A. Analysis of dose using CBCT and synthetic CT during head and neck radiotherapy: A single centre feasibility study. Tech Innov Patient Support Radiat Oncol 2020; 14:21-29. [PMID: 32226833 PMCID: PMC7093804 DOI: 10.1016/j.tipsro.2020.02.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/31/2020] [Accepted: 02/25/2020] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVES The study aimed to assess the suitability of deformable image registration (DIR) software to generate synthetic CT (sCT) scans for dose verification during radiotherapy to the head and neck. Planning and synthetic CT dose volume histograms were compared to evaluate dosimetric changes during the treatment course. METHODS Eligible patients had locally advanced (stage III, IVa and IVb) oropharyngeal cancer treated with primary radiotherapy. Weekly CBCT images were acquired post treatment at fractions 1, 6, 11, 16, 21 and 26 over a 30 fraction treatment course. Each CBCT was deformed with the planning CT to generate a sCT which was used to calculate the dose at that point in the treatment. A repeat planning CT2 was acquired at fraction 16 and deformed with the fraction 16 CBCT to compare differences between the calculations mid-treatment. RESULTS 20 patients were evaluated generating 138 synthetic CT sets. The single fraction mean dose to PTV_HR between the synthetic and planning CT did not vary, although dose to 95% of PTV_HR was smaller at week 6 compared to planning (difference 2.0%, 95% CI (0.8 to 3.1), p = 0.0). There was no statistically significant difference in PRV_brainstem or PRV_spinal cord maximum dose, although greater variation using the sCT calculations was reported. The mean dose to structures based on the fraction 16 sCT and CT2 scans were similar. CONCLUSIONS Synthetic CT provides comparable dose calculations to those of a repeat planning CT; however the limitations of DIR must be understood before it is applied within the clinical setting.
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Key Words
- ART, adaptive radiotherapy
- CBCT, Cone Beam Computed Tomography
- CTV, Clinical Target Volume
- Cone-beam CT
- DIR, deformable image registration
- DVH, dose volume histogram
- Deformable
- Dose
- GTV, Gross Tumour Volume
- Head and neck cancer
- IGRT, Image Guided Radiotherapy
- OAR, Organs at Risk
- OPSCC, oropharyngeal squamous cell cancer
- PRV, planning organ at risk volume
- PTV, Planning Target Volume
- RT, radiotherapy
- Radiotherapy
- SCC, Squamous Cell Carcinoma
- Synthetic CT
- TPS, treatment planning system
- VMAT, volumetric arc therapy
- pCT, planning Computed Tomography
- sCT, synthetic Computed Tomography
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Affiliation(s)
- Lisa K Hay
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Claire Paterson
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Philip McLoone
- Institute of Health & Wellbeing, University of Glasgow, University Ave, Glasgow G12 8QQ, United Kingdom
| | - Eliane Miguel-Chumacero
- Department of Radiotherapy Physics, Beatson West of Scotland Cancer Centre, 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Ronan Valentine
- Department of Radiotherapy Physics, Beatson West of Scotland Cancer Centre, 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Suzanne Currie
- Department of Radiotherapy Physics, Beatson West of Scotland Cancer Centre, 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Derek Grose
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Stefano Schipani
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Christina Wilson
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Ioanna Nixon
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Allan James
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
| | - Aileen Duffton
- Department of Clinical Oncology, Beatson West of Scotland Cancer Centre, Glasgow 1053 Great Western Road, Glasgow G12 0YN, United Kingdom
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26
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Ashida R, Nakamura M, Yoshimura M, Mizowaki T. Impact of interfractional anatomical variation and setup correction methods on interfractional dose variation in IMPT and VMAT plans for pancreatic cancer patients: A planning study. J Appl Clin Med Phys 2020; 21:49-59. [PMID: 32350969 PMCID: PMC7386168 DOI: 10.1002/acm2.12883] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 02/13/2020] [Accepted: 03/24/2020] [Indexed: 12/17/2022] Open
Abstract
Purpose To investigate the impact of interfractional anatomical changes and setup correction methods on dose distributions in pancreatic cancer patients under breath‐hold conditions. Methods Three intensity‐modulated proton therapy (IMPT) plans with different beam arrangements and one volumetric‐modulated arc therapy (VMAT) plan prescribing 54 Gy in 30 fractions were created for 10 patients who underwent three additional CT scans performed at an interval of 1–2 weeks. The additional CT sets were rigidly registered to the simulation CT set using both bone‐matching (BM) and organ‐matching (OM) methods in each patient. Recalculated dose distributions and dose–volume indices on the additional CT sets using either the BM or the OM method were compared with the simulation values. Results Differences in the gross tumor volume D98% value from the simulation sets ranged from −0.8 to −5.9% on average. In addition, the variations were larger with OM compared with BM for two IMPT plans. Meanwhile, differences in the D98% value in the region isotropically enlarged by 5 mm from the gross tumor volume were significantly improved with OM on two IMPT plans and the VMAT plan. Among the organs at risk, the dose–volume indices were significantly improved with OM only in the duodenum on all plans. Conclusion Organ‐matching may be a better setup correction technique than BM for both photon therapy and IMPT plans. However, in some beam arrangements of IMPT, the dose distribution may be somewhat worse using OM, due to interfractional anatomical variation. Therefore, it is important to choose beam angles that are less likely to be influenced by changes in the gastrointestinal gas volume, especially in IMPT plans.
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Affiliation(s)
- Ryo Ashida
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Michio Yoshimura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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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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28
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Lowther NJ, Marsh SH, Louwe RJW. Quantifying the dose accumulation uncertainty after deformable image registration in head-and-neck radiotherapy. Radiother Oncol 2020; 143:117-125. [PMID: 32063377 DOI: 10.1016/j.radonc.2019.12.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 12/12/2019] [Accepted: 12/15/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE Deformable image registration (DIR) facilitated dose reconstruction and accumulation can be applied to assess delivered dose and verify the validity of the treatment plan during treatment. This retrospective study used in silico deformations based on clinically observed anatomical changes as ground truth to investigate the uncertainty of reconstructed and accumulated dose in head-and-neck radiotherapy (HNRT). MATERIALS AND METHODS A planning CT (pCT), cone beam CT (CBCT) from week one of treatment and three later CBCTs were selected for 12 HNRT patients. These images were used to generate in silico reference CBCTs and deformation vector fields (DVFs) as ground truth with B-spline DIR. Inverse consistency (IC) of voxels was assessed by determining their net displacement after successive application of the forward and backward DVF. The reconstructed dose based on demons DIR was compared to the ground truth to assess the structure-specific uncertainties of this DIR algorithm for inverse consistent and inverse inconsistent voxels. RESULTS Overall, 98.5% of voxels were inverse consistent with the 95% level of confidence range for dose reconstruction of a single fraction equal to [-2.3%; +2.1%], [-10.2%; +15.2%] and [-9.5%; +12.5%] relative to their planned dose for target structures, critical organs at risk (OARs) and non-critical OARs, respectively. Inverse inconsistent voxels generally showed a higher level of uncertainty. CONCLUSION The uncertainty in accumulated dose using DIR can be accurately quantified and incorporated in dose-volume histograms (DVHs). This method can be used to prospectively assess the adequacy of target coverage during treatment in an objective manner.
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Affiliation(s)
- Nicholas J Lowther
- Wellington Blood and Cancer Centre, Department of Radiation Oncology, Wellington, New Zealand; University of Canterbury, School of Physical and Chemical Sciences, Christchurch, New Zealand
| | - Steven H Marsh
- University of Canterbury, School of Physical and Chemical Sciences, Christchurch, New Zealand
| | - Robert J W Louwe
- Wellington Blood and Cancer Centre, Department of Radiation Oncology, Wellington, New Zealand.
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Optimizing planning CT using past CT images for prostate cancer volumetric modulated arc therapy. Med Dosim 2020; 45:213-218. [PMID: 32008885 DOI: 10.1016/j.meddos.2019.12.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/30/2019] [Accepted: 12/11/2019] [Indexed: 11/20/2022]
Abstract
This study aimed to evaluate a new method to optimize planning computed tomography (CT) using three-dimensional (3D) displacement error between the planning and diagnosed past CT scans. Thirty-two patients undergoing volumetric modulated arc therapy for prostate cancer were evaluated for a 3D displacement error between bone- and prostate-matching spatial coordinates using multiple acquisition planning CT (MPCT) scans. Each MPCT image and a past CT image were used to perform rigid image registration (RIR) and deformable image registration (DIR), and the 3D displacement error was calculated. Correlations of the 3D displacement error in each MPCT scan and between the MPCT and past CT were evaluated based on RIR and DIR, respectively. The 3D displacement error in the MPCT images exhibited moderate correlation with the 3D displacement error between MPCT and past CT for both RIR (adjusted r2 = 0.495) and DIR (adjusted r2 = 0.398). In the correlation analysis between MPCT and past CT, image pairs with 3D displacement errors ≥ 6 mm were significantly different from those with errors < 6 mm (p < 0.0001). Past CT images were different from the planning CT images, which can be attributed to setup tools, flat-top plates, and physical differences due to the presence or absence of urine as well as prescription effects. The relationship between bone and prostate exhibited small deviations between the planning and past CT regardless of pretreatment. The prostate, which only has a slight effect on the displacement between it and bladder volume, was covered with a stiff pelvic bone. As a result, MPCT images exhibited correlations with past CT images of various difference states such as body positions. Finally, large 3D displacement errors in prostate position were caused by pelvic tension and stress, which can be detected using diagnosed past CT images instead of requiring MPCT scans. By comparing past and planning CT images, the random displacement error in the planning CT scan can be avoided by evaluating 3D displacement errors. The new method using the past CT images can estimate the displacement error of the prostate during the treatment period with 1 plan CT scan only, and it helps improve the treatment accuracy.
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30
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Yuan Z, Rong Y, Benedict SH, Daly ME, Qiu J, Yamamoto T. "Dose of the day" based on cone beam computed tomography and deformable image registration for lung cancer radiotherapy. J Appl Clin Med Phys 2019; 21:88-94. [PMID: 31816170 PMCID: PMC6964750 DOI: 10.1002/acm2.12793] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 02/04/2019] [Accepted: 11/17/2019] [Indexed: 12/25/2022] Open
Abstract
Purpose Adaptive radiotherapy (ART) has potential to reduce toxicity and facilitate safe dose escalation. Dose calculations with the planning CT deformed to cone beam CT (CBCT) have shown promise for estimating the “dose of the day”. The purpose of this study is to investigate the “dose of the day” calculation accuracy based on CBCT and deformable image registration (DIR) for lung cancer radiotherapy. Methods A total of 12 lung cancer patients were identified, for which daily CBCT imaging was performed for treatment positioning. A re‐planning CT (rCT) was acquired after 20 Gy for all patients. A virtual CT (vCT) was created by deforming initial planning CT (pCT) to the simulated CBCT that was generated from deforming CBCT to rCT acquired on the same day. Treatment beams from the initial plan were copied to the vCT and rCT for dose calculation. Dosimetric agreement between vCT‐based and rCT‐based accumulated doses was evaluated using the Bland‐Altman analysis. Results Mean differences in dose‐volume metrics between vCT and rCT were smaller than 1.5%, and most discrepancies fell within the range of ± 5% for the target volume, lung, esophagus, and heart. For spinal cord Dmax, a large mean difference of −5.55% was observed, which was largely attributed to very limited CBCT image quality (e.g., truncation artifacts). Conclusion This study demonstrated a reasonable agreement in dose‐volume metrics between dose accumulation based on vCT and rCT, with the exception for cases with poor CBCT image quality. These findings suggest potential utility of vCT for providing a reasonable estimate of the “dose of the day”, and thus facilitating the process of ART for lung cancer.
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Affiliation(s)
- Zilong Yuan
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA.,Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Megan E Daly
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Jianfeng Qiu
- Medical Engineering and Technology Research Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
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Guerreiro F, Zachiu C, Seravalli E, Ribeiro CO, Janssens GO, Ries M, de Senneville BD, Maduro JH, Brouwer CL, Korevaar EW, Knopf AC, Raaymakers BW. Evaluating the benefit of PBS vs. VMAT dose distributions in terms of dosimetric sparing and robustness against inter-fraction anatomical changes for pediatric abdominal tumors. Radiother Oncol 2019; 138:158-165. [DOI: 10.1016/j.radonc.2019.06.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/27/2019] [Accepted: 06/17/2019] [Indexed: 11/16/2022]
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Astaburuaga R, Gabryś HS, Sánchez-Nieto B, Floca RO, Klüter S, Schubert K, Hauswald H, Bangert M. Incorporation of Dosimetric Gradients and Parotid Gland Migration Into Xerostomia Prediction. Front Oncol 2019; 9:697. [PMID: 31417872 PMCID: PMC6684756 DOI: 10.3389/fonc.2019.00697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/15/2019] [Indexed: 12/23/2022] Open
Abstract
Purpose: Due to the sharp gradients of intensity-modulated radiotherapy (IMRT) dose distributions, treatment uncertainties may induce substantial deviations from the planned dose during irradiation. Here, we investigate if the planned mean dose to parotid glands in combination with the dose gradient and information about anatomical changes during the treatment improves xerostomia prediction in head and neck cancer patients. Materials and methods: Eighty eight patients were retrospectively analyzed. Three features of the contralateral parotid gland were studied in terms of their association with the outcome, i.e., grade ≥ 2 (G2) xerostomia between 6 months and 2 years after radiotherapy (RT): planned mean dose (MD), average lateral dose gradient (GRADX), and parotid gland migration toward medial (PGM). PGM was estimated using daily megavoltage computed tomography (MVCT) images. Three logistic regression models where analyzed: based on (1) MD only, (2) MD and GRADX, and (3) MD, GRADX, and PGM. Additionally, the cohort was stratified based on the median value of GRADX, and a univariate analysis was performed to study the association of the MD with the outcome for patients in low- and high-GRADX domains. Results: The planned MD failed to recognize G2 xerostomia patients (AUC = 0.57). By adding the information of GRADX (second model), the model performance increased to AUC = 0.72. The addition of PGM (third model) led to further improvement in the recognition of the outcome (AUC = 0.79). Remarkably, xerostomia patients in the low-GRADX domain were successfully identified (AUC = 0.88) by the MD alone. Conclusions: Our results indicate that GRADX and PGM, which together serve as a proxy of dosimetric changes, provide valuable information for xerostomia prediction.
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Affiliation(s)
- Rosario Astaburuaga
- Department of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Medical Faculty of Heidelberg, Universität Heidelberg, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Institute of Physics, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Hubert S Gabryś
- Department of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Medical Faculty of Heidelberg, Universität Heidelberg, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology, Heidelberg, Germany
| | | | - Ralf O Floca
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Medical Image Computing, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Sebastian Klüter
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Kai Schubert
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Henrik Hauswald
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany.,Clinical Cooperation Unit Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany
| | - Mark Bangert
- Department of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology, Heidelberg, Germany
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Sokooti H, Saygili G, Glocker B, Lelieveldt BPF, Staring M. Quantitative error prediction of medical image registration using regression forests. Med Image Anal 2019; 56:110-121. [PMID: 31226661 DOI: 10.1016/j.media.2019.05.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 04/25/2019] [Accepted: 05/10/2019] [Indexed: 11/17/2022]
Abstract
Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. The task of predicting registration error is demanding due to the lack of a ground truth in medical images. This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans. A random regression forest is utilized to predict the registration error locally. The forest is built with features related to the transformation model and features related to the dissimilarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans in two experiments: SPREAD (trained and tested on SPREAD) and inter-database (including three databases SPREAD, DIR-Lab-4DCT and DIR-Lab-COPDgene). The results show that the mean absolute errors of regression are 1.07 ± 1.86 and 1.76 ± 2.59 mm for the SPREAD and inter-database experiment, respectively. The overall accuracy of classification in three classes (correct, poor and wrong registration) is 90.7% and 75.4%, for SPREAD and inter-database respectively. The good performance of the proposed method enables important applications such as automatic quality control in large-scale image analysis.
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Affiliation(s)
- Hessam Sokooti
- Leiden University Medical Center, Leiden, the Netherlands.
| | - Gorkem Saygili
- Leiden University Medical Center, Leiden, the Netherlands
| | | | - Boudewijn P F Lelieveldt
- Leiden University Medical Center, Leiden, the Netherlands; Delft University of Technology, Delft, the Netherlands
| | - Marius Staring
- Leiden University Medical Center, Leiden, the Netherlands; Delft University of Technology, Delft, the Netherlands
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Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. Br J Radiol 2019; 92:20190001. [PMID: 31112393 PMCID: PMC6724618 DOI: 10.1259/bjr.20190001] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning interest in machine learning comes the significant risk of misaligned expectations as to what it can and cannot accomplish. This paper evaluates the role of machine learning and the problems it solves within the context of current clinical challenges in radiation oncology. The role of learning algorithms within the workflow for external beam radiation therapy are surveyed, considering simulation imaging, multimodal fusion, image segmentation, treatment planning, quality assurance, and treatment delivery and adaptation. For each aspect, the clinical challenges faced, the learning algorithms proposed, and the successes and limitations of various approaches are analyzed. It is observed that machine learning has largely thrived on reproducibly mimicking conventional human-driven solutions with more efficiency and consistency. On the other hand, since algorithms are generally trained using expert opinion as ground truth, machine learning is of limited utility where problems or ground truths are not well-defined, or if suitable measures of correctness are not available. As a result, machines may excel at replicating, automating and standardizing human behaviour on manual chores, meanwhile the conceptual clinical challenges relating to definition, evaluation, and judgement remain in the realm of human intelligence and insight.
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Affiliation(s)
- Daniel Jarrett
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK.,2 Mirada Medical Ltd, Oxford, UK
| | - Eleanor Stride
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Katherine Vallis
- 3 Department of Oncology, Oxford Institute for Radiation Oncology, University of Oxford, UK
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Rigaud B, Simon A, Castelli J, Lafond C, Acosta O, Haigron P, Cazoulat G, de Crevoisier R. Deformable image registration for radiation therapy: principle, methods, applications and evaluation. Acta Oncol 2019; 58:1225-1237. [PMID: 31155990 DOI: 10.1080/0284186x.2019.1620331] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.
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Affiliation(s)
- Bastien Rigaud
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Antoine Simon
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Joël Castelli
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Caroline Lafond
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Oscar Acosta
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Pascal Haigron
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center , Houston , TX , USA
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Abstract
As deformable image registration makes its way into the clinical routine, the summation of doses from fractionated treatment regimens to evaluate cumulative doses to targets and healthy tissues is also becoming a frequently utilized tool in the context of image-guided adaptive radiotherapy. Accounting for daily geometric changes using deformable image registration and dose accumulation potentially enables a better understanding of dose-volume-effect relationships, with the goal of translation of this knowledge to personalization of treatment, to further enhance treatment outcomes. Treatment adaptation involving image deformation requires patient-specific quality assurance of the image registration and dose accumulation processes, to ensure that uncertainties in the 3D dose distributions are identified and appreciated from a clinical relevance perspective. While much research has been devoted to identifying and managing the uncertainties associated with deformable image registration and dose accumulation approaches, there are still many unanswered questions. Here, we provide a review of current deformable image registration and dose accumulation techniques, and related clinical application. We also discuss salient issues that need to be deliberated when applying deformable algorithms for dose mapping and accumulation in the context of adaptive radiotherapy and response assessment.
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Yang J, Zhang Y, Zhang Z, Zhang L, Balter P, Court L. Technical Note: Density correction to improve CT number mapping in thoracic deformable image registration. Med Phys 2019; 46:2330-2336. [PMID: 30896047 DOI: 10.1002/mp.13502] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 03/11/2019] [Accepted: 03/11/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE To improve the accuracy of computed tomography (CT) number mapping inside the lung in deformable image registration with large differences in lung volume for applications in vertical CT imaging and adaptive radiotherapy. METHODS The deep inspiration breath hold (DIBH) CT image and the end of exhalation (EE) phase image in four-dimensional CT of 14 thoracic cancer patients were used in this study. Lung volumes were manually delineated. A Demons-based deformable registration was first applied to register the EE CT to the DIBH CT for each patient, and the resulting deformation vector field deformed the EE CT image to the DIBH CT space. Given that the mass of the lung remains the same during respiration, we created a mass-preserving model to correlate lung density variations with volumetric changes, which were characterized by the Jacobian derived from the deformation field. The Jacobian determinant was used to correct the lung CT numbers transferred from the EE CT image. The absolute intensity differences created by subtracting the deformed EE CT from the DIBH CT with and without density correction were compared. RESULTS The ratio of DIBH CT to EE CT lung volumes was 1.6 on average. The deformable registration registered the lung shape well, but the appearance of voxel intensities inside the lung was different, demonstrating the need for density correction. Without density correction, the mean and standard deviation of the absolute intensity difference between the deformed EE CT and the DIBH CT inside the lung were 54.5 ± 45.5 for all cases. After density correction, these numbers decreased to 18.1 ± 34.9, demonstrating greater accuracy. The cumulative histogram of the intensity difference also showed that density correction improved CT number mapping greatly. CONCLUSION Density correction improves CT number mapping inside the lung in deformable image registration for difficult cases with large lung volume differences.
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Affiliation(s)
- Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yongbin Zhang
- Proton Therapy Center, University of Cincinnati Medical Center, Liberty Township, OH, USA
| | - Zijian Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Botas P, Kim J, Winey B, Paganetti H. Online adaption approaches for intensity modulated proton therapy for head and neck patients based on cone beam CTs and Monte Carlo simulations. ACTA ACUST UNITED AC 2018; 64:015004. [DOI: 10.1088/1361-6560/aaf30b] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Qin A, Ionascu D, Liang J, Han X, O’Connell N, Yan D. The evaluation of a hybrid biomechanical deformable registration method on a multistage physical phantom with reproducible deformation. Radiat Oncol 2018; 13:240. [PMID: 30514348 PMCID: PMC6280462 DOI: 10.1186/s13014-018-1192-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 11/23/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Advanced clinical applications, such as dose accumulation and adaptive radiation therapy, require deformable image registration (DIR) algorithms capable of voxel-wise accurate mapping of treatment dose or functional imaging. By utilizing a multistage deformable phantom, the authors investigated scenarios where biomechanical refinement method (BM-DIR) may be better than the pure image intensity based deformable registration (IM-DIR). METHODS The authors developed a biomechanical-model based DIR refinement method (BM-DIR) to refine the deformable vector field (DVF) from any initial intensity-based DIR (IM-DIR). The BM-DIR method was quantitatively evaluated on a novel phantom capable of ten reproducible gradually-increasing deformation stages using the urethra tube as a surrogate. The internal DIR accuracy was inspected in term of the Dice similarity coefficient (DSC), Hausdorff and mean surface distance as defined in of the urethra structure inside the phantom and compared with that of the initial IM-DIR under various stages of deformation. Voxel-wise deformation vector discrepancy and Jacobian regularity were also inspected to evaluate the output DVFs. In addition to phantom, two pairs of Head&Neck patient MR images with expert-defined landmarks inside parotids were utilized to evaluate the BM-DIR accuracy with target registration error (TRE). RESULTS The DSC and surface distance measures of the inner urethra tube indicated the BM-DIR method can improve the internal DVF accuracy on masked MR images for the phases of a large degree of deformation. The smoother Jacobian distribution from the BM-DIR suggests more physically-plausible internal deformation. For H&N cancer patients, the BM-DIR improved the TRE from 0.339 cm to 0.210 cm for the landmarks inside parotid on the masked MR images. CONCLUSIONS We have quantitatively demonstrated on a multi-stage physical phantom and limited patient data that the proposed BM-DIR can improve the accuracy inside solid organs with large deformation where distinctive image features are absent.
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Affiliation(s)
- An Qin
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI USA
| | - Dan Ionascu
- Department of Radiation Oncology, College of Medicine, University of Cincinnati, Cincinnati, OH USA
| | - Jian Liang
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI USA
| | - Xiao Han
- Elekta Inc., Maryland Heights, MO USA
| | | | - Di Yan
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI USA
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40
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Paganelli C, Meschini G, Molinelli S, Riboldi M, Baroni G. “Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats”. Med Phys 2018; 45:e908-e922. [DOI: 10.1002/mp.13162] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 07/30/2018] [Accepted: 08/24/2018] [Indexed: 12/26/2022] Open
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | - Giorgia Meschini
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | | | - Marco Riboldi
- Department of Medical Physics; Ludwig-Maximilians-Universitat Munchen; Munich 80539 Germany
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
- Centro Nazionale di Adroterapia Oncologica; Pavia 27100 Italy
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Vickress JR, Battista J, Barnett R, Yartsev S. Online daily assessment of dose change in head and neck radiotherapy without dose-recalculation. J Appl Clin Med Phys 2018; 19:659-665. [PMID: 30084159 PMCID: PMC6123138 DOI: 10.1002/acm2.12432] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 06/21/2018] [Accepted: 07/17/2018] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Head and neck cancers are commonly treated with radiation therapy, but due to possible volume changes, plan adaptation may be required during the course of treatment. Currently, plan adaptations consume significant clinical resources. Existing methods to evaluate the need for plan adaptation requires deformable image registration (DIR) to a new CT simulation or daily cone beam CT (CBCT) images and the recalculation of the dose distribution. In this study, we explore a tool to assist the decision for plan adaptation using a CBCT without re-computation of dose, allowing for rapid online assessment. METHODS This study involved 18 head and neck cancer patients treated with CBCT image guidance who had their treatment plan modified based on a new CT simulation (ReCT). Dose changes were estimated using different methods and compared to the current gold standard of using DIR between the planning CT scan (PCT) and ReCT with recomputed dose. The first and second methods used DIR between the PCT and daily CBCT with the planned dose or recalculated dose from the ReCT respectively, with the dose transferred to the CBCT using rigid registration. The necessity of plan adaptation was assessed by the change in dose to 95% of the planning target volume (D95) and mean dose to the parotids. RESULTS The treatment plans were adapted clinically for all 18 patients but only 7 actually needed an adaptation yielding 11 unnecessary adaptations. Applying a method using the daily CBCT with the planned dose distribution would have yielded only four unnecessary adaptations and no missed adaptations: a significant improvement from that done clinically. CONCLUSION Using the DIR between the planning CT and daily CBCT can flag cases for plan adaptation before every fraction while not requiring a new re-planning CT scan and dose recalculation.
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Affiliation(s)
| | - Jerry Battista
- Department of Medical BiophysicsWestern UniversityLondonONCanada
- Department of OncologyWestern UniversityLondonONCanada
- London Regional Cancer ProgramLondon Health Sciences CentreLondonONCanada
| | - Rob Barnett
- Department of Medical BiophysicsWestern UniversityLondonONCanada
- Department of OncologyWestern UniversityLondonONCanada
- London Regional Cancer ProgramLondon Health Sciences CentreLondonONCanada
| | - Slav Yartsev
- Department of Medical BiophysicsWestern UniversityLondonONCanada
- Department of OncologyWestern UniversityLondonONCanada
- London Regional Cancer ProgramLondon Health Sciences CentreLondonONCanada
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42
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Brivio D, Hu YD, Margalit DN, Zygmanski P. Selection of head and neck cancer patients for adaptive replanning of radiation treatment using kV-CBCT. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aad546] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Siciarz P, Mccurdy B, Alshafa F, Greer P, Hatton J, Wright P. Evaluation of CT to CBCT non-linear dense anatomical block matching registration for prostate patients. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aacada] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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44
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Takemura A, Nagano A, Kojima H, Ikeda T, Yokoyama N, Tsukamoto K, Noto K, Isomura N, Ueda S, Kawashima H. An uncertainty metric to evaluate deformation vector fields for dose accumulation in radiotherapy. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2018; 6:77-82. [PMID: 33458393 PMCID: PMC7807581 DOI: 10.1016/j.phro.2018.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 05/14/2018] [Accepted: 05/23/2018] [Indexed: 02/08/2023]
Abstract
Background and purpose In adaptive radiotherapy, deformable image registration (DIR) is used to propagate delineations of tumors and organs into a new therapy plan and to calculate the accumulated total dose. Many DIR accuracy metrics have been proposed. An alternative proposed here could be a local uncertainty (LU) metric for DIR results. Materials and methods The LU represented the uncertainty of each DIR position and was focused on deformation evaluation in uniformly-dense regions. Four cases demonstrated LU calculations: two head and neck cancer cases, a lung cancer case, and a prostate cancer case. Each underwent two CT examinations for radiotherapy planning. Results LU maps were calculated from each DIR of the clinical cases. Reduced fat regions had LUs of 4.6 ± 0.9 mm, 4.8 ± 1.0 mm, and 4.5 ± 0.7 mm, while the shrunken left parotid gland had a LU of 4.1 ± 0.8 mm and the shrunken lung tumor had a LU of 3.7 ± 0.7 mm. The bowels in the pelvic region had a LU of 10.2 ± 3.7 mm. LU histograms for the cases were similar and 99% of the voxels had a LU < 3 mm. Conclusions LU is a new uncertainty metric for DIR that was demonstrated for clinical cases. It had a tolerance of <3 mm.
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Affiliation(s)
- Akihiro Takemura
- Faculty of Health Sciences, Institution of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan
| | - Akira Nagano
- Division of Radiology, Okayama University Hospital, 2-5-1 Shikatacho, Kitaku, Okayama 700-8558, Japan
| | - Hironori Kojima
- Department of Radiological Technology, Kanazawa University Hospital, 13-1 Takaramachi, Kanazawa 920-8641, Japan.,Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan
| | - Tomohiro Ikeda
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-115 Yatsuyamada, Koriyama-City, Fukushima-Pref. 963-8563, Japan
| | - Noriomi Yokoyama
- Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan
| | - Kosuke Tsukamoto
- Department of Radiological Technology, Kanazawa University Hospital, 13-1 Takaramachi, Kanazawa 920-8641, Japan
| | - Kimiya Noto
- Department of Radiological Technology, Kanazawa University Hospital, 13-1 Takaramachi, Kanazawa 920-8641, Japan
| | - Naoki Isomura
- Department of Radiological Technology, Kanazawa University Hospital, 13-1 Takaramachi, Kanazawa 920-8641, Japan
| | - Shinichi Ueda
- Department of Radiological Technology, Kanazawa University Hospital, 13-1 Takaramachi, Kanazawa 920-8641, Japan
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institution of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan
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Miyakawa S, Tachibana H, Moriya S, Kurosawa T, Nishio T, Sato M. Design and development of a nonrigid phantom for the quantitative evaluation of DIR-based mapping of simulated pulmonary ventilation. Med Phys 2018; 45:3496-3505. [PMID: 29807393 DOI: 10.1002/mp.13017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 05/16/2018] [Accepted: 05/16/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE The validation of deformable image registration (DIR)-based pulmonary ventilation mapping is time consuming and prone to inaccuracies and is also affected by deformation parameters. In this study, we developed a nonrigid phantom as a quality assurance (QA) tool that simulates ventilation to evaluate DIR-based images quantitatively. METHODS The phantom consists of an acrylic cylinder filled with polyurethane foam designed to simulate pulmonic alveoli. A polyurethane membrane is attached to the inferior end of the phantom to simulate the diaphragm. In addition, tracheobronchial-tree-shaped polyurethane tubes are inserted through the foam and converge outside the phantom to simulate the trachea. Solid polyurethane is also used to model arteries, which closely follow the model airways. Two three-dimensional (3D) CT scans were performed during exhalation and inhalation phases using xenon (Xe) gas as the inhaled contrast agent. The exhalation 3D-CT image is deformed to an inhalation 3D-CT image using our in-house program based on the NiftyReg open-source package. The target registration error (TRE) between the two images was calculated for 16 landmarks located in the simulated lung volume. The DIR-based ventilation image was generated using Jacobian determinant (JD) metrics. Subsequently, differences in the Hounsfield unit (HU) values between the two images were measured. The correlation coefficient between the JD and HU differences was calculated. In addition, three 4D-CT scans are performed to evaluate the reproducibility of the phantom motion and Xe gas distribution. RESULTS The phantom exhibited a variety of displacements for each landmark (range: 1-20 mm). The reproducibility analysis indicated that the location differences were <1 mm for all landmarks, and the HU variation in the Xe gas distribution was close to zero. The mean TRE in the evaluation of spatial accuracy according to the DIR software was 1.47 ± 0.71 mm (maximum: 2.6 mm). The relationship between the JD and HU differences had a large correlation (R = -0.71) for the DIR software. CONCLUSION The phantom implemented new features, namely, deformation and simulated ventilation. To assess the accuracy of the DIR-based mapping of the simulated pulmonary ventilation, the phantom allows for simulation of Xe gas wash-in and wash-out. The phantom may be an effective QA tool, because the DIR algorithm can be quickly changed and its accuracy evaluated with a high degree of precision.
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Affiliation(s)
- Shin Miyakawa
- Department of Medical Physics, Graduate School of Medicine, Tokyo Women's Medical University, Tokyo, 162-8666, Japan
- Radiological Sciences, Graduate Division of Health Sciences, Komazawa University, Tokyo, 154-8525, Japan
| | - Hidenobu Tachibana
- Particle Therapy Division, Research Center for Innovative Oncology, National Cancer Center, Chiba, 277-8577, Japan
| | - Shunsuke Moriya
- Doctoral Program in Biomedical Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Chiba, 305-8577, Japan
| | - Tomoyuki Kurosawa
- Department of Medical Physics, Graduate School of Medicine, Tokyo Women's Medical University, Tokyo, 162-8666, Japan
- Radiological Sciences, Graduate Division of Health Sciences, Komazawa University, Tokyo, 154-8525, Japan
| | - Teiji Nishio
- Department of Medical Physics, Graduate School of Medicine, Tokyo Women's Medical University, Tokyo, 162-8666, Japan
| | - Masanori Sato
- Radiological Sciences, Graduate Division of Health Sciences, Komazawa University, Tokyo, 154-8525, Japan
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Loi G, Fusella M, Lanzi E, Cagni E, Garibaldi C, Iacoviello G, Lucio F, Menghi E, Miceli R, Orlandini LC, Roggio A, Rosica F, Stasi M, Strigari L, Strolin S, Fiandra C. Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study. Med Phys 2018; 45:748-757. [PMID: 29266262 DOI: 10.1002/mp.12737] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 10/04/2017] [Accepted: 12/01/2017] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms. METHODS AND MATERIALS Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets. Head-and-neck (HN), thorax, and pelvis sites were included. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Statistical inference on validation results was carried out in order to identify the prognostic factors of DIR performances. RESULTS DVF intensity, anatomic site and participating center were significant prognostic factors of DIR performances. Sub-voxel accuracy was obtained in the HN by all algorithms. Large errors, with MDC ranging up to 6 mm, were observed in low-contrast regions that underwent significant deformation, such as in the pelvis, or large DVF with strong contrast, such as the clinical tumor volume (CTV) in the lung. Under these conditions, the hybrid DIR algorithms performed significantly better than the free-form intensity based algorithms and resulted robust against intercenter variability. CONCLUSIONS The performances of the systems proved to be site specific, depending on the DVF type and the platforms and the procedures used at the various centers. The pelvis was the most challenging site for most of the algorithms, which failed to achieve sub-voxel accuracy. Improved reproducibility was observed among the centers using the same hybrid registration algorithm.
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Affiliation(s)
- Gianfranco Loi
- Department of Medical Physics, University Hospital "Maggiore della Carità", Novara, Italy
| | - Marco Fusella
- Medical Physics Department, Veneto Institute of Oncology IOV IRCCS, Padua, Italy
| | | | - Elisabetta Cagni
- Department of Medical Physics, S. Maria Nuova Hospital, Reggio Emilia, Italy
| | - Cristina Garibaldi
- Unit of Radiation Research, European Institute of Oncology, Milano, Italy
| | | | - Francesco Lucio
- Department of Medical Physics, "Santa Croce e Carle" Hospital, Cuneo, Italy
| | - Enrico Menghi
- Medical Physics Department, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Roberto Miceli
- Department of Diagnostic Imaging, Molecular Imaging, Interventional Radiology and Radiotherapy, Tor Vergata General Hospital, Rome, Italy
| | - Lucia C Orlandini
- Medical Physics Unit, Centro Oncologico Fiorentino, Firenze, Italy.,Radiation Oncology Department, Sichuan Cancer Hospital, Chengdu, China
| | - Antonella Roggio
- Medical Physics Department, Veneto Institute of Oncology IOV IRCCS, Padua, Italy
| | - Federica Rosica
- Department of Medical Physics, Ospedale Civile Giuseppe Mazzini, Teramo, Italy
| | - Michele Stasi
- SC Fisica sanitaria, A.O. Ordine Mauriziano di Torino, Turin, Italy
| | - Lidia Strigari
- Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome, Italy
| | - Silvia Strolin
- Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome, Italy
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van der Horst A, Houweling AC, van Tienhoven G, Visser J, Bel A. Dosimetric effects of anatomical changes during fractionated photon radiation therapy in pancreatic cancer patients. J Appl Clin Med Phys 2017; 18:142-151. [PMID: 28980445 PMCID: PMC5689920 DOI: 10.1002/acm2.12199] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 08/22/2017] [Accepted: 08/29/2017] [Indexed: 02/06/2023] Open
Abstract
Pancreatic tumors show large interfractional position variation. In addition, changes in gastrointestinal gas volumes and body contour take place over the course of radiation therapy. We aimed to quantify the effect of these anatomical changes on target dose coverage, for the clinically used fiducial marker‐based patient position verification and, for comparison, also for simulated bony anatomy‐based position verification. Nine consecutive patients were included in this retrospective study. To enable fraction dose calculations on cone‐beam CT (CBCT), the planning CT was deformably registered to each CBCT (13–15 per patient); gas volumes visible on CBCT were copied to the deformed CT. Fraction doses were calculated for the clinically used 10 MV VMAT treatment plan (with for the planning target volume (PTV): D98% = 95%), according to fiducial marker‐based and bony anatomy‐based image registrations. Dose distributions were rigidly summed to yield the accumulated dose. To evaluate target dose coverage, we defined an iCTV+5 mm volume, i.e., the internal clinical target volume (iCTV) expanded with a 5 mm margin to account for remaining uncertainties including delineation uncertainties. We analyzed D98%, Dmean, and D2% for iCTV+5 mm and PTV (i.e., iCTV plus 10 mm margin). We found that for fiducial marker‐based registration, differences between fraction doses and planned dose were minimal. For bony anatomy‐based registration, fraction doses differed considerably, resulting in large differences between planned and accumulated dose for some patients, up to a decrease in D98% of the iCTV+5 mm from 95.9% to 85.8%. Our study shows that fractionated photon irradiation of pancreatic tumors is robust against variations in body contour and gastrointestinal gas, with dose coverage only mildly affected. However, as a result of interfractional tumor position variations, target dose coverage can severely decline when using bony anatomy for patient position verification. Therefore, the use of intratumoral fiducial marker‐based daily position verification is essential in pancreatic cancer patients.
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Affiliation(s)
- Astrid van der Horst
- Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Antonetta C Houweling
- Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Geertjan van Tienhoven
- Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jorrit Visser
- Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Arjan Bel
- Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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Branchini M, Fiorino C, Dell'Oca I, Belli M, Perna L, Di Muzio N, Calandrino R, Broggi S. Validation of a method for “dose of the day” calculation in head-neck tomotherapy by using planning ct-to-MVCT deformable image registration. Phys Med 2017; 39:73-79. [DOI: 10.1016/j.ejmp.2017.05.070] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 04/29/2017] [Accepted: 05/28/2017] [Indexed: 01/25/2023] Open
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Kadoya N, Miyasaka Y, Nakajima Y, Kuroda Y, Ito K, Chiba M, Sato K, Dobashi S, Yamamoto T, Takahashi N, Kubozono M, Takeda K, Jingu K. Evaluation of deformable image registration between external beam radiotherapy and HDR brachytherapy for cervical cancer with a 3D-printed deformable pelvis phantom. Med Phys 2017; 44:1445-1455. [PMID: 28214368 DOI: 10.1002/mp.12168] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 12/13/2016] [Accepted: 02/10/2017] [Indexed: 11/09/2022] Open
Abstract
PURPOSE In this study, we developed a 3D-printed deformable pelvis phantom for evaluating spatial DIR accuracy. We then evaluated the spatial DIR accuracies of various DIR settings for cervical cancer. METHODS A deformable female pelvis phantom was created based on patient CT data using 3D printing. To create the deformable uterus phantom, we first 3D printed both a model of uterus and a model of the internal cavities of the vagina and uterus. We then made a mold using the 3D printed uterus phantom. Finally, urethane was poured into the mold with the model of the internal cavities in place, creating the deformable uterus phantom with a cavity into which an applicator could be inserted. To create the deformable bladder phantom, we first 3D printed models of the bladder and of the same bladder scaled down by 2 mm. We then made a mold using the larger bladder model. Finally, silicone was poured into the mold with the smaller bladder model in place to create the deformable bladder phantom with a wall thickness of 2 mm. To emulate the anatomical bladder, water was poured into the created bladder. We acquired phantom image without applicator for EBRT. Then, we inserted the applicator into the phantom to simulate BT. In this situation, we scanned the phantom again to obtain the phantom image for BT. We performed DIR using the two phantom images in two cases: Case A, with full bladder (170 ml) in both EBRT and BT images; and Case B with full bladder in the BT image and half-full bladder (100 ml) in the EBRT image. DIR was evaluated using Dice similarity coefficients (DSCs) and 31 landmarks for the uterus and 25 landmarks for the bladder. A hybrid intensity and structure DIR algorithm implemented in RayStation with four DIR settings was evaluated. RESULTS On visual inspection, reasonable agreement in shape of the uterus between the phantom and patient CT images was observed for both EBRT and BT, although some regional disagreements in shape of the bladder and rectum were apparent. The created phantom could reproduce the actual patient's uterus deformation by the applicator. For both Case A and B, large variation was seen in landmark error among the four DIR parameters. In addition, although DSCs were comparable, moderate differences in landmark error existed between the two different DIR parameters selected from the four DIR parameters (i.e., DSC = 0.96, landmark error = 13.2 ± 5.7 mm vs. DSC = 0.97, landmark error = 9.7 ± 4.0 mm). This result suggests that landmark error evaluation might thus be more effective than DSC for evaluating DIR accuracy. CONCLUSIONS Our developed phantom enabled the evaluation of spatial DIR accuracy for the female pelvic region for the first time. Although the DSCs are high, the spatial errors can still be significant and our developed phantom facilitates their quantification. Our results showed that optimization is needed to identify suitable DIR settings. For determining suitable DIR settings, our method of evaluating spatial DIR accuracy using the 3D-printed phantom may prove helpful.
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Affiliation(s)
- Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Yuya Miyasaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Yoshihiro Kuroda
- Department of Bioengineering, Graduate School of Engineering Science, Osaka University, Osaka, 565-0871, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Mizuki Chiba
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Kiyokazu Sato
- Department of Radiology, Tohoku University Hospital, Sendai, 980-8574, Japan
| | - Suguru Dobashi
- Department of Therapeutic Radiology, Tohoku University School of Medicine, Miyagi, 980-8574, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Noriyoshi Takahashi
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Masaki Kubozono
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
| | - Ken Takeda
- Department of Therapeutic Radiology, Tohoku University School of Medicine, Miyagi, 980-8574, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, 980-8574, Japan
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Teske H, Bartelheimer K, Meis J, Bendl R, Stoiber EM, Giske K. Construction of a biomechanical head and neck motion model as a guide to evaluation of deformable image registration. Phys Med Biol 2017; 62:N271-N284. [PMID: 28350540 DOI: 10.1088/1361-6560/aa69b6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The use of deformable image registration methods in the context of adaptive radiotherapy leads to uncertainties in the simulation of the administered dose distributions during the treatment course. Evaluation of these methods is a prerequisite to decide if a plan adaptation will improve the individual treatment. Current approaches using manual references limit the validity of evaluation, especially for low-contrast regions. In particular, for the head and neck region, the highly flexible anatomy and low soft tissue contrast in control images pose a challenge to image registration and its evaluation. Biomechanical models promise to overcome this issue by providing anthropomorphic motion modelling of the patient. We introduce a novel biomechanical motion model for the generation and sampling of different postures of the head and neck anatomy. Motion propagation behaviour of the individual bones is defined by an underlying kinematic model. This model interconnects the bones by joints and thus is capable of providing a wide range of motion. Triggered by the motion of the individual bones, soft tissue deformation is described by an extended heterogeneous tissue model based on the chainmail approach. This extension, for the first time, allows the propagation of decaying rotations within soft tissue without the necessity for explicit tissue segmentation. Overall motion simulation and sampling of deformed CT scans including a basic noise model is achieved within 30 s. The proposed biomechanical motion model for the head and neck site generates displacement vector fields on a voxel basis, approximating arbitrary anthropomorphic postures of the patient. It was developed with the intention of providing input data for the evaluation of deformable image registration.
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Affiliation(s)
- Hendrik Teske
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
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