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Hardcastle N, Vasquez Osorio E, Jackson A, Mayo C, Aarberg AE, Ayadi M, Belosi F, Ceylan C, Davey A, Dupuis P, Handley JC, Hemminger T, Hoffmann L, Kelly C, Michailidou C, Muscat S, Murrell DH, Pérez-Alija J, Palmer C, Placidi L, Popovic M, Rønde HS, Selby A, Skopidou T, Solomou N, Stroom J, Thompson C, West NS, Zaila A, Appelt AL. Multi-centre evaluation of variation in cumulative dose assessment in reirradiation scenarios. Radiother Oncol 2024; 194:110184. [PMID: 38453055 DOI: 10.1016/j.radonc.2024.110184] [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: 09/13/2023] [Revised: 02/18/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE Safe reirradiation relies on assessment of cumulative doses to organs at risk (OARs) across multiple treatments. Different clinical pathways can result in inconsistent estimates. Here, we quantified the consistency of cumulative dose to OARs across multi-centre clinical pathways. MATERIAL AND METHODS We provided DICOM planning CT, structures and doses for two reirradiation cases: head & neck (HN) and lung. Participants followed their standard pathway to assess the cumulative physical and EQD2 doses (with provided α/β values), and submitted DVH metrics and a description of their pathways. Participants could also submit physical dose distributions from Course 1 mapped onto the CT of Course 2 using their best available tools. To assess isolated impact of image registrations, a single observer accumulated each submitted spatially mapped physical dose for every participating centre. RESULTS Cumulative dose assessment was performed by 24 participants. Pathways included rigid (n = 15), or deformable (n = 5) image registration-based 3D dose summation, visual inspection of isodose line contours (n = 1), or summation of dose metrics extracted from each course (n = 3). Largest variations were observed in near-maximum cumulative doses (25.4 - 41.8 Gy for HN, 2.4 - 33.8 Gy for lung OARs), with lower variations in volume/dose metrics to large organs. A standardised process involving spatial mapping of the first course dose to the second course CT followed by summation improved consistency for most near-maximum dose metrics in both cases. CONCLUSION Large variations highlight the uncertainty in reporting cumulative doses in reirradiation scenarios, with implications for outcome analysis and understanding of published doses. Using a standardised workflow potentially including spatially mapped doses improves consistency in determination of accumulated dose in reirradiation scenarios.
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Affiliation(s)
- Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia.
| | | | - Andrew Jackson
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Charles Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | | | - Myriam Ayadi
- Department of Radiation Oncology, Physics Unit, Centre Léon Bérard, Lyon, France
| | - Francesca Belosi
- Department of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Cemile Ceylan
- Department of Radiation Oncology, Istanbul Oncology Hospital, Istanbul, Turkey; Department of Medical Physics, University of Yeditepe, Istanbul, Turkey
| | - Angela Davey
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Pauline Dupuis
- Department of Radiation Oncology, Physics Unit, Centre Léon Bérard, Lyon, France
| | | | | | - Lone Hoffmann
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Faculty of Health Sciences, Aarhus University, Aarhus, Denmark
| | - Colin Kelly
- St Luke's Radiation Oncology Network, Dublin, Ireland
| | | | - Sarah Muscat
- Department of Medical Physics, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Donna H Murrell
- Department of Oncology, Western University, London, Ontario, Canada; London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada
| | - Jaime Pérez-Alija
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Catherine Palmer
- Department of Radiotherapy Physics, Norfolk and Norwich University Hospitals, NHS Foundation Trust, UK
| | - Lorenzo Placidi
- Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Marija Popovic
- Department of Medical Physics, McGill University Health Centre, Montreal, Quebec, Canada
| | - Heidi S Rønde
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Adam Selby
- South West Wales Cancer Centre, Swansea, Wales, UK
| | | | - Natasa Solomou
- Department of Radiotherapy Physics, Norfolk and Norwich University Hospitals, NHS Foundation Trust, UK
| | - Joep Stroom
- Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | | | | | - Ali Zaila
- Biomedical Physics Department, King Faisal Specialist Hospital and Research Center (KFSHRC), Riyadh, Saudi Arabia
| | - Ane L Appelt
- Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, UK; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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Zhong H, Kainz KK, Paulson ES. Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy. J Appl Clin Med Phys 2024:e14358. [PMID: 38634799 DOI: 10.1002/acm2.14358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 03/03/2024] [Accepted: 03/25/2024] [Indexed: 04/19/2024] Open
Abstract
PURPOSE We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. MATERIALS AND METHODS Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. RESULTS For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. CONCLUSIONS Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.
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Affiliation(s)
- Hualiang Zhong
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Kristofer K Kainz
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Eric S Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Lorenzo Polo A, Nix M, Thompson C, O'Hara C, Entwisle J, Murray L, Appelt A, Weistrand O, Svensson S. Improving hybrid image and structure-based deformable image registration for large internal deformations. Phys Med Biol 2024; 69:095011. [PMID: 38518382 DOI: 10.1088/1361-6560/ad3723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 03/22/2024] [Indexed: 03/24/2024]
Abstract
Objective.Deformable image registration (DIR) is a widely used technique in radiotherapy. Complex deformations, resulting from large anatomical changes, are a regular challenge. DIR algorithms generally seek a balance between capturing large deformations and preserving a smooth deformation vector field (DVF). We propose a novel structure-based term that can enhance the registration efficacy while ensuring a smooth DVF.Approach.The proposed novel similarity metric for controlling structures was introduced as a new term into a commercially available algorithm. Its performance was compared to the original algorithm using a dataset of 46 patients who received pelvic re-irradiation, many of which exhibited complex deformations.Main results.The mean Dice Similarity Coefficient (DSC) under the improved algorithm was 0.96, 0.94, 0.76, and 0.91 for bladder, rectum, colon, and bone respectively, compared to 0.69, 0.89, 0.62, and 0.88 for the original algorithm. The improvement was more pronounced for complex deformations.Significance.With this work, we have demonstrated that the proposed term is able to improve registration accuracy for complex cases while maintaining realistic deformations.
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Affiliation(s)
| | - M Nix
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - C Thompson
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - C O'Hara
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - J Entwisle
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - L Murray
- Leeds Cancer Centre, Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - A Appelt
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - O Weistrand
- RaySearch Laboratories, SE-104 30 Stockholm, Sweden
| | - S Svensson
- RaySearch Laboratories, SE-104 30 Stockholm, Sweden
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Abstract
Magnetic resonance imaging-guided radiation therapy (MRIgRT) has improved soft tissue contrast over computed tomography (CT) based image-guided RT. Superior visualization of the target and surrounding radiosensitive structures has the potential to improve oncological outcomes partly due to safer dose-escalation and adaptive planning. In this review, we highlight the workflow of adaptive MRIgRT planning, which includes simulation imaging, daily MRI, identifying isocenter shifts, contouring, plan optimization, quality control, and delivery. Increased utilization of MRIgRT will depend on addressing technical limitations of this technology, while addressing treatment efficacy, cost-effectiveness, and workflow training.
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Affiliation(s)
- Cecil M Benitez
- Department of Radiation Oncology, UCLA Medical Center, Los Angeles, CA
| | - Michael D Chuong
- Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida; Miami, FL
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden; German Cancer Research Center (DKFZ), Heidelberg; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; 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
| | - Daniela Thorwarth
- Department of Radiation Oncology, Section for Biomedical Physics, University of Tübingen, Tübingen, Germany..
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Ödén J, Eriksson K, Svensson S, Lilley J, Thompson C, Pagett C, Appelt A, Murray L, Bokrantz R. Technical note: Optimization functions for re-irradiation treatment planning. Med Phys 2024; 51:476-484. [PMID: 37921262 DOI: 10.1002/mp.16815] [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: 06/28/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Although re-irradiation is increasingly used in clinical practice, almost no dedicated planning software exists. PURPOSE Standard dose-based optimization functions were adjusted for re-irradiation planning using accumulated equivalent dose in 2-Gy fractions (EQD2) with rigid or deformable dose mapping, tissue-specific α/β, treatment-specific recovery coefficients, and voxelwise adjusted EQD2 penalization levels based on the estimated previously delivered EQD2 (EQD2deliv ). METHODS To demonstrate proof-of-concept, 35 Gy in 5 fractions was planned to a fictitious spherical relapse planning target volume (PTV) in three separate locations following previous prostate treatment on a virtual human phantom. The PTV locations represented one repeated irradiation scenario and two re-irradiation scenarios. For each scenario, three re-planning strategies with identical PTV dose-functions but various organ at risk (OAR) EQD2-functions was used: 1) reRTregular : Regular functions with fixed EQD2 penalization levels larger than EQD2deliv for all OAR voxels. 2) reRTreduce : As reRTregular , but with lower fixed EQD2 penalization levels aiming to reduce OAR EQD2. 3) reRTvoxelwise : As reRTregular and reRTreduce , but with voxelwise adjusted EQD2 penalization levels based on EQD2deliv . PTV near-minimum and near-maximum dose (D98% /D2% ), homogeneity index (HI), conformity index (CI) and accumulated OAR EQD2 (α/β = 3 Gy) were evaluated. RESULTS For the repeated irradiation scenario, all strategies resulted in similar dose distributions. For the re-irradiation scenarios, reRTreduce and reRTvoxelwise reduced accumulated average and near-maximum EQD2 by ˜1-10 Gy for all relevant OARs compared to reRTregular . The reduced OAR doses for reRTreduce came at the cost of distorted dose distributions with D98% = 92.3%, HI = 12.0%, CI = 73.7% and normal tissue hot spots ≥150% for the most complex scenario, while reRTregular (D98% = 98.1%, HI = 3.2%, CI = 94.2%) and reRTvoxelwise (D98% = 96.9%, HI = 6.1%, CI = 93.7%) fulfilled PTV coverage without hot spots. CONCLUSIONS The proposed re-irradiation-specific EQD2-based optimization functions introduce novel planning possibilities with flexible options to guide the trade-off between target coverage and OAR sparing with voxelwise adapted penalization levels based on EQD2deliv .
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Affiliation(s)
- Jakob Ödén
- RaySeach Laboratories AB, Stockholm, Sweden
| | | | | | - John Lilley
- Department of Medical Physics, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Christopher Thompson
- Department of Medical Physics, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Christopher Pagett
- Department of Medical Physics, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Ane Appelt
- Department of Medical Physics, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Louise Murray
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Department of Clinical Oncology, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
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Meyer S, Zhang L, Liu Y, Kuo LC, Hu YC, Yamada Y, Zarepisheh M, Zhang P, Cerviño L. Automated planning of stereotactic spine re-irradiation using cumulative dose limits. Phys Imaging Radiat Oncol 2024; 29:100547. [PMID: 38390589 PMCID: PMC10881437 DOI: 10.1016/j.phro.2024.100547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/24/2024] Open
Abstract
Background and Purpose The lack of dedicated tools in commercial planning systems currently restricts efficient review and planning for re-irradiation. The aim of this study was to develop an automated re-irradiation planning framework based on cumulative doses. Materials and Methods We performed a retrospective study of 14 patients who received spine SBRT re-irradiation near a previously irradiated treatment site. A fully-automated workflow, DART (Dose Accumulation-based Re-irradiation Tool), was implemented within Eclipse by leveraging a combination of a dose accumulation script and a proprietary automated optimization algorithm. First, we converted the prior treatment dose into equivalent dose in 2 Gy fractions (EQD2) and mapped it to the current anatomy, utilizing deformable image registration. Subsequently, the intersection of EQD2 isodose lines with relevant organs at risk defines a series of optimization structures. During plan optimization, the residual allowable dose at a specified tissue tolerance was treated as a hard constraint. Results All DART plans met institutional physical and cumulative constraints and passed plan checks by qualified medical physicists. DART demonstrated significant improvements in target coverage over clinical plans, with an average increase in PTV D99% and V100% of 2.3 Gy [range -0.3-7.7 Gy] and 3.4 % [range -0.4 %-7.6 %] (p < 0.01, paired t-test), respectively. Moreover, high-dose spillage (>105 %) outside the PTV was reduced by up to 7 cm3. The homogeneity index for DART plans was improved by 19 % (p < 0.001). Conclusions DART provides a powerful framework to achieve more tailored re-irradiation plans by accounting for dose distributions from the previous treatments. The superior plan quality could improve the therapeutic ratio for re-irradiation patients.
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Affiliation(s)
- Sebastian Meyer
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Lei Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Yilin Liu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Li Cheng Kuo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Yoshiya Yamada
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Masoud Zarepisheh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Laura Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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McDonald BA, Dal Bello R, Fuller CD, Balermpas P. The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance. Semin Radiat Oncol 2024; 34:69-83. [PMID: 38105096 DOI: 10.1016/j.semradonc.2023.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.
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Affiliation(s)
- Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Panagiotis Balermpas
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
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Smolders A, Lomax A, Weber DC, Albertini F. Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy. Phys Med Biol 2023; 68:245027. [PMID: 37820691 DOI: 10.1088/1361-6560/ad0282] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 10/11/2023] [Indexed: 10/13/2023]
Abstract
Objective.Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics.Approach.Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty.Main results.The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods anexpected calibration errorof less than 3%. Additionally, theprobabilisticly accumulated dose volume histograms(DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining.Significance.Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.
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Affiliation(s)
- A Smolders
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - A Lomax
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - D C Weber
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - F Albertini
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
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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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Nenoff L, Sudhyadhom A, Lau J, Sharp GC, Paganetti H, Pursley J. Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes. Cancers (Basel) 2023; 15:4592. [PMID: 37760560 PMCID: PMC10526201 DOI: 10.3390/cancers15184592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/30/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.
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Affiliation(s)
- Lena Nenoff
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Atchar Sudhyadhom
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Radiation Oncology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Jackson Lau
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gregory C. Sharp
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Harald Paganetti
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jennifer Pursley
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
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11
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Bosma LS, Ries M, Denis de Senneville B, Raaymakers BW, Zachiu C. Integration of operator-validated contours in deformable image registration for dose accumulation in radiotherapy. Phys Imaging Radiat Oncol 2023; 27:100483. [PMID: 37664798 PMCID: PMC10472292 DOI: 10.1016/j.phro.2023.100483] [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: 04/17/2023] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 09/05/2023] Open
Abstract
Background and Purpose Deformable image registration (DIR) is a core element of adaptive radiotherapy workflows, integrating daily contour propagation and/or dose accumulation in their design. Propagated contours are usually manually validated and may be edited, thereby locally invalidating the registration result. This means the registration cannot be used for dose accumulation. In this study we proposed and evaluated a novel multi-modal DIR algorithm that incorporated contour information to guide the registration. This integrates operator-validated contours with the estimated deformation vector field and warped dose. Materials and Methods The proposed algorithm consisted of both a normalized gradient field-based data-fidelity term on the images and an optical flow data-fidelity term on the contours. The Helmholtz-Hodge decomposition was incorporated to ensure anatomically plausible deformations. The algorithm was validated for same- and cross-contrast Magnetic Resonance (MR) image registrations, Computed Tomography (CT) registrations, and CT-to-MR registrations for different anatomies, all based on challenging clinical situations. The contour-correspondence, anatomical fidelity, registration error, and dose warping error were evaluated. Results The proposed contour-guided algorithm considerably and significantly increased contour overlap, decreasing the mean distance to agreement by a factor of 1.3 to 13.7, compared to the best algorithm without contour-guidance. Importantly, the registration error and dose warping error decreased significantly, by a factor of 1.2 to 2.0. Conclusions Our contour-guided algorithm ensured that the deformation vector field and warped quantitative information were consistent with the operator-validated contours. This provides a feasible semi-automatic strategy for spatially correct warping of quantitative information even in difficult and artefacted cases.
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Affiliation(s)
- Lando S Bosma
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | - Mario Ries
- Imaging Division, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | - Baudouin Denis de Senneville
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
- Institut de Mathématiques de Bordeaux (IMB), UMR 5251 CNRS/University of Bordeaux, F-33400 Talence, France
| | - Bas W Raaymakers
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | - Cornel Zachiu
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
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12
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Regnery S, Leiner L, Buchele C, Hoegen P, Sandrini E, Held T, Deng M, Eichkorn T, Rippke C, Renkamp CK, König L, Lang K, Adeberg S, Debus J, Klüter S, Hörner-Rieber J. Comparison of different dose accumulation strategies to estimate organ doses after stereotactic magnetic resonance-guided adaptive radiotherapy. Radiat Oncol 2023; 18:92. [PMID: 37248504 DOI: 10.1186/s13014-023-02284-7] [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: 01/23/2023] [Accepted: 05/17/2023] [Indexed: 05/31/2023] Open
Abstract
INTRODUCTION Re-irradiation is frequently performed in the era of precision oncology, but previous doses to organs-at-risk (OAR) must be assessed to avoid cumulative overdoses. Stereotactic magnetic resonance-guided online adaptive radiotherapy (SMART) enables highly precise ablation of tumors close to OAR. However, OAR doses may change considerably during adaptive treatment, which complicates potential re-irradiation. We aimed to compare the baseline plan with different dose accumulation techniques to inform re-irradiation. PATIENTS & METHODS We analyzed 18 patients who received SMART to lung or liver tumors inside prospective databases. Cumulative doses were calculated inside the planning target volumes (PTV) and OAR for the adapted plans and theoretical non-adapted plans via (1) cumulative dose volume histograms (DVH sum plan) and (2) deformable image registration (DIR)-based dose accumulation to planning images (DIR sum plan). We compared cumulative dose parameters between the baseline plan, DVH sum plan and DIR sum plan using equivalent doses in 2 Gy fractions (EQD2). RESULTS Individual patients presented relevant increases of near-maximum doses inside the proximal bronchial tree, spinal cord, heart and gastrointestinal OAR when comparing adaptive treatment to the baseline plans. The spinal cord near-maximum doses were significantly increased in the liver patients (D2% median: baseline 6.1 Gy, DIR sum 8.1 Gy, DVH sum 8.4 Gy, p = 0.04; D0.1 cm³ median: baseline 6.1 Gy, DIR sum 8.1 Gy, DVH sum 8.5 Gy, p = 0.04). Three OAR overdoses occurred during adaptive treatment (DIR sum: 1, DVH sum: 2), and four more intense OAR overdoses would have occurred during non-adaptive treatment (DIR sum: 4, DVH sum: 3). Adaptive treatment maintained similar PTV coverages to the baseline plans, while non-adaptive treatment yielded significantly worse PTV coverages in the lung (D95% median: baseline 86.4 Gy, DIR sum 82.4 Gy, DVH sum 82.2 Gy, p = 0.006) and liver patients (D95% median: baseline 87.4 Gy, DIR sum 82.1 Gy, DVH sum 81.1 Gy, p = 0.04). CONCLUSION OAR doses can increase during SMART, so that re-irradiation should be planned based on dose accumulations of the adapted plans instead of the baseline plan. Cumulative dose volume histograms represent a simple and conservative dose accumulation strategy.
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Affiliation(s)
- Sebastian Regnery
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Lukas Leiner
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Carolin Buchele
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Philipp Hoegen
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elisabetta Sandrini
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Thomas Held
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Maximilian Deng
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Tanja Eichkorn
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Carolin Rippke
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - C Katharina Renkamp
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Laila König
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Kristin Lang
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Sebastian Adeberg
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Juliane Hörner-Rieber
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
- National Center for Tumor diseases (NCT), Heidelberg, Germany.
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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13
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Vasquez Osorio E, Mayo C, Jackson A, Appelt A. Challenges of re-irradiation: A call to arms for physicists - and radiotherapy vendors. Radiother Oncol 2023; 182:109585. [PMID: 36842664 DOI: 10.1016/j.radonc.2023.109585] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 02/28/2023]
Affiliation(s)
| | - Charles Mayo
- University of Michigan, Ann Arbor, MI 48109-5010, USA
| | - Andrew Jackson
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ane Appelt
- University of Leeds, LS2 9JT Leeds, United Kingdom
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