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Gooding MJ, Aluwini S, Guerrero Urbano T, McQuinlan Y, Om D, Staal FHE, Perennec T, Azzarouali S, Cardenas CE, Carver A, Korreman SS, Bibault JE. Fully automated radiotherapy treatment planning: A scan to plan challenge. Radiother Oncol 2024:110513. [PMID: 39222848 DOI: 10.1016/j.radonc.2024.110513] [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: 04/30/2024] [Revised: 08/19/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND PURPOSE Over the past decade, tools for automation of various sub-tasks in radiotherapy planning have been introduced, such as auto-contouring and auto-planning. The purpose of this study was to benchmark what degree of automation is possible. MATERIALS AND METHODS A challenge to perform automated treatment planning for prostate and prostate bed radiotherapy was set up. Participants were provided with simulation CTs and a treatment prescription and were asked to use automated tools to produce a deliverable radiotherapy treatment plan with as little human intervention as possible. Plans were scored for their adherence to the protocol when assessed using consensus expert contours. RESULTS Thirteen entries were received. The top submission adhered to 81.8% of the minimum objectives across all cases using the consensus contour, meeting all objectives in one of the ten cases. The same system met 89.5% of objectives when assessed with their own auto-contours, meeting all objectives in four of the ten cases. The majority of systems used in the challenge had regulatory clearance (Auto-contouring: 82.5%, Auto-planning: 77%). Despite the 'hard' rule that participants should not check or edit contours or plans, 69% reported looking at their results before submission. CONCLUSIONS Automation of the full planning workflow from simulation CT to deliverable treatment plan is possible for prostate and prostate bed radiotherapy. While many generated plans were found to require none or minor adjustment to be regarded as clinically acceptable, the result indicated there is still a lack of trust in such systems preventing full automation.
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Affiliation(s)
- Mark J Gooding
- Inpictura Ltd, 5 The Chambers, Vineyard, Abingdon OX14 3PX, The United Kingdom of Great Britain and Northern Ireland; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, M20 4BX Manchester, The United Kingdom of Great Britain and Northern Ireland.
| | - Shafak Aluwini
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Teresa Guerrero Urbano
- Department of Clinical Oncology Guy's and St Thomas' NHS Foundation Trust School of Cancer and Pharmaceutical Sciences King's College London, London, The United Kingdom of Great Britain and Northern Ireland.
| | - Yasmin McQuinlan
- Mirada Medical Ltd, Barclay House, 234 Botley Road OX2 0HP, The United Kingdom of Great Britain and Northern Ireland.
| | - Deborah Om
- Department of Medical Physics, Hôpital Européen Georges Pompidou, Université Paris Cité, 75015 Paris, France.
| | - Floor H E Staal
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, thte Netherlands.
| | - Tanguy Perennec
- Département de radiothérapie, Institut de Cancérologie de l'Ouest, Nantes, France.
| | - Sana Azzarouali
- Radiation Oncology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Antony Carver
- Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, The United Kingdom of Great Britain and Northern Ireland.
| | - Stine Sofia Korreman
- Department of Clinical Medicine, Aarhus University, Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.
| | - Jean-Emmanuel Bibault
- Department of Radiation Oncology, Hôpital Européen Georges-Pompidou, Université Paris Cité, 75015 Paris, France.
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Nachbar M, Lo Russo M, Gani C, Boeke S, Wegener D, Paulsen F, Zips D, Roque T, Paragios N, Thorwarth D. Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy. Z Med Phys 2024; 34:197-207. [PMID: 37263911 PMCID: PMC11156783 DOI: 10.1016/j.zemedi.2023.05.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/03/2023] [Accepted: 05/02/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND AND PURPOSE MR-guided radiotherapy (MRgRT) online plan adaptation accounts for tumor volume changes, interfraction motion and thus allows daily sparing of relevant organs at risk. Due to the high interfraction variability of bladder and rectum, patients with tumors in the pelvic region may strongly benefit from adaptive MRgRT. Currently, fast automatic annotation of anatomical structures is not available within the online MRgRT workflow. Therefore, the aim of this study was to train and validate a fast, accurate deep learning model for automatic MRI segmentation at the MR-Linac for future implementation in a clinical MRgRT workflow. MATERIALS AND METHODS For a total of 47 patients, T2w MRI data were acquired on a 1.5 T MR-Linac (Unity, Elekta) on five different days. Prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, body and bony structures were manually annotated. These training data consisting of 232 data sets in total was used for the generation of a deep learning based autocontouring model and validated on 20 unseen T2w-MRIs. For quantitative evaluation the validation set was contoured by a radiation oncologist as gold standard contours (GSC) and compared in MATLAB to the automatic contours (AIC). For the evaluation, dice similarity coefficients (DSC), and 95% Hausdorff distances (95% HD), added path length (APL) and surface DSC (sDSC) were calculated in a caudal-cranial window of ± 4 cm with respect to the prostate ends. For qualitative evaluation, five radiation oncologists scored the AIC on the possible usage within an online adaptive workflow as follows: (1) no modifications needed, (2) minor adjustments needed, (3) major adjustments/ multiple minor adjustments needed, (4) not usable. RESULTS The quantitative evaluation revealed a maximum median 95% HD of 6.9 mm for the rectum and minimum median 95% HD of 2.7 mm for the bladder. Maximal and minimal median DSC were detected for bladder with 0.97 and for penile bulb with 0.73, respectively. Using a tolerance level of 3 mm, the highest and lowest sDSC were determined for rectum (0.94) and anal canal (0.68), respectively. Qualitative evaluation resulted in a mean score of 1.2 for AICs over all organs and patients across all expert ratings. For the different autocontoured structures, the highest mean score of 1.0 was observed for anal canal, sacrum, femur left and right, and pelvis left, whereas for prostate the lowest mean score of 2.0 was detected. In total, 80% of the contours were rated be clinically acceptable, 16% to require minor and 4% major adjustments for online adaptive MRgRT. CONCLUSION In this study, an AI-based autocontouring was successfully trained for online adaptive MR-guided radiotherapy on the 1.5 T MR-Linac system. The developed model can automatically generate contours accepted by physicians (80%) or only with the need of minor corrections (16%) for the irradiation of primary prostate on the clinically employed sequences.
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Affiliation(s)
- Marcel Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Monica Lo Russo
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Cihan Gani
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Simon Boeke
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Daniel Wegener
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Frank Paulsen
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Nikos Paragios
- TheraPanacea, Paris, France; CentraleSupelec, University of Paris-Saclay, Gif-sur-Yvette, France
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Cobanaj M, Corti C, Dee EC, McCullum L, Boldrini L, Schlam I, Tolaney SM, Celi LA, Curigliano G, Criscitiello C. Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow. Eur J Cancer 2024; 198:113504. [PMID: 38141549 PMCID: PMC11362966 DOI: 10.1016/j.ejca.2023.113504] [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: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.
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Affiliation(s)
- Marisa Cobanaj
- National Center for Radiation Research in Oncology, OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Chiara Corti
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
| | - Edward C Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucas McCullum
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Boldrini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Ilana Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leo A Celi
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
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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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Tengler B, Künzel LA, Hagmüller M, Mönnich D, Boeke S, Wegener D, Gani C, Zips D, Thorwarth D. Full daily re-optimization improves plan quality during online adaptive radiotherapy. Phys Imaging Radiat Oncol 2024; 29:100534. [PMID: 38298884 PMCID: PMC10827578 DOI: 10.1016/j.phro.2024.100534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/02/2024] Open
Abstract
Background and purpose Daily online treatment plan adaptation requires a fast workflow and planning process. Current online planning consists of adaptation of a predefined reference plan, which might be suboptimal in cases of large anatomic changes. The aim of this study was to investigate plan quality differences between the current online re-planning approach and a complete re-optimization. Material and methods Magnetic resonance linear accelerator reference plans for ten prostate cancer patients were automatically generated using particle swarm optimization (PSO). Adapted plans were created for each fraction using (1) the current re-planning approach and (2) full PSO re-optimization and evaluated overall compliance with institutional dose-volume criteria compared to (3) clinically delivered fractions. Relative volume differences between reference and daily anatomy were assessed for planning target volumes (PTV60, PTV57.6), rectum and bladder and correlated with dose-volume results. Results The PSO approach showed significantly higher adherence to dose-volume criteria than the reference approach and clinical fractions (p < 0.001). In 74 % of PSO plans at most one criterion failed compared to 56 % in the reference approach and 41 % in clinical plans. A fair correlation between PTV60 D98% and relative bladder volume change was observed for the reference approach. Bladder volume reductions larger than 50 % compared to the reference plan recurrently decreased PTV60 D98% below 56 Gy. Conclusion Complete re-optimization maintained target coverage and organs at risk sparing even after large anatomic variations. Re-planning based on daily magnetic resonance imaging was sufficient for small variations, while large variations led to decreasing target coverage and organ-at-risk sparing.
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Affiliation(s)
- Benjamin Tengler
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Luise A. Künzel
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, 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
| | - Markus Hagmüller
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - David Mönnich
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Simon Boeke
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Daniel Wegener
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Cihan Gani
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Guckenberger M, Andratschke N, Chung C, Fuller D, Tanadini-Lang S, Jaffray DA. The Future of MR-Guided Radiation Therapy. Semin Radiat Oncol 2024; 34:135-144. [PMID: 38105088 DOI: 10.1016/j.semradonc.2023.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT.
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Affiliation(s)
- Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland..
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Caroline Chung
- Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Dave Fuller
- Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - David A Jaffray
- Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
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Tanaka S, Kadoya N, Ishizawa M, Katsuta Y, Arai K, Takahashi H, Xiao Y, Takahashi N, Sato K, Takeda K, Jingu K. Evaluation of Unity 1.5 T MR-linac plan quality in patients with prostate cancer. J Appl Clin Med Phys 2023; 24:e14122. [PMID: 37559561 PMCID: PMC10691646 DOI: 10.1002/acm2.14122] [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: 03/21/2023] [Revised: 06/26/2023] [Accepted: 07/31/2023] [Indexed: 08/11/2023] Open
Abstract
The Unity magnetic resonance (MR) linear accelerator (MRL) with MR-guided adaptive radiotherapy (MRgART) is capable of online MRgART where images are acquired on the treatment day and the radiation treatment plan is immediately replanned and performed. We evaluated the MRgART plan quality and plan reproducibility of the Unity MRL in patients with prostate cancer. There were five low- or moderate-risk and five high-risk patients who received 36.25 Gy or 40 Gy, respectively in five fractions. All patients underwent simulation magnetic resonance imaging (MRI) and five online adaptive MRI. We created plans for 5, 7, 9, 16, and 20 beams and for 60, 100, and 150 segments. We evaluated the target and organ doses for different number of beams and segments, respectively. Variation in dose constraint between the simulation plan and online adaptive plan was measured for each patient to assess plan reproducibility. The plan quality improved with the increasing number of beams. However, the proportion of significantly improved dose constraints decreased as the number of beams increased. For some dose parameters, there were statistically significant differences between 60 and 100 segments, and 100 and 150 segments. The plan of five beams exhibited limited reproducibility. The number of segments had minimal impact on plan reproducibility, but 60 segments sometimes failed to meet dose constraints for online adaptive plan. The optimization and delivery time increased with the number of beams and segments. We do not recommend using five or fewer beams for a reproducible and high-quality plan in the Unity MRL. In addition, many number of segments and beams may help meet dose constraints during online adaptive plan. Treatment with the Unity MRL should be performed with the appropriate number of beams and segments to achieve a good balance among plan quality, delivery time, and optimization time.
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Affiliation(s)
- Shohei Tanaka
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
| | - Noriyuki Kadoya
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
| | - Miyu Ishizawa
- Department of Radiological TechnologySchool of Health SciencesFaculty of MedicineTohoku UniversitySendaiJapan
| | - Yoshiyuki Katsuta
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
| | - Kazuhiro Arai
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
| | - Haruna Takahashi
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
| | - Yushan Xiao
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
| | - Noriyoshi Takahashi
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
| | - Kiyokazu Sato
- Radiation TechnologyTohoku University HospitalSendaiJapan
| | - Ken Takeda
- Department of Radiological TechnologySchool of Health SciencesFaculty of MedicineTohoku UniversitySendaiJapan
| | - Keiichi Jingu
- Department of Radiation OncologyTohoku University Graduate School of MedicineSendaiJapan
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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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10
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Adair Smith G, Dunlop A, Alexander SE, Barnes H, Casey F, Chick J, Gunapala R, Herbert T, Lawes R, Mason SA, Mitchell A, Mohajer J, Murray J, Nill S, Patel P, Pathmanathan A, Sritharan K, Sundahl N, Tree AC, Westley R, Williams B, McNair HA. Evaluation of therapeutic radiographer contouring for magnetic resonance image guided online adaptive prostate radiotherapy. Radiother Oncol 2023; 180:109457. [PMID: 36608770 PMCID: PMC10074473 DOI: 10.1016/j.radonc.2022.109457] [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: 08/24/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND AND PURPOSE The implementation of MRI-guided online adaptive radiotherapy has facilitated the extension of therapeutic radiographers' roles to include contouring, thus releasing the clinician from attending daily treatment. Following undergoing a specifically designed training programme, an online interobserver variability study was performed. MATERIALS AND METHODS 117 images from six patients treated on a MR Linac were contoured online by either radiographer or clinician and the same images contoured offline by the alternate profession. Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) and volume metrics were used to analyse contours. Additionally, the online radiographer contours and optimised plans (n = 59) were analysed using the offline clinician defined contours. After clinical implementation of radiographer contouring, target volume comparison and dose analysis was performed on 20 contours from five patients. RESULTS Comparison of the radiographers' and clinicians' contours resulted in a median (range) DSC of 0.92 (0.86 - 0.99), median (range) MDA of 0.98 mm (0.2-1.7) and median (range) HD of 6.3 mm (2.5-11.5) for all 117 fractions. There was no significant difference in volume size between the two groups. Of the 59 plans created with radiographer online contours and overlaid with clinicians' offline contours, 39 met mandatory dose constraints and 12 were acceptable because 95 % of the high dose PTV was covered by 95 % dose, or the high dose PTV was within 3 % of online plan. A clinician blindly reviewed the eight remaining fractions and, using trial quality assurance metrics, deemed all to be acceptable. Following clinical implementation of radiographer contouring, the median (range) DSC of CTV was 0.93 (0.88-1.0), median (range) MDA was 0.8 mm (0.04-1.18) and HD was 5.15 mm (2.09-8.54) respectively. Of the 20 plans created using radiographer online contours overlaid with clinicians' offline contours, 18 met the dosimetric success criteria, the remaining 2 were deemed acceptable by a clinician. CONCLUSION Radiographer and clinician prostate and seminal vesicle contours on MRI for an online adaptive workflow are comparable and produce clinically acceptable plans. Radiographer contouring for prostate treatment on a MR-linac can be effectively introduced with appropriate training and evaluation. A DSC threshold for target structures could be implemented to streamline future training.
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Affiliation(s)
| | - Alex Dunlop
- Joint Department of Physics at the Royal Marsden and The Institute of Cancer Research, United Kingdom
| | - Sophie E Alexander
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Helen Barnes
- The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Francis Casey
- Joint Department of Physics at the Royal Marsden and The Institute of Cancer Research, United Kingdom
| | - Joan Chick
- Joint Department of Physics at the Royal Marsden and The Institute of Cancer Research, United Kingdom
| | - Ranga Gunapala
- Clinical Trials and Statistic Unit, The Institute for Cancer Research, London, United Kingdom
| | - Trina Herbert
- The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Rebekah Lawes
- The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Sarah A Mason
- Joint Department of Physics at the Royal Marsden and The Institute of Cancer Research, United Kingdom
| | - Adam Mitchell
- Joint Department of Physics at the Royal Marsden and The Institute of Cancer Research, United Kingdom
| | - Jonathan Mohajer
- Joint Department of Physics at the Royal Marsden and The Institute of Cancer Research, United Kingdom
| | - Julia Murray
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Simeon Nill
- Joint Department of Physics at the Royal Marsden and The Institute of Cancer Research, United Kingdom
| | - Priyanka Patel
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Angela Pathmanathan
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Kobika Sritharan
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Nora Sundahl
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Alison C Tree
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Rosalyne Westley
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | | | - Helen A McNair
- The Institute of Cancer Research/The Royal Marsden NHS Foundation Trust, London, United Kingdom
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11
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De-Colle C, Kirby A, Russell N, Shaitelman S, Currey A, Donovan E, Hahn E, Han K, Anandadas C, Mahmood F, Lorenzen E, van den Bongard D, Groot Koerkamp M, Houweling A, Nachbar M, Thorwarth D, Zips D. Adaptive radiotherapy for breast cancer. Clin Transl Radiat Oncol 2023; 39:100564. [PMID: 36632056 PMCID: PMC9826896 DOI: 10.1016/j.ctro.2022.100564] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Research in the field of local and locoregional breast cancer radiotherapy aims to maintain excellent oncological outcomes while reducing treatment-related toxicity. Adaptive radiotherapy (ART) considers variations in target and organs at risk (OARs) anatomy occurring during the treatment course and integrates these in re-optimized treatment plans. Exploiting ART routinely in clinic may result in smaller target volumes and better OAR sparing, which may lead to reduction of acute as well as late toxicities. In this review MR-guided and CT-guided ART for breast cancer patients according to different clinical scenarios (neoadjuvant and adjuvant partial breast irradiation, whole breast, chest wall and regional nodal irradiation) are reviewed and their advantages as well as challenging aspects discussed.
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Affiliation(s)
- C. De-Colle
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - A. Kirby
- Department of Radiotherapy, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Sutton, United Kingdom
| | - N. Russell
- Department of Radiotherapy, The Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - S.F. Shaitelman
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - A. Currey
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - E. Donovan
- Department of Radiation Oncology, Odette Cancer Centre - Sunnybrook Health Sciences Centre, Toronto, Canada
| | - E. Hahn
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - K. Han
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - C.N. Anandadas
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - F. Mahmood
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - E.L. Lorenzen
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | | | - M.L. Groot Koerkamp
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - A.C. Houweling
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - M. Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology. University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - D. Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology. University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - D. Zips
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
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12
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Towards real-time radiotherapy planning: The role of autonomous treatment strategies. Phys Imaging Radiat Oncol 2022; 24:136-137. [DOI: 10.1016/j.phro.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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13
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Gurney-Champion OJ, Landry G, Redalen KR, Thorwarth D. Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy. Semin Radiat Oncol 2022; 32:377-388. [DOI: 10.1016/j.semradonc.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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14
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Milder MT, Magallon-Baro A, den Toom W, de Klerck E, Luthart L, Nuyttens JJ, Hoogeman MS. Technical feasibility of online adaptive stereotactic treatments in the abdomen on a robotic radiosurgery system. Phys Imaging Radiat Oncol 2022; 23:103-108. [PMID: 35928600 PMCID: PMC9344339 DOI: 10.1016/j.phro.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Maaike T.W. Milder
- Corresponding author at: Department of Radiation Oncology, Erasmus MC – Cancer Institute, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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15
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Vivas Maiques B, Ruiz IO, Janssen T, Mans A. Clinical rationale for in vivo portal dosimetry in magnetic resonance guided online adaptive radiotherapy. Phys Imaging Radiat Oncol 2022; 23:16-23. [PMID: 35734264 PMCID: PMC9207286 DOI: 10.1016/j.phro.2022.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 10/28/2022] Open
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16
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Evaluation of an automated template-based treatment planning system for radiotherapy of anal, rectal and prostate cancer. Tech Innov Patient Support Radiat Oncol 2022; 22:30-36. [PMID: 35464888 PMCID: PMC9020095 DOI: 10.1016/j.tipsro.2022.04.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/11/2022] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Automated treatment planning system compared to manual planning. Equivalent plan quality between VMAT manually generated- and IMRT automatically generated plans. Evaluation of anal, prostate and rectum treatment plans. Generation of highly consistent IMRT automated plan within 2 to 3.5 min.
Background and purpose The Ethos system has enabled online adaptive radiotherapy (oART) by implementing an automated treatment planning system (aTPS) for both intensity-modulated radiotherapy (IMRT) and volumetric modulated arc radiotherapy (VMAT) plan creation. The purpose of this study is to evaluate the quality of aTPS plans in the pelvic region. Material and Methods Sixty patients with anal (n = 20), rectal (n = 20) or prostate (n = 20) cancer were retrospectively re-planned with the aTPS. Three IMRT (7-, 9- and 12-field) and two VMAT (2 and 3 arc) automatically generated plans (APs) were created per patient. The duration of the automated plan generation was registered. The best IMRT-AP and VMAT-AP for each patient were selected based on target coverage and dose to organs at risk (OARs). The AP quality was analyzed and compared to corresponding clinically accepted and manually generated VMAT plans (MPs) using several clinically relevant dose metrics. Calculation-based pre-treatment plan quality assurance (QA) was performed for all plans. Results The median total duration to generate the five APs with the aTPS was 55 min, 39 min and 35 min for anal, prostate and rectal plans, respectively. The target coverage and the OAR sparing were equivalent for IMRT-APs and VMAT-MPs, while VMAT-Aps. demonstrated lower target dose homogeneity and higher dose to some OARs. Both conformity and homogeneity index were equivalent (rectal) or better (anal and prostate) for IMRT-APs compared to VMAT-MPs. All plans passed the patient-specific QA tolerance limit. Conclusions The aTPS generates plans comparable to MPs within a short time-frame which is highly relevant for oART treatments.
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Key Words
- AP, automatically generated plan
- Automated treatment planning
- CN, conformity number
- CT, computed tomography
- CTV, clinical target volume
- DVH, dose volume histogram
- FFF, flattening filter free
- GTV, gross tumor volume
- HI, homogeneity index
- IMRT, intensity modulated radiotherapy
- Intelligent optimization engine
- KPB, knowledge-based planning
- Linac, Linear accelerators
- MCO, multi-criteria optimization
- MLC, multileaf collimator
- MP, manually-generated plan
- MR, magnetic resonance
- MU, Monitor Unit
- OAR, Organ at risk
- Online adaptive radiotherapy
- PTV, planning target volume
- Pelvic cancer
- Plan quality
- QA, Quality assurance
- SD, standard deviation
- Template-based Ethos TPS
- VMAT, volumetric arc radiotherapy
- aTPS, automated treatment planning system
- oART, online adaptive radiotherapy
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17
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Naccarato S, Rigo M, Pellegrini R, Voet P, Akhiat H, Gurrera D, De Simone A, Sicignano G, Mazzola R, Figlia V, Ricchetti F, Nicosia L, Giaj-Levra N, Cuccia F, Stavreva N, Pressyanov DS, Stavrev P, Alongi F, Ruggieri R. Automated Planning for Prostate Stereotactic Body Radiation Therapy on the 1.5 T MR-Linac. Adv Radiat Oncol 2022; 7:100865. [PMID: 35198836 PMCID: PMC8850203 DOI: 10.1016/j.adro.2021.100865] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/19/2021] [Indexed: 11/25/2022] Open
Abstract
Purpose Adaptive stereotactic body radiation therapy (SBRT) for prostate cancer (PC) by the 1.5 T MR-linac currently requires online planning by an expert user. A fully automated and user-independent solution to adaptive planning (mCycle) of PC-SBRT was compared with user's plans for the 1.5 T MR-linac. Methods and Materials Fifty adapted plans on daily magnetic resonance imaging scans for 10 patients with PC treated by 35 Gy (prescription dose [Dp]) in 5 fractions were reoptimized offline from scratch, both by an expert planner (manual) and by mCycle. Manual plans consisted of multicriterial optimization (MCO) of the fluence map plus manual tweaking in segmentation, whereas in mCycle plans, the objectives were sequentially optimized by MCO according to an a-priori assigned priority list. The main criteria for planning approval were a dose ≥95% of the Dp to at least 95% of the planning target volume (PTV), V33.2 (PTV) ≥ 95%, a dose less than the Dp to the hottest cubic centimeter (V35 ≤ 1 cm3) of rectum, bladder, penile bulb, and urethral planning risk volume (ie, urethra plus 3 mm isotropically), and V32 ≤ 5%, V28 ≤ 10%, and V18 ≤ 35% to the rectum. Such dose-volume metrics, plus some efficiency and deliverability metrics, were used for the comparison of mCycle versus manual plans. Results mCycle plans improved target dose coverage, with V33.2 (PTV) passing on average (±1 SD) from 95.7% (±1.0%) for manual plans to 97.5% (±1.3%) for mCycle plans (P < .001), and rectal dose sparing, with significantly reduced V32, V28, and V18 (P ≤ .004). Although at an equivalent number of segments, mCycle plans consumed moderately more monitor units (+17%) and delivery time (+9%) (P < .001), whereas they were generally faster (–19%) in terms of optimization times (P < .019). No significant differences were found for the passing rates of locally normalized γ (3 mm, 3%) (P = .059) and γ (2 mm, 2%) (P = .432) deliverability metrics. Conclusions In the offline setting, mCycle proved to be a trustable solution for automated planning of PC-SBRT on the 1.5 T MR-linac. mCycle integration in the online workflow will free the user from the challenging online-optimization task.
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18
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Kron T, Fox C, Ebert MA, Thwaites D. Quality management in radiotherapy treatment delivery. J Med Imaging Radiat Oncol 2022; 66:279-290. [PMID: 35243785 DOI: 10.1111/1754-9485.13348] [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: 09/01/2021] [Accepted: 10/29/2021] [Indexed: 12/17/2022]
Abstract
Radiation Oncology continues to rely on accurate delivery of radiation, in particular where patients can benefit from more modulated and hypofractioned treatments that can deliver higher dose to the target while optimising dose to normal structures. These deliveries are more complex, and the treatment units are more computerised, leading to a re-evaluation of quality assurance (QA) to test a larger range of options with more stringent criteria without becoming too time and resource consuming. This review explores how modern approaches of risk management and automation can be used to develop and maintain an effective and efficient QA programme. It considers various tools to control and guide radiation delivery including image guidance and motion management. Links with typical maintenance and repair activities are discussed, as well as patient-specific quality control activities. It is demonstrated that a quality management programme applied to treatment delivery can have an impact on individual patients but also on the quality of treatment techniques and future planning. Developing and customising a QA programme for treatment delivery is an important part of radiotherapy. Using modern multidisciplinary approaches can make this also a useful tool for department management.
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Affiliation(s)
- Tomas Kron
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Institute of Oncology, Melbourne University, Melbourne, Victoria, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
| | - Chris Fox
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Martin A Ebert
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia.,Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.,School of Physics, Mathematics and Computing, University of Western Australia, Perth, Western Australia, Australia.,5D Clinics, Perth, Western Australia, Australia
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia.,Medical Physics Group, Leeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute of Medical Research, University of Leeds, Leeds, UK
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19
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Hansen CR, Hussein M, Bernchou U, Zukauskaite R, Thwaites D. Plan quality in radiotherapy treatment planning - Review of the factors and challenges. J Med Imaging Radiat Oncol 2022; 66:267-278. [PMID: 35243775 DOI: 10.1111/1754-9485.13374] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/14/2021] [Indexed: 12/25/2022]
Abstract
A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.
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Affiliation(s)
- Christian Rønn Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark.,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia.,Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Mohammad Hussein
- Metrology for Medical Physics Centre, National Physical Laboratory, Teddington, UK
| | - Uffe Bernchou
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ruta Zukauskaite
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark.,Department of Oncology, Odense University Hospital, Odense, Denmark
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
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20
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Clinical evaluation of autonomous, unsupervised planning integrated in MR-guided radiotherapy for prostate cancer. Radiother Oncol 2022; 168:229-233. [DOI: 10.1016/j.radonc.2022.01.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 01/18/2023]
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21
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Wegener D, Zips D, Gani C, Boeke S, Nikolaou K, Othman AE, Almansour H, Paulsen F, Müller AC. [Primary treatment of prostate cancer using 1.5 T MR-linear accelerator]. Radiologe 2021; 61:839-845. [PMID: 34297139 PMCID: PMC8410708 DOI: 10.1007/s00117-021-00882-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2021] [Indexed: 11/26/2022]
Abstract
Hintergrund Der potenzielle Nutzen des verbesserten Weichteilkontrastes von MR-Sequenzen gegenüber der Computertomographie (CT) für die Radiotherapie des Prostatakarzinoms ist bekannt und führt zu konsistenteren und kleineren Zielvolumina sowie verbesserter Risikoorganschonung. Hybridgeräte aus Magnetresonanztomographie (MRT) und Linearbeschleuniger (MR-Linac) stellen eine neue vielversprechende Erweiterung der radioonkologischen Therapieoptionen dar. Material und Methoden Dieser Artikel gibt eine Übersicht über bisherige Erfahrungen, Indikationen, Vorteile und Herausforderungen für die Radiotherapie des primären Prostatakarzinoms mit dem 1,5-T-MR-Linac. Ergebnisse Alle strahlentherapeutischen Therapieindikationen für das primäre Prostatakarzinom können mit dem 1,5-T-MR-Linac abgedeckt werden. Die potenziellen Vorteile umfassen die tägliche MR-basierte Lagekontrolle in Bestrahlungsposition und die Möglichkeit der täglichen Echtzeitanpassung des Bestrahlungsplans an die aktuelle Anatomie der Beckenorgane (adaptive Strahlentherapie). Zusätzlich werden am 1,5-T-MR-Linac funktionelle MRT-Sequenzen für individuelles Response-Assessment für die Therapieanpassung untersucht. Dadurch soll das therapeutische Fenster weiter optimiert werden. Herausforderungen stellen u. a. die technische Komplexität und die Dauer der Behandlungssitzung dar. Schlussfolgerung Der 1,5-T-MR-Linac erweitert das radioonkologische Spektrum in der Therapie des Prostatakarzinoms und bietet Vorteile durch tagesaktuelle MRT-basierte Zielvolumendefinition und Planadaptation. Weitere klinische Untersuchungen sind notwendig, um die Patienten zu identifizieren, die von der Behandlung am MR-Linac gegenüber anderen strahlentherapeutischen Methoden besonders profitieren.
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Affiliation(s)
- Daniel Wegener
- Universitätsklinik für Radioonkologie, Universitätsklinikum Tübingen, Eberhard Karls Universität Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Deutschland.
| | - Daniel Zips
- Universitätsklinik für Radioonkologie, Universitätsklinikum Tübingen, Eberhard Karls Universität Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Deutschland
| | - Cihan Gani
- Universitätsklinik für Radioonkologie, Universitätsklinikum Tübingen, Eberhard Karls Universität Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Deutschland
| | - Simon Boeke
- Universitätsklinik für Radioonkologie, Universitätsklinikum Tübingen, Eberhard Karls Universität Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Deutschland
| | - Konstantin Nikolaou
- Universitätsklinik für Radiologie, Eberhard Karls Universität Tübingen, Tübingen, Deutschland
| | - Ahmed E Othman
- Universitätsklinik für Radiologie, Eberhard Karls Universität Tübingen, Tübingen, Deutschland
- Universitätsklink für Neuroradiologie, Johannes Gutenberg-Universität Mainz, Mainz, Deutschland
| | - Haidara Almansour
- Universitätsklinik für Radiologie, Eberhard Karls Universität Tübingen, Tübingen, Deutschland
| | - Frank Paulsen
- Universitätsklinik für Radioonkologie, Universitätsklinikum Tübingen, Eberhard Karls Universität Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Deutschland
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