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Villegas F, Dal Bello R, Alvarez-Andres E, Dhont J, Janssen T, Milan L, Robert C, Salagean GAM, Tejedor N, Trnková P, Fusella M, Placidi L, Cusumano D. Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy. Radiother Oncol 2024; 198:110387. [PMID: 38885905 DOI: 10.1016/j.radonc.2024.110387] [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: 10/29/2023] [Revised: 06/13/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024]
Abstract
Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.
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Affiliation(s)
- Fernanda Villegas
- Department of Oncology-Pathology, Karolinska Institute, Solna, Sweden; Radiotherapy Physics and Engineering, Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Emilie Alvarez-Andres
- OncoRay - National Center for Radiation Research in Oncology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Tomas Janssen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Lisa Milan
- Medical Physics Unit, Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Charlotte Robert
- UMR 1030 Molecular Radiotherapy and Therapeutic Innovations, ImmunoRadAI, Paris-Saclay University, Institut Gustave Roussy, Inserm, Villejuif, France; Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Ghizela-Ana-Maria Salagean
- Faculty of Physics, Babes-Bolyai University, Cluj-Napoca, Romania; Department of Radiation Oncology, TopMed Medical Centre, Targu Mures, Romania
| | - Natalia Tejedor
- Department of Medical Physics and Radiation Protection, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Petra Trnková
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Rome, Italy.
| | - Davide Cusumano
- Mater Olbia Hospital, Strada Statale Orientale Sarda 125, Olbia, Sassari, Italy
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Grigo J, Szkitsak J, Höfler D, Fietkau R, Putz F, Bert C. "sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy. Radiat Oncol 2024; 19:33. [PMID: 38459584 PMCID: PMC10924348 DOI: 10.1186/s13014-024-02428-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/29/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data. METHODS A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery. DISCUSSION Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow. TRIAL REGISTRATION NCT06106997.
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Affiliation(s)
- Johanna Grigo
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Juliane Szkitsak
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Daniel Höfler
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
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Dornisch AM, Zhong AY, Poon DMC, Tree AC, Seibert TM. Focal radiotherapy boost to MR-visible tumor for prostate cancer: a systematic review. World J Urol 2024; 42:56. [PMID: 38244059 PMCID: PMC10799816 DOI: 10.1007/s00345-023-04745-w] [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: 09/14/2023] [Accepted: 10/30/2023] [Indexed: 01/22/2024] Open
Abstract
PURPOSE The FLAME trial provides strong evidence that MR-guided external beam radiation therapy (EBRT) focal boost for localized prostate cancer increases biochemical disease-free survival (bDFS) without increasing toxicity. Yet, there are many barriers to implementation of focal boost. Our objectives are to systemically review clinical outcomes for MR-guided EBRT focal boost and to consider approaches to increase implementation of this technique. METHODS We conducted literature searches in four databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. We included prospective phase II/III trials of patients with localized prostate cancer underdoing definitive EBRT with MR-guided focal boost. The outcomes of interest were bDFS and acute/late gastrointestinal and genitourinary toxicity. RESULTS Seven studies were included. All studies had a median follow-up of greater than 4 years. There were heterogeneities in fractionation, treatment planning, and delivery. Studies demonstrated effectiveness, feasibility, and tolerability of focal boost. Based on the Phoenix criteria for biochemical recurrence, the reported 5-year biochemical recurrence-free survival rates ranged 69.7-100% across included studies. All studies reported good safety profiles. The reported ranges of acute/late grade 3 + gastrointestinal toxicities were 0%/1-10%. The reported ranges of acute/late grade 3 + genitourinary toxicities were 0-13%/0-5.6%. CONCLUSIONS There is strong evidence that it is possible to improve oncologic outcomes without substantially increasing toxicity through MR-guided focal boost, at least in the setting of a 35-fraction radiotherapy regimen. Barriers to clinical practice implementation are addressable through additional investigation and new technologies.
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Affiliation(s)
- Anna M Dornisch
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Allison Y Zhong
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, CA, USA
- University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium and Hospital, Happy Valley, Hong Kong, Special Administrative Region of China
| | - Alison C Tree
- The Royal Marsden NHS Foundation Trust, Sutton, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, Sutton, UK
| | - Tyler M Seibert
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, CA, USA.
- Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, CA, USA.
- Department of Radiology, UC San Diego School of Medicine, La Jolla, CA, USA.
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4
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Courtney PT, Valle LF, Raldow AC, Steinberg ML. MRI-Guided Radiation Therapy-An Emerging and Disruptive Process of Care: Healthcare Economic and Policy Considerations. Semin Radiat Oncol 2024; 34:4-13. [PMID: 38105092 DOI: 10.1016/j.semradonc.2023.10.014] [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
MRI-guided radiation therapy (MRgRT) is an emerging, innovative technology that provides opportunities to transform and improve the current clinical care process in radiation oncology. As with many new technologies in radiation oncology, careful evaluation from a healthcare economic and policy perspective is required for its successful implementation. In this review article, we describe the current evidence surrounding MRgRT, framing it within the context of value within the healthcare system. Additionally, we highlight areas in which MRgRT may disrupt the current process of care, and discuss the evidence thresholds and timeline required for the widespread adoption of this promising technology.
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Affiliation(s)
- P Travis Courtney
- Department of Radiation Oncology, University of California, Los Angeles, CA
| | - Luca F Valle
- Department of Radiation Oncology, University of California, Los Angeles, CA
| | - Ann C Raldow
- Department of Radiation Oncology, University of California, Los Angeles, CA
| | - Michael L Steinberg
- Department of Radiation Oncology, University of California, Los Angeles, CA.
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Olsson LE, Af Wetterstedt S, Scherman J, Gunnlaugsson A, Persson E, Jamtheim Gustafsson C. Evaluation of a deep learning magnetic resonance imaging reconstruction method for synthetic computed tomography generation in prostate radiotherapy. Phys Imaging Radiat Oncol 2024; 29:100557. [PMID: 38414521 PMCID: PMC10897922 DOI: 10.1016/j.phro.2024.100557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/29/2024] Open
Abstract
Background and Purpose In magnetic resonance imaging (MRI) only radiotherapy computed tomography (CT) is excluded. The method relies entirely on synthetic CT images generated from MRI. This study evaluates the compatibility of a commercial synthetic CT (sCT) with an accelerated commercial deep learning reconstruction (DLR) in MRI-only prostate radiotherapy. Materials and Methods For a group of 24 patients (cohort 1) the effects of DLR were studied in isolation. MRI data were reconstructed conventionally and with DLR from identical k-space data, and sCTs were generated for both reconstructions. The sCT quality, Hounsfield Unit (HU) and dosimetric impact were investigated. In another group of 15 patients (cohort 2) effects on sCT generation using accelerated MRI acquisition (40 % time reduction) reconstructed with DLR were investigated. Results sCT images from both cohorts, generated from DLR MRI data, were of clinically expected image quality. The mean dose differences for targets and organs at risks in cohort 1 were <0.06 Gy, corresponding to a 0.1 % prescribed dose difference. Similar dose differences were observed in cohort 2. Gamma pass rates for cohort 1 were 100 % for criteria 3 %/3mm, 2 %/2mm and 1 %/1mm for all dose levels. Mean error and mean absolute error inside the body, between sCTs, averaged over all cohort 1 subjects, were -1.1 ± 0.6 [-2.4 0.2] and 2.9 ± 0.4 [2.3 3.9] HU, respectively. Conclusions DLR was suitable for sCT generation with clinically negligible differences in HU and calculated dose compared to the conventional MRI reconstruction method. For sCT generation DLR enables scan time reduction, without compromised sCT quality.
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Affiliation(s)
- Lars E Olsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
| | - Sacha Af Wetterstedt
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
| | - Jonas Scherman
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
| | - Adalsteinn Gunnlaugsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
| | - Emilia Persson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
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Autret D, Guillerminet C, Roussel A, Cossec-Kerloc'h E, Dufreneix S. Comparison of four synthetic CT generators for brain and prostate MR-only workflow in radiotherapy. Radiat Oncol 2023; 18:146. [PMID: 37670397 PMCID: PMC10478301 DOI: 10.1186/s13014-023-02336-y] [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: 02/09/2023] [Accepted: 08/23/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND The interest in MR-only workflows is growing with the introduction of artificial intelligence in the synthetic CT generators converting MR images into CT images. The aim of this study was to evaluate several commercially available sCT generators for two anatomical localizations. METHODS Four sCT generators were evaluated: one based on the bulk density method and three based on deep learning methods. The comparison was performed on large patient cohorts (brain: 42 patients and pelvis: 52 patients). It included geometric accuracy with the evaluation of Hounsfield Units (HU) mean error (ME) for several structures like the body, bones and soft tissues. Dose evaluation included metrics like the Dmean ME for bone structures (skull or femoral heads), PTV and soft tissues (brain or bladder or rectum). A 1%/1 mm gamma analysis was also performed. RESULTS HU ME in the body were similar to those reported in the literature. Dmean ME were smaller than 2% for all structures. Mean gamma pass rate down to 78% were observed for the bulk density method in the brain. Performances of the bulk density generator were generally worse than the artificial intelligence generators for the brain but similar for the pelvis. None of the generators performed best in all the metrics studied. CONCLUSIONS All four generators can be used in clinical practice to implement a MR-only workflow but the bulk density method clearly performed worst in the brain.
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Affiliation(s)
| | | | | | | | - Stéphane Dufreneix
- Institut de Cancérologie de l'Ouest, Angers, France.
- CEA, List, Laboratoire National Henri Becquerel (LNE-LNHB), Palaiseau, France.
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Young T, Dowling J, Rai R, Liney G, Greer P, Thwaites D, Holloway L. Clinical validation of MR imaging time reduction for substitute/synthetic CT generation for prostate MRI-only treatment planning. Phys Eng Sci Med 2023; 46:1015-1021. [PMID: 37219797 PMCID: PMC10480277 DOI: 10.1007/s13246-023-01268-x] [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: 10/31/2022] [Accepted: 04/26/2023] [Indexed: 05/24/2023]
Abstract
Radiotherapy treatment planning based only on magnetic resonance imaging (MRI) has become clinically achievable. Though computed tomography (CT) is the gold standard for radiotherapy imaging, directly providing the electron density values needed for planning calculations, MRI has superior soft tissue visualisation to guide treatment planning decisions and optimisation. MRI-only planning removes the need for the CT scan, but requires generation of a substitute/synthetic/pseudo CT (sCT) for electron density information. Shortening the MRI imaging time would improve patient comfort and reduce the likelihood of motion artefacts. A volunteer study was previously carried out to investigate and optimise faster MRI sequences for a hybrid atlas-voxel conversion to sCT for prostate treatment planning. The aim of this follow-on study was to clinically validate the performance of the new optimised sequence for sCT generation in a treated MRI-only prostate patient cohort. 10 patients undergoing MRI-only treatment were scanned on a Siemens Skyra 3T MRI as part of the MRI-only sub-study of the NINJA clinical trial (ACTRN12618001806257). Two sequences were used, the standard 3D T2-weighted SPACE sequence used for sCT conversion which has been previously validated against CT, and a modified fast SPACE sequence, selected based on the volunteer study. Both were used to generate sCT scans. These were then compared to evaluate the fast sequence conversion for anatomical and dosimetric accuracy against the clinically approved treatment plans. The average Mean Absolute Error (MAE) for the body was 14.98 ± 2.35 HU, and for bone was 40.77 ± 5.51 HU. The external volume contour comparison produced a Dice Similarity Coefficient (DSC) of at least 0.976, and an average of 0.985 ± 0.004, and the bony anatomy contour comparison a DSC of at least 0.907, and an average of 0.950 ± 0.018. The fast SPACE sCT agreed with the gold standard sCT within an isocentre dose of -0.28% ± 0.16% and an average gamma pass rate of 99.66% ± 0.41% for a 1%/1 mm gamma tolerance. In this clinical validation study, the fast sequence, which reduced the required imaging time by approximately a factor of 4, produced an sCT with similar clinical dosimetric results compared to the standard sCT, demonstrating its potential for clinical use for treatment planning.
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Affiliation(s)
- Tony Young
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
| | - Jason Dowling
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
- CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Brisbane, QLD Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW Australia
- School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW Australia
| | - Robba Rai
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW Australia
| | - Gary Liney
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW Australia
| | - Peter Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW Australia
- Calvary Mater Newcastle Hospital, Newcastle, NSW Australia
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
| | - Lois Holloway
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW Australia
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He M, Cao Y, Chi C, Yang X, Ramin R, Wang S, Yang G, Mukhtorov O, Zhang L, Kazantsev A, Enikeev M, Hu K. Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives. Front Oncol 2023; 13:1189370. [PMID: 37546423 PMCID: PMC10400334 DOI: 10.3389/fonc.2023.1189370] [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: 03/19/2023] [Accepted: 05/30/2023] [Indexed: 08/08/2023] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
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Affiliation(s)
- Mingze He
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Yu Cao
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Changliang Chi
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
| | - Xinyi Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Rzayev Ramin
- Department of Radiology, The Second University Clinic, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shuowen Wang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Guodong Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Otabek Mukhtorov
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Liqun Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, China
| | - Anton Kazantsev
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Mikhail Enikeev
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Kebang Hu
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
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Scherman J, af Wetterstedt S, Persson E, Olsson LE, Jamtheim Gustafsson C. Geometric impact and dose estimation of on-patient placement of a lightweight receiver coil in a clinical magnetic resonance imaging-only radiotherapy workflow for prostate cancer. Phys Imaging Radiat Oncol 2023; 26:100433. [PMID: 37063614 PMCID: PMC10091035 DOI: 10.1016/j.phro.2023.100433] [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: 10/21/2022] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 04/18/2023] Open
Abstract
Background and Purpose For pelvic magnetic resonance imaging (MRI)-only radiotherapy the use of receiver coil bridges (CB) is recommended to avoid deformation of the patient. Development in coil technology has enabled lightweight, flexible coils. In this work we evaluate the effects of a lightweight coil in a pelvic MRI-only radiotherapy workflow. Materials and Methods Twenty-one patients, referred to prostate MRI-only radiotherapy, were included. Images were acquired with and without CB. Anatomical deformation from the on-patient coil placement was measured in the anterior-posterior (AP) and left-right (LR) direction. The change in signal-to-noise ratio (SNR) was measured in phantom and in vivo.The clinical treatment plan, created on the image with CB, was transferred and recalculated on the image without the CB. Dose metrics for the targets (planning- and clinical target volume) and organs at risks (OAR) were analyzed. Results There was a statistically significant increase in SNR in-vivo (median 21 %, p = 0.002) when removing the CB. Anatomical differences after removing the CB in patients were -1.5 mm in AP (median change) and + 2.5 mm in LR direction. Dosimetric differences for the target structures were clinically negligible, but statistically significant. The difference in target mean doses were 0.2 % (both p = 0.004) of the prescribed dose. No dosimetric differences were observed for the OAR, except for the penile bulb. Conclusions We concluded that anatomical change and dosimetric differences, originating from scanning without CB were minor. The CB can thereby be removed from the workflow, enabling easier patient positioning and increased SNR when using lightweight coils.
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Affiliation(s)
- Jonas Scherman
- Department of Hematology, Oncology, and Radiation Physics Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Corresponding author.
| | - Sacha af Wetterstedt
- Department of Hematology, Oncology, and Radiation Physics Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
| | - Emilia Persson
- Department of Hematology, Oncology, and Radiation Physics Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
| | - Lars E. Olsson
- Department of Hematology, Oncology, and Radiation Physics Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
| | - Christian Jamtheim Gustafsson
- Department of Hematology, Oncology, and Radiation Physics Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
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10
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Persson E, Svanberg N, Scherman J, Jamtheim Gustafsson C, Fridhammar A, Hjalte F, Bäck S, Nilsson P, Gunnlaugsson A, Olsson LE. MRI-only radiotherapy from an economic perspective: Can new techniques in prostate cancer treatment be cost saving? Clin Transl Radiat Oncol 2022; 38:183-187. [DOI: 10.1016/j.ctro.2022.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 10/16/2022] [Accepted: 11/19/2022] [Indexed: 11/23/2022] Open
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11
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Lewis S, Dawson L, Barry A, Stanescu T, Mohamad I, Hosni A. Stereotactic body radiation therapy for hepatocellular carcinoma: from infancy to ongoing maturity. JHEP Rep 2022; 4:100498. [PMID: 35860434 PMCID: PMC9289870 DOI: 10.1016/j.jhepr.2022.100498] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 12/16/2022] Open
Affiliation(s)
- Shirley Lewis
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Canada
| | - Laura Dawson
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Canada
| | - Aisling Barry
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Canada
| | - Teodor Stanescu
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Canada
| | - Issa Mohamad
- Department of Radiation Oncology, King Hussein Cancer Centre, Jordan
| | - Ali Hosni
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Canada
- Corresponding author. Address: Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
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12
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Lerner M, Medin J, Jamtheim Gustafsson C, Alkner S, Olsson LE. Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy. Front Oncol 2022; 11:812643. [PMID: 35083159 PMCID: PMC8784680 DOI: 10.3389/fonc.2021.812643] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/20/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES MRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup. MATERIAL AND METHODS Twenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed. RESULTS Twenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT. CONCLUSION We report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.
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Affiliation(s)
- Minna Lerner
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Joakim Medin
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Medical Radiation Physics, Clinical Sciences, Lund, Lund University, Lund, Sweden
| | - Christian Jamtheim Gustafsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Sara Alkner
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | - Lars E. Olsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
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13
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Kruis MF. Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT. J Appl Clin Med Phys 2021; 23:e13468. [PMID: 34743405 PMCID: PMC8803285 DOI: 10.1002/acm2.13468] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/13/2021] [Accepted: 10/19/2021] [Indexed: 12/11/2022] Open
Abstract
Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT. In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.
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14
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Jamtheim Gustafsson C, Lempart M, Swärd J, Persson E, Nyholm T, Thellenberg Karlsson C, Scherman J. Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy. J Appl Clin Med Phys 2021; 22:51-63. [PMID: 34623738 PMCID: PMC8664152 DOI: 10.1002/acm2.13446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/16/2021] [Accepted: 09/24/2021] [Indexed: 11/12/2022] Open
Abstract
Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop a modality independent deep learning (DL) model for automatic classification and annotation of prostate RT DICOM structures. Delineated prostate organs at risk (OAR), support- and target structures (gross tumor volume [GTV]/clinical target volume [CTV]/planning target volume [PTV]), along with or without separate vesicles and/or lymph nodes, were extracted as binary masks from 1854 patients. An image modality independent 2D InceptionResNetV2 classification network was trained with varying amounts of training data using four image input channels. Channel 1-3 consisted of orthogonal 2D projections from each individual binary structure. The fourth channel contained a summation of the other available binary structure masks. Structure classification performance was assessed in independent CT (n = 200 pat) and magnetic resonance imaging (MRI) (n = 40 pat) test datasets and an external CT (n = 99 pat) dataset from another clinic. A weighted classification accuracy of 99.4% was achieved during training. The unweighted classification accuracy and the weighted average F1 score among different structures in the CT test dataset were 98.8% and 98.4% and 98.6% and 98.5% for the MRI test dataset, respectively. The external CT dataset yielded the corresponding results 98.4% and 98.7% when analyzed for trained structures only, and results from the full dataset yielded 79.6% and 75.2%. Most misclassifications in the external CT dataset occurred due to multiple CTVs and PTVs being fused together, which was not included in the training data. Our proposed DL-based method for automated renaming and standardization of prostate radiotherapy annotations shows great potential. Clinic specific contouring standards however need to be represented in the training data for successful use. Source code is available at https://github.com/jamtheim/DicomRTStructRenamerPublic.
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Affiliation(s)
- Christian Jamtheim Gustafsson
- Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Michael Lempart
- Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Johan Swärd
- Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden
| | - Emilia Persson
- Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Tufve Nyholm
- Department of Radiation Sciences, Radiation Physics, Umeå University, Umeå, Sweden
| | | | - Jonas Scherman
- Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
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15
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Persson E, Emin S, Scherman J, Jamtheim Gustafsson C, Brynolfsson P, Ceberg S, Gunnlaugsson A, Olsson LE. Investigation of the clinical inter-observer bias in prostate fiducial marker image registration between CT and MR images. Radiat Oncol 2021; 16:150. [PMID: 34399806 PMCID: PMC8365967 DOI: 10.1186/s13014-021-01865-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 07/17/2021] [Indexed: 01/17/2023] Open
Abstract
Background and purpose Inter-modality image registration between computed tomography (CT) and magnetic resonance (MR) images is associated with systematic uncertainties and the magnitude of these uncertainties is not well documented.
The purpose of this study was to investigate the potential uncertainty of gold fiducial marker (GFM) registration for localized prostate cancer and to estimate the inter-observer bias in a clinical setting. Methods
Four experienced observers registered CT and MR images for 42 prostate cancer patients. Manual GFM identification was followed by a landmark-based registration. The absolute difference between observers in GFM identification and the displacement of the clinical target volume (CTV) was investigated. The CTV center of mass (CoM) vector displacements, DICE-index and Hausdorff distances for the observer registrations were compared against a clinical baseline registration. The time allocated for the manual registrations was compared. Results Absolute difference in GFM identification between observers ranged from 0.0 to 3.0 mm. The maximum CTV CoM displacement from the clinical baseline was 3.1 mm. Displacements larger than or equal to 1 mm, 2 mm and 3 mm were 46%, 18% and 4%, respectively. No statistically significant difference was detected between observers in terms of CTV displacement. Median DICE-index and Hausdorff distance for the CTV, with their respective ranges were 0.94 [0.70–1.00] and 2.5 mm [0.7–8.7]. Conclusions Registration of CT and MR images using GFMs for localized prostate cancer patients was subject to inter-observer bias on an individual patient level. A CTV displacement as large as 3 mm occurred for individual patients. These results show that GFM registration in a clinical setting is associated with uncertainties, which motivates the removal of inter-modality registrations in the radiotherapy workflow and a transition to an MRI-only workflow for localized prostate cancer.
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Affiliation(s)
- Emilia Persson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics , Skåne University Hospital, Klinikgatan 5, 221 85, Lund, Sweden. .,Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurellsgata 9, 205 02, Malmö, Sweden.
| | - Sevgi Emin
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics , Skåne University Hospital, Klinikgatan 5, 221 85, Lund, Sweden
| | - Jonas Scherman
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics , Skåne University Hospital, Klinikgatan 5, 221 85, Lund, Sweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics , Skåne University Hospital, Klinikgatan 5, 221 85, Lund, Sweden.,Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurellsgata 9, 205 02, Malmö, Sweden
| | - Patrik Brynolfsson
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurellsgata 9, 205 02, Malmö, Sweden
| | - Sofie Ceberg
- Department of Medical Radiation Physics, Lund University, Barngatan 4, 222 85, Lund, Sweden
| | - Adalsteinn Gunnlaugsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics , Skåne University Hospital, Klinikgatan 5, 221 85, Lund, Sweden
| | - Lars E Olsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics , Skåne University Hospital, Klinikgatan 5, 221 85, Lund, Sweden.,Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurellsgata 9, 205 02, Malmö, Sweden
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16
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Ahangari S, Hansen NL, Olin AB, Nøttrup TJ, Ryssel H, Berthelsen AK, Löfgren J, Loft A, Vogelius IR, Schnack T, Jakoby B, Kjaer A, Andersen FL, Fischer BM, Hansen AE. Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients. Acta Oncol 2021; 60:1045-1053. [PMID: 34107847 DOI: 10.1080/0284186x.2021.1936164] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Radiotherapy (RT) planning for cervical cancer patients entails the acquisition of both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Further, molecular imaging by Positron Emission Tomography (PET) could contribute to target volume delineation as well as treatment response monitoring. The objective of this study was to investigate the feasibility of a PET/MRI-only RT planning workflow of patients with cervical cancer. This includes attenuation correction (AC) of MRI hardware and dedicated positioning equipment as well as evaluating MRI-derived synthetic CT (sCT) of the pelvic region for positioning verification and dose calculation to enable a PET/MRI-only setup. MATERIAL AND METHODS 16 patients underwent PET/MRI using a dedicated RT setup after the routine CT (or PET/CT), including eight pilot patients and eight cervical cancer patients who were subsequently referred for RT. Data from 18 patients with gynecological cancer were added for training a deep convolutional neural network to generate sCT from Dixon MRI. The mean absolute difference between the dose distributions calculated on sCT and a reference CT was measured in the RT target volume and organs at risk. PET AC by sCT and a reference CT were compared in the tumor volume. RESULTS All patients completed the examination. sCT was inferred for each patient in less than 5 s. The dosimetric analysis of the sCT-based dose planning showed a mean absolute error (MAE) of 0.17 ± 0.12 Gy inside the planning target volumes (PTV). PET images reconstructed with sCT and CT had no significant difference in quantification for all patients. CONCLUSIONS These results suggest that multiparametric PET/MRI can be successfully integrated as a one-stop-shop in the RT workflow of patients with cervical cancer.
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Affiliation(s)
- Sahar Ahangari
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Naja Liv Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anders Beck Olin
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Trine Jakobi Nøttrup
- Department of Oncology, Section of Radiotherapy, University of Copenhagen, Rigshospitalet, Denmark
| | - Heidi Ryssel
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anne Kiil Berthelsen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Johan Löfgren
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Annika Loft
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Richter Vogelius
- Department of Oncology, Section of Radiotherapy, University of Copenhagen, Rigshospitalet, Denmark
| | - Tine Schnack
- Department of Gynecology, University of Copenhagen, Copenhagen, Denmark
- Department of Gynecology and Obstetrics, Odense University Hospital, Odense, Denmark
| | | | - Andreas Kjaer
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Cluster for Molecular Imaging, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- The PET Centre, School of Biomedical Engineering and Imaging Sciences, Kings College London, St Thomas’ Hospital, London, UK
| | - Adam Espe Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Diagnostic Radiology, Rigshospitalet, University of Copenhagen, Denmark Copenhagen
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17
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Young T, Dowling J, Rai R, Liney G, Greer P, Thwaites D, Holloway L. Effects of MR imaging time reduction on substitute CT generation for prostate MRI-only treatment planning. Phys Eng Sci Med 2021; 44:799-807. [PMID: 34228255 DOI: 10.1007/s13246-021-01031-0] [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: 11/19/2020] [Accepted: 06/24/2021] [Indexed: 11/25/2022]
Abstract
The introduction of MRI linear accelerators (MR-linacs) and the increased use of MR imaging in radiotherapy, requires improved approaches to MRI-only radiotherapy. MRI provides excellent soft tissue visualisation but does not provide any electron density information required for radiotherapy dose calculation, instead MRI is registered to CT images to enable dose calculations. MRI-only radiotherapy eliminates registration errors and reduces patient discomfort, workload and cost. Electron density requirements may be addressed in different ways, from manually applying bulk density corrections, to more computationally intensive methods to produce substitute CT datasets (sCT), requiring additional sequences, increasing overall imaging time. Reducing MR imaging time would reduce potential artefacts from intrafraction motion and patient discomfort. The aim of this study was to assess the impact of reducing MR imaging time on a hybrid atlas-voxel sCT conversion for prostate MRI-only treatment planning, considering both anatomical and dosimetric parameters. 10 volunteers were scanned on a Siemens Skyra 3T MRI. Sequences included the 3D T2-weighted (T2-w) SPACE sequence used for sCT conversion as previously validated against CT, along with variations to this sequence in repetition time (TR), turbo factor, and combinations of these to reduce the imaging time. All scans were converted to sCT and were compared to the sCT from the original SPACE sequence, evaluating for anatomical changes and dosimetric differences for a standard prostate VMAT plan. Compared to the previously validated T2-w SPACE sequence, scan times were reduced by up to 80%. The external volume and bony anatomy were compared, with all but one sequence meeting a DICE coefficient of 0.9 or better, with the largest variations occurring at the edges of the external body volume. The generated sCT agreed with the gold standard sCT within an isocentre dose of 1% and a gamma pass rate of 99% for a 1%/1 mm gamma tolerance for all but one sequence. This study demonstrates that the MR imaging sequence time was able to be reduced by approximately 80% with similar dosimetric results.
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Affiliation(s)
- Tony Young
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, NSW, Australia. .,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia.
| | - Jason Dowling
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia.,CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Brisbane, QLD, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia.,School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Robba Rai
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, NSW, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Gary Liney
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, NSW, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Peter Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia.,Calvary Mater Newcastle Hospital, Newcastle, NSW, Australia
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Lois Holloway
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Sydney, NSW, Australia.,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
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18
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Edmund JM, Andreasen D, Van Leemput K. Cone beam computed tomography based image guidance and quality assessment of prostate cancer for magnetic resonance imaging-only radiotherapy in the pelvis. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 18:55-60. [PMID: 34258409 PMCID: PMC8254192 DOI: 10.1016/j.phro.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/23/2021] [Accepted: 05/04/2021] [Indexed: 12/22/2022]
Abstract
MRI-only IGRT accuracy is ≤2 mm as compared to CT but significant differences were observed. MRI-only CBCT-based IGRT seems feasible but caution is advised. The median absolute error (MeAE) for independent verification on the sCT quality is proposed. A MeAE around 0.1 in mass density could call for sCT quality inspection.
Background and purpose Radiotherapy (RT) based on magentic resonance imaging (MRI) only is currently used clinically in the pelvis. A synthetic computed tomography (sCT) is needed for dose planning. Here, we investigate the accuracy of cone beam CT (CBCT) based MRI-only image guided RT (IGRT) and sCT image quality. Materials and methods CT, MRI and CBCT scans of ten prostate cancer patients were included. The MRI was converted to a sCT using a multi-atlas approach. The sCT, CT and MR images were auto-matched with the CBCT on the bony anatomy. Paired sCT-CT and sCT-CBCT data were created. CT numbers were converted to relative electron (RED) and mass densities (DES) using a standard calibration curve for the CT and sCT. For the CBCT RED/DES conversion, a phantom and paired CT-CBCT population based calibration curve was used. For the latter, the CBCT numbers were averaged in 100 HU bins and the known RED/DES of the CT were assigned. The paired sCT-CT and sCT-CBCT data were averaged in bins of 10 HU or 0.01 RED/DES. The median absolute error (MeAE) between the sCT-CT and sCT-CBCT bins was calculated. Wilcoxon rank-sum tests were carried out for the IGRT and MeAE study. Results The mean sCT or MR IGRT difference from CT was ≤ 2 mm but significant differences were observed. A CBCT HU or phantom-based RED/DES MeAE did not estimate the sCT quality similar to a CT based MeAE but the CBCT population-based RED/DES MeAE did. Conclusions MRI-only CBCT-based IGRT seems feasible but caution is advised. A MeAE around 0.1 DES could call for sCT quality inspection.
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Affiliation(s)
- Jens M Edmund
- Radiotherapy Research Unit, Department of Oncology, Gentofte and Herlev Hospital, University of Copenhagen, 2730 Herlev, Denmark.,Niels Bohr Institute, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Daniel Andreasen
- Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Koen Van Leemput
- Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark.,Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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19
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Ip WY, Yeung FK, Yung SPF, Yu HCJ, So TH, Vardhanabhuti V. Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Artif Intell Med Imaging 2021; 2:37-55. [DOI: 10.35711/aimi.v2.i2.37] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/01/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has seen tremendous growth over the past decade and stands to disrupts the medical industry. In medicine, this has been applied in medical imaging and other digitised medical disciplines, but in more traditional fields like medical physics, the adoption of AI is still at an early stage. Though AI is anticipated to be better than human in certain tasks, with the rapid growth of AI, there is increasing concerns for its usage. The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Topics on AI for image acquisition, image segmentation, treatment delivery, quality assurance and outcome prediction will be explored as well as the interaction between human and AI. This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.
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Affiliation(s)
- Wing-Yan Ip
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Fu-Ki Yeung
- Medical Physics and Research Department, The Hong Kong Sanitorium & Hospital, Hong Kong SAR, China and Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shang-Peng Felix Yung
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | | | - Tsz-Him So
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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20
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Gustafsson CJ, Swärd J, Adalbjörnsson SI, Jakobsson A, Olsson LE. Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow. Phys Med Biol 2020; 65:225011. [PMID: 33179610 DOI: 10.1088/1361-6560/abb0f9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Identification of prostate gold fiducial markers in magnetic resonance imaging (MRI) images is challenging when CT images are not available, due to misclassifications from intra-prostatic calcifications. It is also a time consuming task and automated identification methods have been suggested as an improvement for both objectives. Multi-echo gradient echo (MEGRE) images have been utilized for manual fiducial identification with 100% detection accuracy. The aim is therefore to develop an automatic deep learning based method for fiducial identification in MRI images intended for MRI-only prostate radiotherapy. MEGRE images from 326 prostate cancer patients with fiducials were acquired on a 3T MRI, post-processed with N4 bias correction, and the fiducial center of mass (CoM) was identified. A 9 mm radius sphere was created around the CoM as ground truth. A deep learning HighRes3DNet model for semantic segmentation was trained using image augmentation. The model was applied to 39 MRI-only patients and 3D probability maps for fiducial location and segmentation were produced and spatially smoothed. In each of the three largest probability peaks, a 9 mm radius sphere was defined. Detection sensitivity and geometric accuracy was assessed. To raise awareness of potential false findings a 'BeAware' score was developed, calculated from the total number and quality of the probability peaks. All datasets, annotations and source code used were made publicly available. The detection sensitivity for all fiducials were 97.4%. Thirty-six out of thirty-nine patients had all fiducial markers correctly identified. All three failed patients generated a user notification using the BeAware score. The mean absolute difference between the detected fiducial and ground truth CoM was 0.7 ± 0.9 [0 3.1] mm. A deep learning method for automatic fiducial identification in MRI images was developed and evaluated with state-of-the-art results. The BeAware score has the potential to notify the user regarding patients where the proposed method is uncertain.
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Affiliation(s)
- Christian Jamtheim Gustafsson
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden. Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden
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Tyagi N, Zelefsky MJ, Wibmer A, Zakian K, Burleson S, Happersett L, Halkola A, Kadbi M, Hunt M. Clinical experience and workflow challenges with magnetic resonance-only radiation therapy simulation and planning for prostate cancer. Phys Imaging Radiat Oncol 2020; 16:43-49. [PMID: 33134566 PMCID: PMC7598055 DOI: 10.1016/j.phro.2020.09.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 08/24/2020] [Accepted: 09/25/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Magnetic Resonance (MR)-only planning has been implemented clinically for radiotherapy of prostate cancer. However, fewer studies exist regarding the overall success rate of MR-only workflows. We report on successes and challenges of implementing MR-only workflows for prostate. MATERIALS AND METHODS A total of 585 patients with prostate cancer underwent an MR-only simulation and planning between 06/2016-06/2018. MR simulation included images for contouring, synthetic-CT generation and fiducial identification. Workflow interruptions occurred that required a backup CT, a re-simulation or an update to our current quality assurance (QA) process. The challenges were prospectively evaluated and classified into syn-CT generation, motion/artifacts in the MRs, fiducial QA and bowel preparation guidelines. RESULTS MR-only simulation was successful in 544 (93.2 %) patients. . In seventeen patients (2.9%), reconstruction of synthetic-CT failed due to patient size, femur angulation, or failure to determine the body contour. Twenty-four patients (4.1%) underwent a repeat/backup CT scan because of artifacts on the MR such as image blur due to patient motion or biopsy/surgical artifacts that hampered identification of the implanted fiducial markers. In patients requiring large coverage due to nodal involvement, inhomogeneity artifacts were resolved by using a two-stack acquisition and adaptive inhomogeneity correction. Bowel preparation guidelines were modified to address frequent rectum/gas issues due to longer MR scan time. CONCLUSIONS MR-only simulation has been successfully implemented for a majority of patients in the clinic. However, MR-CT or CT-only pathway may still be needed for patients where MR-only solution fails or patients with MR contraindications.
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Affiliation(s)
- Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Michael J. Zelefsky
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Andreas Wibmer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Kristen Zakian
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Sarah Burleson
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Laura Happersett
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Aleksi Halkola
- Philips Healthcare, 595 Milner Road, Cleveland, OH 44143, United States
| | - Mo Kadbi
- Philips Healthcare, 595 Milner Road, Cleveland, OH 44143, United States
| | - Margie Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
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Lindberg J, Holmström P, Hallberg S, Björk-Eriksson T, Olsson CE. A national perspective about the current work situation at modern radiotherapy departments. Clin Transl Radiat Oncol 2020; 24:127-134. [PMID: 32875126 PMCID: PMC7451821 DOI: 10.1016/j.ctro.2020.08.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 08/05/2020] [Accepted: 08/09/2020] [Indexed: 10/27/2022] Open
Abstract
Background The radiotherapy (RT) community faces great challenges to meet the growing cancer incidence, especially regarding workload and recruitment of personnel. Workflow-related issues affect involved professions differently since they have specific expertise and various roles in the workflow. To obtain an objective understanding of the current working situation and identify workflow bottle necks in RT, we conducted a national survey on this topic in 2018. Materials and Methods All 17 (photon-based) RT departments in Sweden were invited to participate in the study, which targeted both managers and employees in RT. Descriptive statistics were calculated for each profession and for small, medium and large departments (2/3-4/≥5 linacs). Results Altogether, 364 filled-in questionnaires were returned (32/332 managers/employees; 94% response rate). Managers reported a general need for more staff (all professions). Small departments reported no problems with waiting times (0/3); whereas 2/3 of medium and large departments did (medium: 5/8, large: 2/3). All professions had a positive attitude towards working in RT (mean = 86%, 0/100%=negative/positive attitude). Organizational issues were ranked highest among reoccurring events that were most frustrating/had most negative effect on the work environment. The most severe workflow-related problems were reported to originate at contouring. Conclusion Future efforts to improve the modern RT workflow need to focus on how to make positive mechanisms at small departments useful in larger settings. Our data also reveal that strong leadership and improved routines at contouring are warranted by all RT professions to reduce frustration related to organizational issues and to increase work effectivity.
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Affiliation(s)
- Jesper Lindberg
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.,Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
| | - Paul Holmström
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Stefan Hallberg
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
| | - Thomas Björk-Eriksson
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden.,Department of Oncology, Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Caroline E Olsson
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
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Mannerberg A, Persson E, Jonsson J, Gustafsson CJ, Gunnlaugsson A, Olsson LE, Ceberg S. Dosimetric effects of adaptive prostate cancer radiotherapy in an MR-linac workflow. Radiat Oncol 2020; 15:168. [PMID: 32650811 PMCID: PMC7350593 DOI: 10.1186/s13014-020-01604-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 06/23/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The purpose was to evaluate the dosimetric effects in prostate cancer treatment caused by anatomical changes occurring during the time frame of adaptive replanning in a magnetic resonance linear accelerator (MR-linac) workflow. METHODS Two MR images (MR1 and MR2) were acquired with 30 min apart for each of the 35 patients enrolled in this study. The clinical target volume (CTV) and organs at risk (OARs) were delineated based on MR1. Using a synthetic CT (sCT), ultra-hypofractionated VMAT treatment plans were created for MR1, with three different planning target volume (PTV) margins of 7 mm, 5 mm and 3 mm. The three treatment plans of MR1, were recalculated onto MR2 using its corresponding sCT. The dose distribution of MR2 represented delivered dose to the patient after 30 min of adaptive replanning, omitting motion correction before beam on. MR2 was registered to MR1, using deformable registration. Using the inverse deformation, the structures of MR1 was deformed to fit MR2 and anatomical changes were quantified. For dose distribution comparison the dose distribution of MR2 was warped to the geometry MR1. RESULTS The mean center of mass vector offset for the CTV was 1.92 mm [0.13 - 9.79 mm]. Bladder volume increase ranged from 12.4 to 133.0% and rectum volume difference varied between -10.9 and 38.8%. Using the conventional 7 mm planning target volume (PTV) margin the dose reduction to the CTV was 1.1%. Corresponding values for 5 mm and 3 mm PTV margin were 2.0% and 4.2% respectively. The dose to the PTV and OARs also decreased from D1 to D2, for all PTV margins evaluated. Statistically significant difference was found for CTV Dmin between D1 and D2 for the 3 mm PTV margin (p < 0.01). CONCLUSIONS A target underdosage caused by anatomical changes occurring during the reported time frame for adaptive replanning MR-linac workflows was found. Volume changes in both bladder and rectum caused large prostate displacements. This indicates the importance of thorough position verification before treatment delivery and that the workflow needs to speed up before introducing margin reduction.
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Affiliation(s)
- Annika Mannerberg
- Department of Medical Radiation Physics, Lund University, Lund, Sweden.
| | - Emilia Persson
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Christian Jamtheim Gustafsson
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Adalsteinn Gunnlaugsson
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Lars E Olsson
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Sofie Ceberg
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
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