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Behzadipour M, Palta J, Ma T, Yuan L, Kim S, Kirby S, Torkelson L, Baker J, Koenig T, Khalifa MA, Hawranko R, Richeson D, Fields E, Weiss E, Song WY. Optimization of treatment workflow for 0.35T MR-Linac system. J Appl Clin Med Phys 2024; 25:e14393. [PMID: 38742819 PMCID: PMC11302807 DOI: 10.1002/acm2.14393] [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: 12/06/2023] [Revised: 03/15/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
PURPOSE This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.
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Affiliation(s)
- Mojtaba Behzadipour
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Jatinder Palta
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tianjun Ma
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Lulin Yuan
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Siyong Kim
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Suzanne Kirby
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Laurel Torkelson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - James Baker
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tammy Koenig
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Mateb Al Khalifa
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Robert Hawranko
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Dylan Richeson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Emma Fields
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Elisabeth Weiss
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - William Y. Song
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
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Lutsik N, Nejad-Davarani SP, Valderrama A, Herr J, Maziero D, Cullison K, Azzam GA, Kubicek G, Meshman J, de la Fuente MI, Armstrong T, Mellon EA. Validation of daily 0.35 T diffusion-weighted MRI for MRI-guided glioblastoma radiotherapy. Med Phys 2024; 51:5386-5398. [PMID: 38588475 PMCID: PMC11321942 DOI: 10.1002/mp.17067] [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: 10/03/2023] [Revised: 02/21/2024] [Accepted: 03/27/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND MRI-Linac systems enable daily diffusion-weighed imaging (DWI) MRI scans for assessing glioblastoma tumor changes with radiotherapy treatment. PURPOSE Our study assessed the image quality of echoplanar imaging (EPI)-DWI scans compared with turbo spin echo (TSE)-DWI scans at 0.35 Tesla (T) and compared the apparent diffusion coefficient (ADC) values and distortion of EPI-DWI on 0.35 T MRI-Linac compared to high-field diagnostic MRI scanners. METHODS The calibrated National Institute of Standards and Technology (NIST)/Quantitative Imaging Biomarkers Alliance (QIBA) Diffusion Phantom was scanned on a 0.35 T MRI-Linac, and 1.5 T and 3 T MRI with EPI-DWI. Five patients were scanned on a 0.35 T MRI-Linac with a TSE-DWI sequence, and five other patients were scanned with EPI-DWI on a 0.35 T MRI-Linac and a 3 T MRI. The quality of images was compared between the TSE-DWI and EPI-DWI on the 0.35 T MRI-Linac assessing signal-to-noise ratios and presence of artifacts. EPI-DWI ADC values and distortion magnitude were measured and compared between 0.35 T MRI-Linac and high-field MRI for both phantom and patient studies. RESULTS The average ADC differences between EPI-DWI acquired on the 0.35 T MRI-Linac, 1.5 T and 3 T MRI scanners and published references in the phantom study were 1.7%, 0.4% and 1.0%, respectively. Comparing the ADC values based on EPI-DWI in glioblastoma tumors, there was a 3.36% difference between 0.35 and 3 T measurements. Susceptibility-induced distortions in the EPI-DWI phantoms were 0.46 ± 1.51 mm for 0.35 MRI-Linac, 0.98 ± 0.51 mm for 1.5 T MRI and 1.14 ± 1.88 mm for 3 T MRI; for patients -0.47 ± 0.78 mm for 0.35 T and 1.73 ± 2.11 mm for 3 T MRIs. The mean deformable registration distortion for a phantom was 1.1 ± 0.22 mm, 3.5 ± 0.39 mm and 4.7 ± 0.37 mm for the 0.35 T MRI-Linac, 1.5 T MRI, and 3 T MRI scanners, respectively; for patients this distortion was -0.46 ± 0.57 mm for 0.35 T and 4.2 ± 0.41 mm for 3 T. EPI-DWI 0.35 T MRI-Linac images showed higher SNR and lack of artifacts compared with TSE-DWI, especially at higher b-values up to 1000 s/mm2. CONCLUSION EPI-DWI on a 0.35 T MRI-Linac showed superior image quality compared with TSE-DWI, minor and less distortions than high-field diagnostic scanners, and comparable ADC values in phantoms and glioblastoma tumors. EPI-DWI should be investigated on the 0.35 T MRI-Linac for prediction of early response in patients with glioblastoma.
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Affiliation(s)
- Natalia Lutsik
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Siamak P. Nejad-Davarani
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Alessandro Valderrama
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Janette Herr
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, La Jolla, CA 92093
| | - Kaylie Cullison
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Gregory A. Azzam
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Gregory Kubicek
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Jessica Meshman
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
| | - Macarena I. de la Fuente
- Neuro-Oncology division, University of Miami Miller School of Medicine, 1150 NW 14th St, Miami, FL 33136
| | - Tess Armstrong
- former ViewRay, Inc., 2 Thermo Fisher Way, Oakwood Village, Ohio 44146
| | - Eric A. Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, 1475 NW 12 Ave, Miami, Fl 33136
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Wallimann P, Piccirelli M, Nowakowska S, Armstrong T, Mayinger M, Boss A, Bink A, Guckenberger M, Tanadini-Lang S, Andratschke N, Pouymayou B. Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac. Phys Imaging Radiat Oncol 2024; 30:100579. [PMID: 38707628 PMCID: PMC11068927 DOI: 10.1016/j.phro.2024.100579] [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: 12/05/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Background and Purpose The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and Methods Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. Results Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. Conclusion Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.
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Affiliation(s)
- Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marco Piccirelli
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Sylwia Nowakowska
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Tess Armstrong
- ViewRay Inc., 2 Thermo Fisher Way, Oakwood Village, OH 44146, USA
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andreas Boss
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Maziero D, Azzam GA, de La Fuente M, Stoyanova R, Ford JC, Mellon EA. Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system. Phys Med 2024; 119:103316. [PMID: 38340693 DOI: 10.1016/j.ejmp.2024.103316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 11/29/2023] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
PURPOSE MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35 T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3D-T1w) and dynamic contrast-enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35 T MRI-Linac. METHODS AND MATERIALS The protocol implemented was used to acquire 3D-T1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35 T MRI-Linac. The detection of post-contrast-enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35 T MRI-Linac to images obtained using a 3 T scanner. The DCE data were tested temporally and spatially using data from a flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes. RESULTS The 3D-T1w contrast-enhancement volumes were visually and volumetrically similar between 0.35 T MRI-Linac and 3 T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54 % decrease and 8.6 % increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. CONCLUSION Our findings support the feasibility of obtaining post-contrast 3D-T1w and DCE data from patients with glioblastoma using a 0.35 T MRI-Linac system.
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Affiliation(s)
- Danilo Maziero
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, La Jolla, CA 92093, United States; Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States.
| | - Gregory Albert Azzam
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - Macarena de La Fuente
- Department of Neurology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - John Chetley Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
| | - Eric Albert Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
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Sung J, Choi Y, Kim JW, Lee H. Dose Calculation Accuracy of Beam Models in RadCalc for a 1.5 T MR-Linac. Cancers (Basel) 2024; 16:526. [PMID: 38339277 PMCID: PMC10854935 DOI: 10.3390/cancers16030526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/18/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
The purpose of this study is to evaluate RadCalc, an independent dose verification software, for patient-specific quality assurance (PSQA) in online adaptive planning with a magnetic resonance linear accelerator (MR-linac) of a 1.5 T. Version 7.1.4 of RadCalc to introduce the capability to establish a beam model that incorporates MR field characteristics. A total of six models were established, with one using manufacturer-provided data and the others differing in percentage depth dose (PDD) data sources. Overall, two models utilized PDD data from the treatment planning system (TPS), and three used commissioned PDD data from gantry angles of 0° and 270°. Simple tests on a virtual water phantom assessed dose-calculation accuracy, revealing percentage differences ranging from -0.5% to -20.6%. Excluding models with significant differences, clinical tests on 575 adaptive plans (prostate, liver, and breast) showed percentage differences of -0.51%, 1.12%, and 4.10%, respectively. The doses calculated using RadCalc demonstrated similar trends to those of the PSQA-based measurements. The newly released version of RadCalc enables beam modeling that considers the characteristics of the 1.5 T magnetic field. The accuracy of the software in calculating doses at 1.5 T magnetic fields has been verified, thereby making it a reliable and effective tool for PSQA in adaptive plans.
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Affiliation(s)
- Jiwon Sung
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea; (J.S.); (Y.C.); (J.W.K.)
| | - Yeonho Choi
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea; (J.S.); (Y.C.); (J.W.K.)
| | - Jun Won Kim
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea; (J.S.); (Y.C.); (J.W.K.)
| | - Ho Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
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Moats E, Siddiqui MS, Parikh P, Snyder KC. Dosimetric comparison of glioblastoma radiotherapy treatment plans on a low-field MRI-guided linear accelerator compared to conventional C-arm linear accelerator. Med Dosim 2024; 49:215-221. [PMID: 38233319 DOI: 10.1016/j.meddos.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/29/2023] [Indexed: 01/19/2024]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI)-guided radiation therapy has proven to provide many benefits such as real-time tracking, dose escalation, and the ability to perform online adaptive therapy. The objective of this study is to compare curative treatment plans for glioblastoma tumors on a low-field MR-guided linac vs a C-arm linac and evaluate if they are comparable in terms of coverage, organ at risk sparing, delivery time, and deliverability. METHODS This is a retrospective study that consisted of 15 previously treated patients who received radiation therapy for glioblastoma on a C-arm linac. The CT simulation data used for the original clinical plans was imported into the MR-linac treatment planning system (TPS) and utilized for MR-linac plan generation. The plans were evaluated utilizing the dose volumetric histogram (DVH) and isodose lines, then compared in terms of plan quality consisting of PTV coverage, dose distributions, and OAR constraints. Statistical analysis was performed to compare differences between the two planning techniques. QA was performed on a subset of the plans to verify deliverability. RESULTS Plans generated on the MR-linac were more heterogenous compared to C-arm linac plans. A statistically significant difference was found in the homogeneity index (HI) and the PTV V105% volume (cc) values. The volume of the normal brain receiving 30 Gy also showed a statistically significant (p = 0.0479) difference, where on average an additional 41.5 cc's of the normal brain tissue received 30 Gy in the MR-linac plans. The maximum dose to the normal brain structure also increased in the MR-linac plans on average by 2.6 Gy (p = 0.0002). Similarly, the average maximum dose to the scalp 4 mm structure was 6.5 Gy higher in the MR-linac plans compared to C-arm linac plans (p = 0.0103). The total MU's were higher in the MR-linac plans compared to the C-arm linac plans (p = 0.0015). CONCLUSIONS Both MR-linac and C-arm linac plans met constraints for PTV coverage and OAR sparing, were deliverable, and resulted to be clinically acceptable. However, our study showed that MR-linac plans were not as conformal or as homogenous as C-arm linac plans utilizing noncoplanar beams.
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Ocanto A, Torres L, Montijano M, Rincón D, Fernández C, Sevilla B, Gonsalves D, Teja M, Guijarro M, Glaría L, Hernánz R, Zafra-Martin J, Sanmamed N, Kishan A, Alongi F, Moghanaki D, Nagar H, Couñago F. MR-LINAC, a New Partner in Radiation Oncology: Current Landscape. Cancers (Basel) 2024; 16:270. [PMID: 38254760 PMCID: PMC10813892 DOI: 10.3390/cancers16020270] [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: 12/17/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
Technological advances in radiation oncology are oriented towards improving treatment precision and tumor control. Among these advances, magnetic-resonance-image-guided radiation therapy (MRgRT) stands out, with technological advances to deliver targeted treatments adapted to a tumor's anatomy on the day while minimizing incidental exposure to organs at risk, offering an unprecedented therapeutic advantage compared to X-ray-based IGRT delivery systems. This new technology changes the traditional workflow in radiation oncology and requires an evolution in team coordination to administer more precise treatments. Once implemented, it paves the way for newer indication for radiation therapy to safely deliver higher doses than ever before, with better preservation of healthy tissues to optimize patient outcomes. In this narrative review, we assess the technical aspects of the novel linear accelerators that can deliver MRgRT and summarize the available published experience to date, focusing on oncological results and future challenges.
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Affiliation(s)
- Abrahams Ocanto
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Lisselott Torres
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Miguel Montijano
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Diego Rincón
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Castalia Fernández
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Beatriz Sevilla
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Daniela Gonsalves
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Macarena Teja
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Marcos Guijarro
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
| | - Luis Glaría
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
| | - Raúl Hernánz
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
| | - Juan Zafra-Martin
- Group of Translational Research in Cancer Immunotherapy, Centro de Investigaciones Médico-Sanitarias (CIMES), Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga (UMA), 29010 Málaga, Spain;
- Department of Radiation Oncology, Hospital Universitario Virgen de la Victoria, 29010 Málaga, Spain
| | - Noelia Sanmamed
- Department of Radiation Oncology, Hospital Universitario Clínico San Carlos, 28040 Madrid, Spain;
| | - Amar Kishan
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA;
| | - Filippo Alongi
- Advanced Radiation Oncology Department, Cancer Care Center, IRCCS Sacro Cuore Don Calabria Hospital, 37024 Negrar, Italy;
- University of Brescia, 25121 Brescia, Italy
| | - Drew Moghanaki
- UCLA Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY 10065, USA;
| | - Felipe Couñago
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, GenesisCare, 28002 Madrid, Spain; (L.T.); (M.M.); (D.R.); (C.F.); (B.S.); (D.G.); (M.T.); (M.G.); (L.G.); (R.H.); (F.C.)
- Department of Radiation Oncology, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010 Madrid, Spain
- GenesisCare, 28043 Madrid, Spain
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Zoteva V, De Meulenaere V, De Boeck M, Vanhove C, Leybaert L, Raedt R, Pieters L, Vral A, Boterberg T, Deblaere K. An improved F98 glioblastoma rat model to evaluate novel treatment strategies incorporating the standard of care. PLoS One 2024; 19:e0296360. [PMID: 38165944 PMCID: PMC10760731 DOI: 10.1371/journal.pone.0296360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/11/2023] [Indexed: 01/04/2024] Open
Abstract
Glioblastoma (GB) is the most common and malignant primary brain tumor in adults with a median survival of 12-15 months. The F98 Fischer rat model is one of the most frequently used animal models for GB studies. However, suboptimal inoculation leads to extra-axial and extracranial tumor formations, affecting its translational value. We aim to improve the F98 rat model by incorporating MRI-guided (hypo)fractionated radiotherapy (3 x 9 Gy) and concomitant temozolomide chemotherapy, mimicking the current standard of care. To minimize undesired tumor growth, we reduced the number of inoculated cells (starting from 20 000 to 500 F98 cells), slowed the withdrawal of the syringe post-inoculation, and irradiated the inoculation track separately. Our results reveal that reducing the number of F98 GB cells correlates with a diminished risk of extra-axial and extracranial tumor growth. However, this introduces higher variability in days until GB confirmation and uniformity in GB growth. To strike a balance, the model inoculated with 5000 F98 cells displayed the best results and was chosen as the most favorable. In conclusion, our improved model offers enhanced translational potential, paving the way for more accurate and reliable assessments of novel adjuvant therapeutic approaches for GB.
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Affiliation(s)
| | | | | | | | - Luc Leybaert
- Physiology Group, Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium
| | - Robrecht Raedt
- Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Leen Pieters
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Anne Vral
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Tom Boterberg
- Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium
| | - Karel Deblaere
- Department of Radiology, Ghent University, Ghent, Belgium
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9
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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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10
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Willems YCP, Vaassen F, Zegers CML, Postma AA, Jaspers J, Romero AM, Unipan M, Swinnen A, Anten M, Teernstra O, Compter I, van Elmpt W, Eekers DBP. Anatomical changes in resection cavity during brain radiotherapy. J Neurooncol 2023; 165:479-486. [PMID: 38095775 DOI: 10.1007/s11060-023-04505-1] [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: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND AND PURPOSE Brain tumors are in general treated with a maximal safe resection followed by radiotherapy of remaining tumor including the resection cavity (RC) and chemotherapy. Anatomical changes of the RC during radiotherapy can have impact on the coverage of the target volume. The aim of the current study was to quantify the potential changes of the RC and to identify risk factors for RC changes. MATERIALS AND METHODS Sixteen patients treated with pencil beam scanning proton therapy between October 2019 and April 2020 were retrospectively analyzed. The RC was delineated on pre-treatment computed tomography (CT) and magnetic resonance imaging, and weekly CT-scans during treatment. Isotropic expansions were applied to the pre-treatment RC (1-5 mm). The percentage of volume of the RC during treatment within the expanded pre-treatment volumes was quantified. Potential risk factors (volume of RC, time interval surgery-radiotherapy and relationship of RC to the ventricles) were evaluated using Spearman's rank correlation coefficient. RESULTS The average variation in relative RC volume during treatment was 26.1% (SD 34.6%). An expansion of 4 mm was required to cover > 95% of the RC volume in > 90% of patients. There was a significant relationship between the absolute volume of the pre-treatment RC and the volume changes during treatment (Spearman's ρ = - 0.644; p = 0.007). CONCLUSION RCs are dynamic after surgery. Potentially, an additional margin in brain cancer patients with an RC should be considered, to avoid insufficient target coverage. Future research on local recurrence patterns is recommended.
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Affiliation(s)
- Yves C P Willems
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Femke Vaassen
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Catharina M L Zegers
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Alida A Postma
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jaap Jaspers
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alejandra Méndez Romero
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Mirko Unipan
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ans Swinnen
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Monique Anten
- Department of Neurology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Onno Teernstra
- Department of Neurosurgery, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Inge Compter
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daniëlle B P Eekers
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
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11
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Breto AL, Cullison K, Zacharaki EI, Wallaengen V, Maziero D, Jones K, Valderrama A, de la Fuente MI, Meshman J, Azzam GA, Ford JC, Stoyanova R, Mellon EA. A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma. Cancers (Basel) 2023; 15:5241. [PMID: 37958415 PMCID: PMC10647471 DOI: 10.3390/cancers15215241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.35T MRI-linac. Tumor and edema (tumor lesion) and resection cavity kinetics throughout the treatment were manually segmented on these daily MRI. Utilizing a convolutional neural network, an automatic segmentation deep learning network was built. A nine-fold cross-validation schema was used to train the network using 80:10:10 for training, validation, and testing. Thirty-six glioblastoma patients were imaged pre-treatment and 30 times during radiotherapy (n = 31 volumes, total of 930 MRIs). The average tumor lesion and resection cavity volumes were 94.56 ± 64.68 cc and 72.44 ± 35.08 cc, respectively. The average Dice similarity coefficient between manual and auto-segmentation for tumor lesion and resection cavity across all patients was 0.67 and 0.84, respectively. This is the first brain lesion segmentation network developed for MRI-linac. The network performed comparably to the only other published network for auto-segmentation of post-operative glioblastoma lesions. Segmented volumes can be utilized for adaptive radiotherapy and propagated across multiple MRI contrasts to create a prognostic model for glioblastoma based on multiparametric MRI.
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Affiliation(s)
- Adrian L. Breto
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Kaylie Cullison
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Evangelia I. Zacharaki
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Veronica Wallaengen
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Danilo Maziero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, La Jolla, CA 92093, USA
| | - Kolton Jones
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
- West Physics, Atlanta, GA 30339, USA
| | - Alessandro Valderrama
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Macarena I. de la Fuente
- Department of Neurology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Jessica Meshman
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Gregory A. Azzam
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - John C. Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Eric A. Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
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12
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Bryant JM, Doniparthi A, Weygand J, Cruz-Chamorro R, Oraiqat IM, Andreozzi J, Graham J, Redler G, Latifi K, Feygelman V, Rosenberg SA, Yu HHM, Oliver DE. Treatment of Central Nervous System Tumors on Combination MR-Linear Accelerators: Review of Current Practice and Future Directions. Cancers (Basel) 2023; 15:5200. [PMID: 37958374 PMCID: PMC10649155 DOI: 10.3390/cancers15215200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Magnetic resonance imaging (MRI) provides excellent visualization of central nervous system (CNS) tumors due to its superior soft tissue contrast. Magnetic resonance-guided radiotherapy (MRgRT) has historically been limited to use in the initial treatment planning stage due to cost and feasibility. MRI-guided linear accelerators (MRLs) allow clinicians to visualize tumors and organs at risk (OARs) directly before and during treatment, a process known as online MRgRT. This novel system permits adaptive treatment planning based on anatomical changes to ensure accurate dose delivery to the tumor while minimizing unnecessary toxicity to healthy tissue. These advancements are critical to treatment adaptation in the brain and spinal cord, where both preliminary MRI and daily CT guidance have typically had limited benefit. In this narrative review, we investigate the application of online MRgRT in the treatment of various CNS malignancies and any relevant ongoing clinical trials. Imaging of glioblastoma patients has shown significant changes in the gross tumor volume over a standard course of chemoradiotherapy. The use of adaptive online MRgRT in these patients demonstrated reduced target volumes with cavity shrinkage and a resulting reduction in radiation dose to uninvolved tissue. Dosimetric feasibility studies have shown MRL-guided stereotactic radiotherapy (SRT) for intracranial and spine tumors to have potential dosimetric advantages and reduced morbidity compared with conventional linear accelerators. Similarly, dosimetric feasibility studies have shown promise in hippocampal avoidance whole brain radiotherapy (HA-WBRT). Next, we explore the potential of MRL-based multiparametric MRI (mpMRI) and genomically informed radiotherapy to treat CNS disease with cutting-edge precision. Lastly, we explore the challenges of treating CNS malignancies and special limitations MRL systems face.
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Affiliation(s)
- John Michael Bryant
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Ajay Doniparthi
- Morsani College of Medicine, University of South Florida, Tampa, FL 33602, USA;
| | - Joseph Weygand
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Ruben Cruz-Chamorro
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Ibrahim M. Oraiqat
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Jacqueline Andreozzi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Jasmine Graham
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Gage Redler
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Vladimir Feygelman
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Stephen A. Rosenberg
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Hsiang-Hsuan Michael Yu
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Daniel E. Oliver
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
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13
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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14
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Ladbury C, Amini A, Schwer A, Liu A, Williams T, Lee P. Clinical Applications of Magnetic Resonance-Guided Radiotherapy: A Narrative Review. Cancers (Basel) 2023; 15:cancers15112916. [PMID: 37296879 DOI: 10.3390/cancers15112916] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/20/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Magnetic resonance-guided radiotherapy (MRgRT) represents a promising new image guidance technology for radiation treatment delivery combining an onboard MRI scanner with radiation delivery technology. By enabling real-time low-field or high-field MRI acquisition, it facilitates improved soft tissue delineation, adaptive treatment, and motion management. Now that MRgRT has been available for nearly a decade, research has shown the technology can be used to effectively shrink treatment margins to either decrease toxicity (in breast, prostate cancer, and pancreatic cancer) or facilitate dose-escalation and improved oncologic outcomes (in pancreatic and liver cancer), as well as enabling indications that require clear soft tissue delineation and gating (lung and cardiac ablation). In doing so, the use of MRgRT has the potential to significantly improve the outcomes and quality of life of the patients it treats. The present narrative review aims to describe the rationale for MRgRT, the current and forthcoming state of technology, existing studies, and future directions for the advancement of MRgRT, including associated challenges.
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Affiliation(s)
- Colton Ladbury
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Arya Amini
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Amanda Schwer
- Department of Radiation Oncology, City of Hope Orange County Lennar Foundation Cancer Center, Irvine, CA 92618, USA
| | - An Liu
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Terence Williams
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Percy Lee
- Department of Radiation Oncology, City of Hope Orange County Lennar Foundation Cancer Center, Irvine, CA 92618, USA
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15
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Ding S, Liu B, Zheng S, Wang D, Liu M, Liu H, Zhang P, Peng K, He H, Zhou R, Guo J, Qiu B, Huang X, Liu H. An exploratory analysis of MR-guided fractionated stereotactic radiotherapy in patients with brain metastases. Clin Transl Radiat Oncol 2023; 40:100602. [PMID: 36910023 PMCID: PMC9996243 DOI: 10.1016/j.ctro.2023.100602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/15/2023] [Accepted: 02/19/2023] [Indexed: 02/24/2023] Open
Abstract
Purpose To assess the feasibility and potential benefits of online adaptive MR-guided fractionated stereotatic radiotherapy (FSRT) in patients with brain metastases (BMs). Methods and materials Twenty-eight consecutive patients with BMs were treated with FSRT of 30 Gy in 5 fractions on the 1.5 T MR-Linac. The FSRT fractions employed daily MR scans and the contours were utilized to create each adapted plan. The brain lesions and perilesional edema were delineated on MR images of pre-treatment simulation (Fx0) and all fractions (Fx1, Fx2, Fx3, Fx4 and Fx5) to evaluate the inter-fractional changes. These changes were quantified using absolute/relative volume, Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics. Planning target volume (PTV) coverage and organ at risk (OAR) constraints were used to compare non-adaptive and adaptive plans. Results A total of 28 patients with 88 lesions were evaluated, and 23 patients (23/28, 82.1%) had primary lung adenocarcinoma. Significant tumor volume reduction had been found during FSRT compared to Fx0 for all 88 lesions (median -0.75%, -5.33%, -9.32%, -17.96% and -27.73% at Fx1, Fx2, Fx3, Fx4 and Fx5, p < 0.05). There were 47 (47/88, 53.4%) lesions being accompanied by perilesional edema and the inter-fractional changes were significantly different compared to those without perilesional edema (p < 0.001). Patients with multiple lesions (13/28, 46.4%) had more significant inter-fractional tumor changes than those with single lesion (15/28, 53.6%), including tumor volume reduction and anatomical shift (p < 0.001). PTV coverage of non-adaptive plans was below the prescribed coverage in 26/140 fractions (19%), with 12 (9%) failing by more than 10%. All 140 adaptive fractions met prescribed target coverage. The adaptive plans also had lower dose to whole brain than non-adaptive plans (p < 0.001). Conclusions Significant inter-fractional tumor changes could be found during FSRT in patients with BMs treated on the 1.5 T MR-Linac. Daily MR-guided re-optimization of treatment plans showed dosimetric benefit in patients with perilesional edema or multiple lesions.
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Affiliation(s)
- Shouliang Ding
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Biaoshui Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Shiyang Zheng
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Daquan Wang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Mingzhi Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Hongdong Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Pengxin Zhang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Kangqiang Peng
- Department of Radiology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Haoqiang He
- Department of Radiology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Rui Zhou
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Jinyu Guo
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Bo Qiu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Xiaoyan Huang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
| | - Hui Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, China
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16
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Bryant JM, Weygand J, Keit E, Cruz-Chamorro R, Sandoval ML, Oraiqat IM, Andreozzi J, Redler G, Latifi K, Feygelman V, Rosenberg SA. Stereotactic Magnetic Resonance-Guided Adaptive and Non-Adaptive Radiotherapy on Combination MR-Linear Accelerators: Current Practice and Future Directions. Cancers (Basel) 2023; 15:2081. [PMID: 37046741 PMCID: PMC10093051 DOI: 10.3390/cancers15072081] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
Stereotactic body radiotherapy (SBRT) is an effective radiation therapy technique that has allowed for shorter treatment courses, as compared to conventionally dosed radiation therapy. As its name implies, SBRT relies on daily image guidance to ensure that each fraction targets a tumor, instead of healthy tissue. Magnetic resonance imaging (MRI) offers improved soft-tissue visualization, allowing for better tumor and normal tissue delineation. MR-guided RT (MRgRT) has traditionally been defined by the use of offline MRI to aid in defining the RT volumes during the initial planning stages in order to ensure accurate tumor targeting while sparing critical normal tissues. However, the ViewRay MRIdian and Elekta Unity have improved upon and revolutionized the MRgRT by creating a combined MRI and linear accelerator (MRL), allowing MRgRT to incorporate online MRI in RT. MRL-based MR-guided SBRT (MRgSBRT) represents a novel solution to deliver higher doses to larger volumes of gross disease, regardless of the proximity of at-risk organs due to the (1) superior soft-tissue visualization for patient positioning, (2) real-time continuous intrafraction assessment of internal structures, and (3) daily online adaptive replanning. Stereotactic MR-guided adaptive radiation therapy (SMART) has enabled the safe delivery of ablative doses to tumors adjacent to radiosensitive tissues throughout the body. Although it is still a relatively new RT technique, SMART has demonstrated significant opportunities to improve disease control and reduce toxicity. In this review, we included the current clinical applications and the active prospective trials related to SMART. We highlighted the most impactful clinical studies at various tumor sites. In addition, we explored how MRL-based multiparametric MRI could potentially synergize with SMART to significantly change the current treatment paradigm and to improve personalized cancer care.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Stephen A. Rosenberg
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (J.M.B.)
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Guevara B, Cullison K, Maziero D, Azzam GA, De La Fuente MI, Brown K, Valderrama A, Meshman J, Breto A, Ford JC, Mellon EA. Simulated Adaptive Radiotherapy for Shrinking Glioblastoma Resection Cavities on a Hybrid MRI-Linear Accelerator. Cancers (Basel) 2023; 15:1555. [PMID: 36900346 PMCID: PMC10000839 DOI: 10.3390/cancers15051555] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
During radiation therapy (RT) of glioblastoma, daily MRI with combination MRI-linear accelerator (MRI-Linac) systems has demonstrated significant anatomic changes, including evolving post-surgical cavity shrinkage. Cognitive function RT for brain tumors is correlated with radiation doses to healthy brain structures, especially the hippocampi. Therefore, this study investigates whether adaptive planning to the shrinking target could reduce normal brain RT dose with the goal of improving post-RT function. We evaluated 10 glioblastoma patients previously treated on a 0.35T MRI-Linac with a prescription of 60 Gy delivered in 30 fractions over six weeks without adaptation ("static plan") with concurrent temozolomide chemotherapy. Six weekly plans were created per patient. Reductions in the radiation dose to uninvolved hippocampi (maximum and mean) and brain (mean) were observed for weekly adaptive plans. The dose (Gy) to the hippocampi for static vs. weekly adaptive plans were, respectively: max 21 ± 13.7 vs. 15.2 ± 8.2 (p = 0.003) and mean 12.5 ± 6.7 vs. 8.4 ± 4.0 (p = 0.036). The mean brain dose was 20.6 ± 6.0 for static planning vs. 18.7 ± 6.8 for weekly adaptive planning (p = 0.005). Weekly adaptive re-planning has the potential to spare the brain and hippocampi from high-dose radiation, possibly reducing the neurocognitive side effects of RT for eligible patients.
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Affiliation(s)
- Beatriz Guevara
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Kaylie Cullison
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Danilo Maziero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, La Jolla, CA 92093, USA
| | - Gregory A. Azzam
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Macarena I. De La Fuente
- Department of Neurology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Karen Brown
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alessandro Valderrama
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Jessica Meshman
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Adrian Breto
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - John Chetley Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Eric A. Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA
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18
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Snyder KC, Mao W, Kim JP, Cunningham J, Chetty IJ, Siddiqui SM, Parikh P, Dolan J. Commissioning, clinical implementation, and initial experience with a new brain tumor treatment package on a low‐field MR‐linac. J Appl Clin Med Phys 2023. [DOI: 10.1002/acm2.13919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023] Open
Affiliation(s)
- Karen Chin Snyder
- Department of Radiation Oncology Henry Ford Health Detroit Michigan USA
| | - Weihua Mao
- Department of Radiation Oncology Henry Ford Health Detroit Michigan USA
| | - Joshua P. Kim
- Department of Radiation Oncology Henry Ford Health Detroit Michigan USA
| | | | - Indrin J. Chetty
- Department of Radiation Oncology Henry Ford Health Detroit Michigan USA
| | - Salim M. Siddiqui
- Department of Radiation Oncology Henry Ford Health Detroit Michigan USA
| | - Parag Parikh
- Department of Radiation Oncology Henry Ford Health Detroit Michigan USA
| | - Jennifer Dolan
- Department of Radiation Oncology Henry Ford Health Detroit Michigan USA
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Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T 1 and T 2 Relaxation Times with Application to Cancer Cell Culture. Int J Mol Sci 2023; 24:ijms24021554. [PMID: 36675075 PMCID: PMC9861169 DOI: 10.3390/ijms24021554] [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: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.
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Guerini AE, Nici S, Magrini SM, Riga S, Toraci C, Pegurri L, Facheris G, Cozzaglio C, Farina D, Liserre R, Gasparotti R, Ravanelli M, Rondi P, Spiazzi L, Buglione M. Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features. Technol Cancer Res Treat 2023; 22:15330338231199286. [PMID: 37774771 PMCID: PMC10542234 DOI: 10.1177/15330338231199286] [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: 04/28/2023] [Revised: 07/24/2023] [Accepted: 08/08/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Possible advantages of magnetic resonance (MR)-guided radiation therapy (MRgRT) for the treatment of brain tumors include improved definition of treatment volumes and organs at risk (OARs) that could allow margin reductions, resulting in limited dose to the OARs and/or dose escalation to target volumes. Recently, hybrid systems integrating a linear accelerator and an magnetic resonance imaging (MRI) scan (MRI-linacs, MRL) have been introduced, that could potentially lead to a fully MRI-based treatment workflow. METHODS We performed a systematic review of the published literature regarding the adoption of MRL for the treatment of primary or secondary brain tumors (last update November 3, 2022), retrieving a total of 2487 records; after a selection based on title and abstracts, the full text of 74 articles was analyzed, finally resulting in the 52 papers included in this review. RESULTS AND DISCUSSION Several solutions have been implemented to achieve a paradigm shift from CT-based radiotherapy to MRgRT, such as the management of geometric integrity and the definition of synthetic CT models that estimate electron density. Multiple sequences have been optimized to acquire images with adequate quality with on-board MR scanner in limited times. Various sophisticated algorithms have been developed to compensate the impact of magnetic field on dose distribution and calculate daily adaptive plans in a few minutes with satisfactory dosimetric parameters for the treatment of primary brain tumors and cerebral metastases. Dosimetric studies and preliminary clinical experiences demonstrated the feasibility of treating brain lesions with MRL. CONCLUSIONS The adoption of an MRI-only workflow is feasible and could offer several advantages for the treatment of brain tumors, including superior image quality for lesions and OARs and the possibility to adapt the treatment plan on the basis of daily MRI. The growing body of clinical data will clarify the potential benefit in terms of toxicity and response to treatment.
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Affiliation(s)
- Andrea Emanuele Guerini
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Co-first authors
| | - Stefania Nici
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
- Co-first authors
| | - Stefano Maria Magrini
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Stefano Riga
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Cristian Toraci
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Ludovica Pegurri
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Giorgio Facheris
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Claudia Cozzaglio
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Davide Farina
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Roberto Liserre
- Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy
| | - Roberto Gasparotti
- Neuroradiology Unit, Department of Medical-Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Paolo Rondi
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Luigi Spiazzi
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
- Co-last author
| | - Michela Buglione
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Co-last author
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Dajani S, Hill VB, Kalapurakal JA, Horbinski CM, Nesbit EG, Sachdev S, Yalamanchili A, Thomas TO. Imaging of GBM in the Age of Molecular Markers and MRI Guided Adaptive Radiation Therapy. J Clin Med 2022; 11:jcm11195961. [PMID: 36233828 PMCID: PMC9572863 DOI: 10.3390/jcm11195961] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/26/2022] [Accepted: 09/29/2022] [Indexed: 12/03/2022] Open
Abstract
Glioblastoma (GBM) continues to be one of the most lethal malignancies and is almost always fatal. In this review article, the role of radiation therapy, systemic therapy, as well as the molecular basis of classifying GBM is described. Technological advances in the treatment of GBM are outlined as well as the diagnostic imaging characteristics of this tumor. In addition, factors that affect prognosis such as differentiating progression from treatment effect is discussed. The role of MRI guided radiation therapy and how this technology may provide a mechanism to improve the care of patients with this disease are described.
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22
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MRI-guided Radiotherapy (MRgRT) for treatment of Oligometastases: Review of clinical applications and challenges. Int J Radiat Oncol Biol Phys 2022; 114:950-967. [PMID: 35901978 DOI: 10.1016/j.ijrobp.2022.07.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE Early clinical results on the application of magnetic resonance imaging (MRI) coupled with a linear accelerator to deliver MR-guided radiation therapy (MRgRT) have demonstrated feasibility for safe delivery of stereotactic body radiotherapy (SBRT) in treatment of oligometastatic disease. Here we set out to review the clinical evidence and challenges associated with MRgRT in this setting. METHODS AND MATERIALS We performed a systematic review of the literature pertaining to clinical experiences and trials on the use of MRgRT primarily for the treatment of oligometastatic cancers. We reviewed the opportunities and challenges associated with the use of MRgRT. RESULTS Benefits of MRgRT pertaining to superior soft-tissue contrast, real-time imaging and gating, and online adaptive radiotherapy facilitate safe and effective dose escalation to oligometastatic tumors while simultaneously sparing surrounding healthy tissues. Challenges concerning further need for clinical evidence and technical considerations related to planning, delivery, quality assurance (QA) of hypofractionated doses, and safety in the MRI environment must be considered. CONCLUSIONS The promising early indications of safety and effectiveness of MRgRT for SBRT-based treatment of oligometastatic disease in multiple treatment locations should lead to further clinical evidence to demonstrate the benefit of this technology.
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23
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Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022; 159:333-346. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/11/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
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CT-MR Image Fusion for Post-Implant Dosimetry Analysis in Brain Tumor Seed Implantation- a Preliminary Study. DISEASE MARKERS 2022; 2022:6310262. [PMID: 35620270 PMCID: PMC9129983 DOI: 10.1155/2022/6310262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 12/17/2022]
Abstract
Purpose To calculate and evaluate postimplant dosimetry (PID) with CT-MR fusion technique after brain tumor brachytherapy and compare the result with CT-based PID. Methods and Materials 16 brain tumor patients received MR-guided intervention with Iodine-125 (125I) seed implantation entered this preliminary study for PID evaluation. Registration and fusion of CT and MR images of the same patients were performed one day after operation. Seeds identification and targets delineation were carried out on CT, MR, and CT-MR fusion images, each. The number and location of seeds on MR or CT- MR fusion images were compared with those of actually implanted seeds. Clinical target volume (CTV) and dosimetric parameters such as %D90, %V100 and external V100 were measured and calculated. In addition, the correlation of the fusion to CT CTV ratio and other factors were analyzed. Results The numbers of fusion seeds were not significantly different compared with reference seeds (t =1.76, p >0.05). The difference between reference seeds numbers and truly extracted MR seeds numbers was statistically significant (t =3.91, p <0.05). All dosimetric parameters showed significant differences between the two techniques (p <0.05). The mean CTV delineated on fusion images was 34.3 ± 33.6, smaller than that on CT images. The mean values of external V100, %V100 and %D90 on fusion images were larger than those on CT images. Correlation analysis showed that the fusion-CT V100 ratio was positively and significantly correlated with the fusion-CT volume ratio. Conclusions This preliminary study indicated that CT-MR fusion-based PID exhibited good accuracy for 125I brain tumor brachytherapy dosimetry when compared to CT-based PID and merits further research to establish best-outcome protocols.
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25
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Graham JA, Redler G, Delozier KB, Yu HHM, Oliver DE, Rosenberg SA. Dosimetric feasibility of hippocampal avoidance whole brain radiotherapy with an MRI-guided linear accelerator. J Appl Clin Med Phys 2022; 23:e13587. [PMID: 35344266 PMCID: PMC9195042 DOI: 10.1002/acm2.13587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/07/2022] [Accepted: 02/23/2022] [Indexed: 11/21/2022] Open
Abstract
Purpose/Objective(s) Whole brain radiotherapy with hippocampal avoidance (HA‐WBRT) is a technique utilized to treat metastatic brain disease while preserving memory and neurocognitive function. We hypothesized that the treatment planning and delivery of HA‐WBRT plans is feasible with an MRI‐guided linear accelerator (linac) and compared plan results with clinical non‐MRI‐guided C‐Arm linac plans. Materials/Methods Twelve HA‐WBRT patients treated on a non‐MRI‐guided C‐Arm linac were selected for retrospective analysis. Treatment plans were developed using a 0.35T MRI‐guided linac system for comparison to clinical plans. Treatment planning goals were defined as provided in the Phase II Trial NRG CC001. MRI‐guided radiotherapy (MRgRT) treatment plans were developed by a dosimetrist and compared with clinical plans. quality assurance (QA) plans were generated and delivered on the MRI‐guided linac to a cylindrical diode detector array. Planning target volume (PTV) coverage was normalized to ∼95% to provide a control point for comparison of dose to the organs at risk. Results MRgRT plans were deliverable and met all clinical goals. Mean values demonstrated that the clinical plans were less heterogeneous than MRgRT plans with mean PTV V37.5 Gy of 0.00% and 0.03% (p = 0.013), respectively. Average hippocampi maximum doses were 14.19 ± 1.29 Gy and 15.00 ± 1.51 Gy, respectively. The gamma analysis comparing planned and measured doses resulted in a mean of 99.9% ± 0.12% of passing points (3%/2mm criteria). MRgRT plans had an average of 38.33 beams with average total delivery time and beam‐on time of 13.7 (11.2–17.5) min and 4.1 (3.2–5.4) min, respectively. Clinical plan delivery times ranged from 3 to 7 min depending on the number of noncoplanar arcs. Planning time between the clinical and MRgRT plans was comparable. Conclusion This study demonstrates that HA‐WBRT can be treated using an MRI‐guided linear accelerator with comparable treatment plan quality and delivery accuracy.
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Affiliation(s)
- Jasmine A Graham
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States
| | - Gage Redler
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States
| | - Kirby B Delozier
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States
| | | | - Daniel E Oliver
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States
| | - Stephen A Rosenberg
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States
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Hall WA, Paulson E, Li XA, Erickson B, Schultz C, Tree A, Awan M, Low DA, McDonald BA, Salzillo T, Glide-Hurst CK, Kishan AU, Fuller CD. Magnetic resonance linear accelerator technology and adaptive radiation therapy: An overview for clinicians. CA Cancer J Clin 2022; 72:34-56. [PMID: 34792808 PMCID: PMC8985054 DOI: 10.3322/caac.21707] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/01/2021] [Accepted: 09/22/2021] [Indexed: 12/25/2022] Open
Abstract
Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat abstract to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR-guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time-consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.
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MESH Headings
- History, 20th Century
- History, 21st Century
- Humans
- Magnetic Resonance Imaging, Interventional/history
- Magnetic Resonance Imaging, Interventional/instrumentation
- Magnetic Resonance Imaging, Interventional/methods
- Magnetic Resonance Imaging, Interventional/trends
- Neoplasms/diagnostic imaging
- Neoplasms/radiotherapy
- Particle Accelerators
- Radiation Oncology/history
- Radiation Oncology/instrumentation
- Radiation Oncology/methods
- Radiation Oncology/trends
- Radiotherapy Planning, Computer-Assisted/history
- Radiotherapy Planning, Computer-Assisted/instrumentation
- Radiotherapy Planning, Computer-Assisted/methods
- Radiotherapy Planning, Computer-Assisted/trends
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Affiliation(s)
- William A. Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Christopher Schultz
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Alison Tree
- The Royal Marsden National Health Service Foundation Trust and the Institute of Cancer Research, London, United Kingdom
| | - Musaddiq Awan
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Daniel A. Low
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Brigid A. McDonald
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Travis Salzillo
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Carri K. Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Amar U. Kishan
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
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