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Buti G, Ajdari A, Bridge CP, Sharp GC, Bortfeld T. Diffusion tensor transformation for personalizing target volumes in radiation therapy. Med Image Anal 2024; 97:103271. [PMID: 39043108 DOI: 10.1016/j.media.2024.103271] [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: 11/03/2023] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 07/25/2024]
Abstract
Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy. The ICT model allows the mapping of tumor cell densities and tumor fronts (as iso-levels of tumor cell density) from the template image to the patient image for inclusion in radiotherapy treatment planning. The proposed approach transforms the diffusion tensors to simulate tumor growth in locally deformed anatomy and outputs the tumor cell density distribution over time. The ICT model is validated in a cohort of ten brain tumor patients. Comparative analysis with the tumor cell density in the original template image shows that the ICT model accurately simulates tumor cell densities in the deformed image space. By creating radiotherapy target volumes as tumor fronts, this study provides a framework for more personalized radiotherapy treatment planning, without the use of additional imaging.
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Affiliation(s)
- Gregory Buti
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, USA.
| | - Ali Ajdari
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, USA
| | - Christopher P Bridge
- Massachusetts General Hospital and Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Charlestown, MA 02129, USA
| | - Gregory C Sharp
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, USA
| | - Thomas Bortfeld
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, USA
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Li P, Lu H, Shi X, Yan J, Zhou L, Yang J, Wang B, Zhao Y, Liu L, Zhu Y, Xu L, Yang X, Su X, Yang Y, Zhang T, Guo L, Liu X. Protective effects of human urinary kallidinogenase against corticospinal tract damage in acute ischemic stroke patients. Neuroreport 2024; 35:431-438. [PMID: 38526971 DOI: 10.1097/wnr.0000000000002028] [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: 03/27/2024]
Abstract
This study aimed to assess the effects of human urinary kallidinogenase (HUK) on motor function outcome and corticospinal tract recovery in patients with acute ischemic stroke (AIS). This study was a randomized, controlled, single-blinded trial. Eighty AIS patients were split into two groups: the HUK and control groups. The HUK group was administered HUK and standard treatment, while the control group received standard treatment only. At admission and discharge, the National Institutes of Health Stroke Scale (NIHSS), Barthel Index (BI) and muscle strength were scored. The primary endpoint was the short-term outcomes of AIS patients under different treatments. The secondary endpoint was the degree of corticospinal tract fiber damage under different treatments. There was a significant improvement in the NIHSS Scale, BI and muscle strength scores in the HUK group compared with controls (Mann-Whitney U test; P < 0.05). Diffusion tensor tractography classification and intracranial arterial stenosis were independent predictors of short-term recovery by linear regression analysis. The changes in fractional anisotropy (FA) and apparent diffusion coefficient (ADC) decline rate were significantly smaller in the HUK group than in the control group ( P < 0.05). Vascular endothelial growth factor (VEGF) increased significantly after HUK treatment ( P < 0.05), and the VEGF change was negatively correlated with changes in ADC. HUK is beneficial for the outcome in AIS patients especially in motor function recovery. It may have protective effects on the corticospinal tract which is reflected by the reduction in the FA and ADC decline rates and increased VEGF expression. The study was registered on ClinicalTrials.gov (unique identifier: NCT04102956).
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Affiliation(s)
- Peifang Li
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
- Department of Neurology, Handan Central Hospital, Handan
| | - Honglin Lu
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Xiaoman Shi
- Department of Pediatrics, Affiliated Hospital of Hebei University, Baoding
| | - Jiajia Yan
- Department of Neurology, Cangzhou Integrated Traditional Chinese and Western Medicine Hospital, Cangzhou
| | - Lixia Zhou
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Jipeng Yang
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Binbin Wang
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Yanying Zhao
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Luji Liu
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Yipu Zhu
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Lei Xu
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Xiaoli Yang
- Department of Neurology, Hebei University of Engineering School of Medicine, Handan
| | - Xudong Su
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Yi Yang
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Tong Zhang
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Li Guo
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
| | - Xiaoyun Liu
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang
- Department of Neurology, the First Hospital of Hebei Medical University, Shijiazhuang, China
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Buti G, Ajdari A, Hochreuter K, Shih H, Bridge CP, Sharp GC, Bortfeld T. The influence of anisotropy on the clinical target volume of brain tumor patients. Phys Med Biol 2024; 69:10.1088/1361-6560/ad1997. [PMID: 38157552 PMCID: PMC10863979 DOI: 10.1088/1361-6560/ad1997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/29/2023] [Indexed: 01/03/2024]
Abstract
Objective.Current radiotherapy guidelines for glioma target volume definition recommend a uniform margin expansion from the gross tumor volume (GTV) to the clinical target volume (CTV), assuming uniform infiltration in the invaded brain tissue. However, glioma cells migrate preferentially along white matter tracts, suggesting that white matter directionality should be considered in an anisotropic CTV expansion. We investigate two models of anisotropic CTV expansion and evaluate their clinical feasibility.Approach.To incorporate white matter directionality into the CTV, a diffusion tensor imaging (DTI) atlas is used. The DTI atlas consists of water diffusion tensors that are first spatially transformed into local tumor resistance tensors, also known as metric tensors, and secondly fed to a CTV expansion algorithm to generate anisotropic CTVs. Two models of spatial transformation are considered in the first step. The first model assumes that tumor cells experience reduced resistance parallel to the white matter fibers. The second model assumes that the anisotropy of tumor cell resistance is proportional to the anisotropy observed in DTI, with an 'anisotropy weighting parameter' controlling the proportionality. The models are evaluated in a cohort of ten brain tumor patients.Main results.To evaluate the sensitivity of the model, a library of model-generated CTVs was computed by varying the resistance and anisotropy parameters. Our results indicate that the resistance coefficient had the most significant effect on the global shape of the CTV expansion by redistributing the target volume from potentially less involved gray matter to white matter tissue. In addition, the anisotropy weighting parameter proved useful in locally increasing CTV expansion in regions characterized by strong tissue directionality, such as near the corpus callosum.Significance.By incorporating anisotropy into the CTV expansion, this study is a step toward an interactive CTV definition that can assist physicians in incorporating neuroanatomy into a clinically optimized CTV.
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Affiliation(s)
- Gregory Buti
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, United States of America
| | - Ali Ajdari
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, United States of America
| | - Kim Hochreuter
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, United States of America
- Aarhus University Hospital, Danish Centre for Particle Therapy, Palle Juul-Jensens Blvd. 99, DK-8200 Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Palle Juul-Jensens Blvd. 82, DK-8200 Aarhus, Denmark
| | - Helen Shih
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, 100 Blossom St, Boston, MA 02114, United States of America
| | - Christopher P Bridge
- Massachusetts General Hospital and Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Charlestown, MA 02129, United States of America
| | - Gregory C Sharp
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, United States of America
| | - Thomas Bortfeld
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, United States of America
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Aldawsari AM, Al-Qaisieh B, Broadbent DA, Bird D, Murray L, Speight R. The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review. Phys Imaging Radiat Oncol 2023; 27:100476. [PMID: 37565088 PMCID: PMC10410581 DOI: 10.1016/j.phro.2023.100476] [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: 03/24/2023] [Revised: 07/15/2023] [Accepted: 07/19/2023] [Indexed: 08/12/2023] Open
Abstract
Background and purpose Improving the accuracy of brain tumour radiotherapy (RT) treatment planning is important to optimise patient outcomes. This systematic review investigates primary studies providing clinical evidence for the integration of quantitative magnetic resonance imaging (qMRI) biomarkers and MRI radiomics to optimise brain tumour RT planning. Materials and methods PubMed, Scopus, Embase and Web of Science databases were searched for all years until June 21, 2022. The search identified original articles demonstrating clinical evidence for the use of qMRI biomarkers and MRI radiomics for the optimization of brain cancer RT planning. Relevant information was extracted and tabulated, including qMRI metrics and techniques, impact on RT plan optimization and changes in target and normal tissue contouring and dose distribution. Results Nineteen articles met the inclusion criteria. Studies were grouped according to the qMRI biomarkers into: 1) diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI; five studies); 2) diffusion tensor imaging (DTI; seven studies); and 3) MR spectroscopic imaging (MRSI; seven studies). No relevant MRI-based radiomics studies were identified. Integration of DTI maps offers the potential for improved organs at risk (OAR) sparing. MRSI metabolic maps are a promising technique for improving delineation accuracy in terms of heterogeneity and infiltration, with OAR sparing. No firm conclusions could be drawn regarding the integration of DWI metrics and PWI maps. Conclusions Integration of qMRI metrics into RT planning offers the potential to improve delineation and OAR sparing. Clinical trials and consensus guidelines are required to demonstrate the clinical benefits of such approaches.
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Affiliation(s)
- Abeer M. Aldawsari
- Leeds Institute of Cardiovascular & Metabolic Medicine (LICAMM), University of Leeds, Woodhouse, Leeds LS2 9JT, United Kingdom
- Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, Riyadh 12371, Saudi Arabia
| | - Bashar Al-Qaisieh
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
| | - David A. Broadbent
- Leeds Institute of Cardiovascular & Metabolic Medicine (LICAMM), University of Leeds, Woodhouse, Leeds LS2 9JT, United Kingdom
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
| | - David Bird
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
| | - Louise Murray
- Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, St James’s University Hospital, Leeds LS9 7LP, United Kingdom
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Richard Speight
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, United Kingdom
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Pang Y, Wang H, Li H. Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy. Front Oncol 2022; 11:764665. [PMID: 35111666 PMCID: PMC8801459 DOI: 10.3389/fonc.2021.764665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/29/2021] [Indexed: 12/22/2022] Open
Abstract
Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.
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Affiliation(s)
- Yaru Pang
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Hui Wang
- Department of Chemical Engineering, University College London, London, United Kingdom
| | - He Li
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
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Li M, Zhang Q, Yang K. Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment. Front Oncol 2021; 11:645177. [PMID: 34513659 PMCID: PMC8429950 DOI: 10.3389/fonc.2021.645177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 07/30/2021] [Indexed: 02/05/2023] Open
Abstract
Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.
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Affiliation(s)
- Mei Li
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Kaixuan Yang
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
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Knobe S, Dzierma Y, Wenske M, Berdel C, Fleckenstein J, Melchior P, Palm J, Nuesken FG, Hunt A, Engwer C, Surulescu C, Yilmaz U, Reith W, Rübe C. Feasibility and clinical usefulness of modelling glioblastoma migration in adjuvant radiotherapy. Z Med Phys 2021; 32:149-158. [PMID: 33966944 PMCID: PMC9948823 DOI: 10.1016/j.zemedi.2021.03.004] [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: 12/17/2020] [Revised: 03/01/2021] [Accepted: 03/18/2021] [Indexed: 11/16/2022]
Abstract
Glioblastoma (GBM) is one of the most common primary brain tumours in adults, with a dismal prognosis despite aggressive multimodality treatment by a combination of surgery and adjuvant radiochemotherapy. A detailed knowledge of the spreading of glioma cells in the brain might allow for more targeted escalated radiotherapy, aiming to reduce locoregional relapse. Recent years have seen the development of a large variety of mathematical modelling approaches to predict glioma migration. The aim of this study is hence to evaluate the clinical applicability of a detailed micro- and meso-scale mathematical model in radiotherapy. First and foremost, a clinical workflow is established, in which the tumour is automatically segmented as input data and then followed in time mathematically based on the diffusion tensor imaging data. The influence of several free model parameters is individually evaluated, then the full model is retrospectively validated for a collective of 3 GBM patients treated at our institution by varying the most important model parameters to achieve optimum agreement with the tumour development during follow-up. Agreement of the model predictions with the real tumour growth as defined by manual contouring based on the follow-up MRI images is analyzed using the dice coefficient. The tumour evolution over 103-212 days follow-up could be predicted by the model with a dice coefficient better than 60% for all three patients. In all cases, the final tumour volume was overestimated by the model by a factor between 1.05 and 1.47. To evaluate the quality of the agreement between the model predictions and the ground truth, we must keep in mind that our gold standard relies on a single observer's (CB) manually-delineated tumour contours. We therefore decided to add a short validation of the stability and reliability of these contours by an inter-observer analysis including three other experienced radiation oncologists from our department. In total, a dice coefficient between 63% and 89% is achieved between the four different observers. Compared with this value, the model predictions (62-66%) perform reasonably well, given the fact that these tumour volumes were created based on the pre-operative segmentation and DTI.
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Affiliation(s)
- Sven Knobe
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany.
| | - Yvonne Dzierma
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Michael Wenske
- Institute for Analysis and Numerics, University of Muenster, Muenster, Germany
| | - Christian Berdel
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Jochen Fleckenstein
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Patrick Melchior
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Jan Palm
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Frank G. Nuesken
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
| | | | - Christian Engwer
- Institute for Analysis and Numerics, University of Muenster, Muenster, Germany
| | - Christina Surulescu
- Felix Klein Centre for Mathematics, University of Kaiserslautern, Kaiserslautern, Germany
| | - Umut Yilmaz
- Department of Diagnostic and Interventional Radiology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Wolfgang Reith
- Department of Diagnostic and Interventional Radiology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Christian Rübe
- Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg/Saar, Germany
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Castellano A, Bailo M, Cicone F, Carideo L, Quartuccio N, Mortini P, Falini A, Cascini GL, Minniti G. Advanced Imaging Techniques for Radiotherapy Planning of Gliomas. Cancers (Basel) 2021; 13:cancers13051063. [PMID: 33802292 PMCID: PMC7959155 DOI: 10.3390/cancers13051063] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/24/2021] [Accepted: 02/26/2021] [Indexed: 02/07/2023] Open
Abstract
The accuracy of target delineation in radiation treatment (RT) planning of cerebral gliomas is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Conventional magnetic resonance imaging (MRI), including contrast-enhanced T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, represents the current standard imaging modality for target volume delineation of gliomas. However, conventional sequences have limited capability to discriminate treatment-related changes from viable tumors, owing to the low specificity of increased blood-brain barrier permeability and peritumoral edema. Advanced physiology-based MRI techniques, such as MR spectroscopy, diffusion MRI and perfusion MRI, have been developed for the biological characterization of gliomas and may circumvent these limitations, providing additional metabolic, structural, and hemodynamic information for treatment planning and monitoring. Radionuclide imaging techniques, such as positron emission tomography (PET) with amino acid radiopharmaceuticals, are also increasingly used in the workup of primary brain tumors, and their integration in RT planning is being evaluated in specialized centers. This review focuses on the basic principles and clinical results of advanced MRI and PET imaging techniques that have promise as a complement to RT planning of gliomas.
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Affiliation(s)
- Antonella Castellano
- Neuroradiology Unit, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (A.C.); (A.F.)
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (P.M.)
| | - Francesco Cicone
- Department of Experimental and Clinical Medicine, “Magna Graecia” University of Catanzaro, and Nuclear Medicine Unit, University Hospital “Mater Domini”, 88100 Catanzaro, Italy;
- Correspondence: ; Tel.: +39-0-961-369-4155
| | - Luciano Carideo
- National Cancer Institute, G. Pascale Foundation, 80131 Naples, Italy;
| | - Natale Quartuccio
- A.R.N.A.S. Ospedale Civico Di Cristina Benfratelli, 90144 Palermo, Italy;
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (P.M.)
| | - Andrea Falini
- Neuroradiology Unit, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (A.C.); (A.F.)
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, “Magna Graecia” University of Catanzaro, and Nuclear Medicine Unit, University Hospital “Mater Domini”, 88100 Catanzaro, Italy;
| | - Giuseppe Minniti
- Radiation Oncology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Policlinico Le Scotte, 53100 Siena, Italy;
- IRCCS Neuromed, 86077 Pozzilli (IS), Italy
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The Utility of Diffusion and Perfusion Magnetic Resonance Imaging in Target Delineation of High-Grade Gliomas. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8718097. [PMID: 32851090 PMCID: PMC7439164 DOI: 10.1155/2020/8718097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 03/22/2020] [Accepted: 07/21/2020] [Indexed: 02/01/2023]
Abstract
Background The tumor volume of high-grade glioma (HGG) after surgery is usually determined by contrast-enhanced MRI (CE-MRI), but the clinical target volume remains controversial. Functional magnetic resonance imaging (multimodality MRI) techniques such as magnetic resonance perfusion-weighted imaging (PWI) and diffusion-tensor imaging (DTI) can make up for CE-MRI. This study explored the survival outcomes and failure patterns of patients with HGG by comparing the combination of multimodality MRI and CE-MRI imaging with CE-MRI alone. Methods 102 patients with postoperative HGG between 2012 and 2016 were included. 50 were delineated based on multimodality MRI (PWI, DTI) and CE-MRI (enhanced T1), and the other 52 were delineated based on CE-MRI as control. Results The median survival benefit was 6 months. The 2-year overall survival, progression-free survival, and local-regional control rates were 48% vs. 25%, 42% vs. 13.46%, and 40% vs. 13.46% for the multimodality MRI and CE-MRI cohorts, respectively. The two cohorts had similar rates of disease progression and recurrence but different proportions of failure patterns. The univariate analysis shows that characteristics of patients such as combined with epilepsy, the dose of radiotherapy, the selection of MRI were significant influence factors for 2-year overall survival. However, in multivariate analyses, only the selection of MRI was an independent significant predictor of overall survival. Conclusions This study was the first to explore the clinical value of multimodality MRI in the delineation of radiotherapy target volume for HGG. The conclusions of the study have positive reference significance to the combination of multimodality MRI and CE-MRI in guiding the delineation of the radiotherapy target area for HGG patients.
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Witulla B, Goerig N, Putz F, Frey B, Engelhorn T, Dörfler A, Uder M, Fietkau R, Bert C, Laun FB. On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data. PLoS One 2020; 15:e0227146. [PMID: 31905221 PMCID: PMC6944332 DOI: 10.1371/journal.pone.0227146] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 12/11/2019] [Indexed: 01/20/2023] Open
Abstract
Radiotherapy (RT) is commonly applied for the treatment of glioblastoma multiforme (GBM). Following the planning target volume (PTV) definition procedure standardized in guidelines, a 20% risk of missing non-local recurrences is present. Purpose of this study was to evaluate whether diffusion tensor imaging (DTI)-based fiber tracking may be beneficial for PTV definition taking into account the prediction of distant recurrences. 56 GBM patients were examined with magnetic resonance imaging (MRI) including DTI performed before RT after resection of the primary tumor. Follow-up MRIs were acquired in three month intervals. For the seven patients with a distant recurrence, fiber tracking was performed with three algorithms and it was evaluated whether connections existed from the primary tumor region to the distant recurrence. It depended strongly on the used tracking algorithm and the used tracking parameters whether a connection was observed. Most of the connections were weak and thus not usable for PTV definition. Only in one of the seven patients with a recurring tumor, a clear connection was present. It seems unlikely that DTI-based fiber tracking can be beneficial for predicting distant recurrences in the planning of PTVs for glioblastoma multiforme.
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Affiliation(s)
- Barbara Witulla
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nicole Goerig
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Benjamin Frey
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Tobias Engelhorn
- Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- * E-mail:
| | - Frederik Bernd Laun
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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11
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Affiliation(s)
- Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Ludvig Paul Muren
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Morten Høyer
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Cai Grau
- Department of Oncology and Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
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12
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Trip AK, Jensen MB, Kallehauge JF, Lukacova S. Individualizing the radiotherapy target volume for glioblastoma using DTI-MRI: a phase 0 study on coverage of recurrences. Acta Oncol 2019; 58:1532-1535. [PMID: 31303079 DOI: 10.1080/0284186x.2019.1637018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Anouk K. Trip
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Morten B. Jensen
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | | | - Slavka Lukacova
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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13
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Jordan K, Morin O, Wahl M, Amirbekian B, Chapman C, Owen J, Mukherjee P, Braunstein S, Henry R. An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography. Front Oncol 2019; 9:810. [PMID: 31544062 PMCID: PMC6730482 DOI: 10.3389/fonc.2019.00810] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 08/08/2019] [Indexed: 12/04/2022] Open
Abstract
There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning.
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Affiliation(s)
- Kesshi Jordan
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Michael Wahl
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States.,Department of Radiation Oncology, Samaritan Pastega Regional Cancer Center, Corvallis, OR, United States
| | - Bagrat Amirbekian
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States
| | - Christopher Chapman
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Julia Owen
- Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology, University of Washington, Seattle, WA, United States
| | - Pratik Mukherjee
- Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,Center for Imaging of Neurodegenerative Disease, San Francisco VA Medical Center, San Francisco, CA, United States
| | - Steve Braunstein
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Roland Henry
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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14
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Molecular and Clinical Insights into the Invasive Capacity of Glioblastoma Cells. JOURNAL OF ONCOLOGY 2019; 2019:1740763. [PMID: 31467533 PMCID: PMC6699388 DOI: 10.1155/2019/1740763] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 07/01/2019] [Accepted: 07/07/2019] [Indexed: 12/22/2022]
Abstract
The invasive capacity of GBM is one of the key tumoral features associated with treatment resistance, recurrence, and poor overall survival. The molecular machinery underlying GBM invasiveness comprises an intricate network of signaling pathways and interactions with the extracellular matrix and host cells. Among them, PI3k/Akt, Wnt, Hedgehog, and NFkB play a crucial role in the cellular processes related to invasion. A better understanding of these pathways could potentially help in developing new therapeutic approaches with better outcomes. Nevertheless, despite significant advances made over the last decade on these molecular and cellular mechanisms, they have not been translated into the clinical practice. Moreover, targeting the infiltrative tumor and its significance regarding outcome is still a major clinical challenge. For instance, the pre- and intraoperative methods used to identify the infiltrative tumor are limited when trying to accurately define the tumor boundaries and the burden of tumor cells in the infiltrated parenchyma. Besides, the impact of treating the infiltrative tumor remains unclear. Here we aim to highlight the molecular and clinical hallmarks of invasion in GBM.
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15
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Leibfarth S, Winter RM, Lyng H, Zips D, Thorwarth D. Potentials and challenges of diffusion-weighted magnetic resonance imaging in radiotherapy. Clin Transl Radiat Oncol 2018; 13:29-37. [PMID: 30294681 PMCID: PMC6169338 DOI: 10.1016/j.ctro.2018.09.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 08/20/2018] [Accepted: 09/03/2018] [Indexed: 02/09/2023] Open
Abstract
Discussion of DW imaging protocols and imaging setup. Discussion of mono- and bi-exponential models for quantitative parameter extraction. Review of recent publications investigating potential benefits of using DWI in RT, including detailed synoptic table. Detailed discussion of geometric and quantitative accuracy of DW imaging and DW-derived parameters.
Purpose To review the potential and challenges of integrating diffusion weighted magnetic resonance imaging (DWI) into radiotherapy (RT). Content Details related to image acquisition of DWI for RT purposes are discussed, along with the challenges with respect to geometric accuracy and the robustness of quantitative parameter extraction. An overview of diffusion- and perfusion-related parameters derived from mono- and bi-exponential models is provided, and their role as potential RT biomarkers is discussed. Recent studies demonstrating potential of DWI in different tumor sites such as the head and neck, rectum, cervix, prostate, and brain, are reviewed in detail. Conclusion DWI has shown promise for RT outcome prediction, response assessment, as well as for tumor delineation and characterization in several cancer types. Geometric and quantification robustness is challenging and has to be addressed adequately. Evaluation in larger clinical trials with well designed imaging protocol and advanced analysis models is needed to develop the optimal strategy for integrating DWI in RT.
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Affiliation(s)
- Sara Leibfarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Germany
| | - René M Winter
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Germany
| | - Heidi Lyng
- Department of Radiation Biology, Norwegian Radium Hospital, Oslo University Hospital, Norway
| | - Daniel Zips
- Department of Radiation Oncology, University Hospital Tübingen, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Germany
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