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Moghaddasi L, Bezak E. An Integrated Monte Carlo Model for Heterogeneous Glioblastoma Treated with Boron Neutron Capture Therapy. Cancers (Basel) 2023; 15:1550. [PMID: 36900341 PMCID: PMC10001318 DOI: 10.3390/cancers15051550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/16/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Glioblastomas (GBMs) are notorious for their aggressive features, e.g., intrinsic radioresistance, extensive heterogeneity, hypoxia, and highly infiltrative behaviours. The prognosis has remained poor despite recent advances in systemic and modern X-ray radiotherapy. Boron neutron capture therapy (BNCT) represents an alternative radiotherapy technique for GBM. Previously, a Geant4 BNCT modelling framework was developed for a simplified model of GBM. PURPOSE The current work expands on the previous model by applying a more realistic in silico GBM model with heterogeneous radiosensitivity and anisotropic microscopic extensions (ME). METHODS Each cell within the GBM model was assigned an α/β value associated with different GBM cell lines and a 10B concentration. Dosimetry matrices corresponding to various MEs were calculated and combined to evaluate cell survival fractions (SF) using clinical target volume (CTV) margins of 2.0 & 2.5 cm. SFs for the BNCT simulation were compared with external X-ray radiotherapy (EBRT) SFs. RESULTS The SFs within the beam region decreased by more than two times compared to EBRT. It was demonstrated that BNCT results in markedly reduced SFs for both CTV margins compared to EBRT. However, the SF reduction as a result of the CTV margin extension using BNCT was significantly lower than using X-ray EBRT for one MEP distribution, while it remained similar for the other two MEP models. CONCLUSIONS Although the efficiency of BNCT in terms of cell kill is superior to EBRT, the extension of the CTV margin by 0.5 cm may not increase the BNCT treatment outcome significantly.
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Affiliation(s)
- Leyla Moghaddasi
- Radiation Oncology, Royal North Shore Hospital, Sydney, NSW 2065, Australia
- School of Physical Sciences, University of Adelaide, Adelaide, SA 5005, Australia
| | - Eva Bezak
- Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia
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2
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Urcun S, Baroli D, Rohan PY, Skalli W, Lubrano V, Bordas SP, Sciumè G. Non-operable glioblastoma: Proposition of patient-specific forecasting by image-informed poromechanical model. BRAIN MULTIPHYSICS 2023. [DOI: 10.1016/j.brain.2023.100067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
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3
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Harkos C, Svensson SF, Emblem KE, Stylianopoulos T. Inducing Biomechanical Heterogeneity in Brain Tumor Modeling by MR Elastography: Effects on Tumor Growth, Vascular Density and Delivery of Therapeutics. Cancers (Basel) 2022; 14:cancers14040884. [PMID: 35205632 PMCID: PMC8870149 DOI: 10.3390/cancers14040884] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/07/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Biomechanical forces aggravate brain tumor progression. In this study, magnetic resonance elastography (MRE) is employed to extract tissue biomechanical properties from five glioblastoma patients and a healthy subject, and data are incorporated in a mathematical model that simulates tumor growth. Mathematical modeling enables further understanding of glioblastoma development and allows patient-specific predictions for tumor vascularity and delivery of drugs. Incorporating MRE data results in a more realistic intratumoral distribution of mechanical stress and anisotropic tumor growth and a better description of subsequent events that are closely related to the development of stresses, including heterogeneity of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs. Abstract The purpose of this study is to develop a methodology that incorporates a more accurate assessment of tissue mechanical properties compared to current mathematical modeling by use of biomechanical data from magnetic resonance elastography. The elastography data were derived from five glioblastoma patients and a healthy subject and used in a model that simulates tumor growth, vascular changes due to mechanical stresses and delivery of therapeutic agents. The model investigates the effect of tumor-specific biomechanical properties on tumor anisotropic growth, vascular density heterogeneity and chemotherapy delivery. The results showed that including elastography data provides a more realistic distribution of the mechanical stresses in the tumor and induces anisotropic tumor growth. Solid stress distribution differs among patients, which, in turn, induces a distinct functional vascular density distribution—owing to the compression of tumor vessels—and intratumoral drug distribution for each patient. In conclusion, incorporating elastography data results in a more accurate calculation of intratumoral mechanical stresses and enables a better mathematical description of subsequent events, such as the heterogeneous development of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs.
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Affiliation(s)
- Constantinos Harkos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia 1678, Cyprus;
| | - Siri Fløgstad Svensson
- Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (S.F.S.); (K.E.E.)
- Department of Physics, The Faculty of Mathematics and Natural Sciences, University of Oslo, 0371 Oslo, Norway
| | - Kyrre E. Emblem
- Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (S.F.S.); (K.E.E.)
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia 1678, Cyprus;
- Correspondence:
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4
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Falco J, Agosti A, Vetrano IG, Bizzi A, Restelli F, Broggi M, Schiariti M, DiMeco F, Ferroli P, Ciarletta P, Acerbi F. In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case. J Clin Med 2021; 10:2169. [PMID: 34067871 PMCID: PMC8156762 DOI: 10.3390/jcm10102169] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/24/2021] [Accepted: 05/14/2021] [Indexed: 01/28/2023] Open
Abstract
Glioblastoma extensively infiltrates the brain; despite surgery and aggressive therapies, the prognosis is poor. A multidisciplinary approach combining mathematical, clinical and radiological data has the potential to foster our understanding of glioblastoma evolution in every single patient, with the aim of tailoring therapeutic weapons. In particular, the ultimate goal of biomathematics for cancer is the identification of the most suitable theoretical models and simulation tools, both to describe the biological complexity of carcinogenesis and to predict tumor evolution. In this report, we describe the results of a critical review about different mathematical models in neuro-oncology with their clinical implications. A comprehensive literature search and review for English-language articles concerning mathematical modelling in glioblastoma has been conducted. The review explored the different proposed models, classifying them and indicating the significative advances of each one. Furthermore, we present a specific case of a glioblastoma patient in which our recently proposed innovative mechanical model has been applied. The results of the mathematical models have the potential to provide a relevant benefit for clinicians and, more importantly, they might drive progress towards improving tumor control and patient's prognosis. Further prospective comparative trials, however, are still necessary to prove the impact of mathematical neuro-oncology in clinical practice.
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Affiliation(s)
- Jacopo Falco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Abramo Agosti
- MOX, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (A.A.); (P.C.)
| | - Ignazio G. Vetrano
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Alberto Bizzi
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy;
| | - Francesco Restelli
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Morgan Broggi
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Marco Schiariti
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Francesco DiMeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy
- Department of Neurological Surgery, Johns Hopkins Medical School, Baltimora, MD 21205, USA
| | - Paolo Ferroli
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Pasquale Ciarletta
- MOX, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (A.A.); (P.C.)
| | - Francesco Acerbi
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
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Menze B, Isensee F, Wiest R, Wiestler B, Maier-Hein K, Reyes M, Bakas S. Analyzing magnetic resonance imaging data from glioma patients using deep learning. Comput Med Imaging Graph 2021; 88:101828. [PMID: 33571780 PMCID: PMC8040671 DOI: 10.1016/j.compmedimag.2020.101828] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 10/29/2020] [Accepted: 11/18/2020] [Indexed: 12/21/2022]
Abstract
The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical background information of key diagnostic biomarkers in the diagnosis of glioma, the most common primary brain tumor. It offers an overview of publicly available resources and datasets for developing new computational tools and image biomarkers, with emphasis on those related to the Multimodal Brain Tumor Segmentation (BraTS) Challenge. We further offer an overview of the state-of-the-art methods in glioma image segmentation, again with an emphasis on publicly available tools and deep learning algorithms that emerged in the context of the BraTS challenge.
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Affiliation(s)
- Bjoern Menze
- Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
| | | | - Roland Wiest
- Support Center for Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland.
| | | | | | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
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6
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Bortfeld T, Shusharina N, Craft D. Probabilistic definition of the clinical target volume-implications for tumor control probability modeling and optimization. Phys Med Biol 2021; 66:01NT01. [PMID: 33197905 DOI: 10.1088/1361-6560/abcad8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Evidence has been presented that moving beyond the binary definition of clinical target volume (CTV) towards a probabilistic CTV can result in better treatment plans. The probabilistic CTV takes the likelihood of disease spread outside of the gross tumor into account. An open question is: how to optimize tumor control probability (TCP) based on the probabilistic CTV. We derive expressions for TCP under the assumptions of voxel independence and dependence. For the dependent case, we make the assumption that tumors grow outward from the gross tumor volume. We maximize the (non-convex) TCP under convex dose constraints for all models. For small numbers of voxels, and when a dose-influence matrix is not used, we use exhaustive search or Lagrange multiplier theory to compute optimal dose distributions. For larger cases we present (1) a multi-start strategy using linear programming with a random cost vector to provide random feasible starting solutions, followed by a local search, and (2) a heuristic strategy that greedily selects which subvolumes to dose, and then for each subvolume assignment runs a convex approximation of the optimization problem. The optimal dose distributions are in general different for the independent and dependent models even though the probabilities of each voxel being tumorous are set to the same in both cases. We observe phase transitions, where a subvolume is either dosed to a high level, or it gets 'sacrificed' by not dosing it at all. The greedy strategy often yields solutions indistinguishable from the multi-start solutions, but for the 2D case involving organs-at-risk and the dependent TCP model, discrepancies of around 5% (absolute) for TCP are observed. For realistic geometries, although correlated voxels is a more reasonable assumption, the correlation function is in general unknown. We demonstrate a tractable heuristic that works very well for the independent models and reasonably well for the dependent models. All data are provided.
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Affiliation(s)
- 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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7
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Dehghan M, Narimani N. Radial basis function-generated finite difference scheme for simulating the brain cancer growth model under radiotherapy in various types of computational domains. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105641. [PMID: 32726719 DOI: 10.1016/j.cmpb.2020.105641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 06/28/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES We extend the original mathematical model, i.e., Swanson's reaction-diffusion equation to the surfaces with no boundary, and we find a new numerical method based on a meshless approach for solving numerically Swanson's reaction-diffusion model in the square and on the sphere. METHODS To solve numerically the Swanson's reaction-diffusion model and its extension version, a collocation meshless technique, namely radial basis function-generated finite difference (RBF-FD) scheme is employed for approximating the spatial variables in the square domain and on the sphere, respectively. Also, to approximate the time variable of the studied models, a first-order semi-implicit backward Euler scheme is used. The resulting fully discrete scheme is a linear system of algebraic equations per time step that is solved via the biconjugate gradient stabilized (BiCGSTAB) iterative algorithm with a zero-fill incomplete lower-upper (ILU) preconditioner. RESULTS The numerical simulations show the growth of untreated and treated brain tumors with radiotherapy using estimated and clinical data (given from magnetic resonance imaging (MRI) scans of patients). Moreover, the results reported here can be used for improving the treatment strategies of the invasive brain tumor. CONCLUSIONS Using the developed numerical scheme in this paper, we can simulate the behavior of the invasive form of brain tumor response to radiotherapy. Also, we can see the effects of radiation response on the brain tumor cell concentration of individual patients. The proposed meshless technique, which is applied for solving numerically the studied model, does not depend on any background mesh or triangulation for approximation in comparison with mesh-dependent methods. Moreover, we apply this technique to the sphere via any set of distributed points easily.
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Affiliation(s)
- Mehdi Dehghan
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
| | - Niusha Narimani
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
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8
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Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology. Annu Rev Biomed Eng 2020; 22:309-341. [PMID: 32501772 PMCID: PMC7520881 DOI: 10.1146/annurev-bioeng-062117-121105] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
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Affiliation(s)
- Andreas Mang
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Spyridon Bakas
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Shashank Subramanian
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA); Department of Radiology; and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ,
| | - George Biros
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
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9
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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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10
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Meaney C, Stastna M, Kardar M, Kohandel M. Spatial optimization for radiation therapy of brain tumours. PLoS One 2019; 14:e0217354. [PMID: 31251755 PMCID: PMC6599149 DOI: 10.1371/journal.pone.0217354] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/18/2019] [Indexed: 11/18/2022] Open
Abstract
Glioblastomas are the most common primary brain tumours. They are known for their highly aggressive growth and invasion, leading to short survival times. Treatments for glioblastomas commonly involve a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Previous works have not only successfully modelled the natural growth of glioblastomas in vivo, but also show potential for the prediction of response to radiation prior to treatment. This suggests that the efficacy of XRT can be optimized before treatment in order to yield longer survival times. However, while current efforts focus on optimal scheduling of radiotherapy treatment, they do not include a similarly sophisticated spatial optimization. In an effort to improve XRT, we present a method for the spatial optimization of radiation profiles. We expand upon previous results in the general problem and examine the more physically reasonable cases of 1-step and 2-step radiation profiles during the first and second XRT fractions. The results show that by including spatial optimization in XRT, while retaining a constant prescribed total dose amount, we are able to increase the total cell kill from the clinically-applied uniform case.
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Affiliation(s)
- Cameron Meaney
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
| | - Marek Stastna
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
| | - Mehran Kardar
- Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America 02139
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
- * E-mail:
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11
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Lin CM, Yu CF, Huang HY, Chen FH, Hong JH, Chiang CS. Distinct Tumor Microenvironment at Tumor Edge as a Result of Astrocyte Activation Is Associated With Therapeutic Resistance for Brain Tumor. Front Oncol 2019; 9:307. [PMID: 31106146 PMCID: PMC6498880 DOI: 10.3389/fonc.2019.00307] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 04/03/2019] [Indexed: 12/01/2022] Open
Abstract
Tumor vasculatures and hypoxia are critical tumor micro-environmental factors associated with tumor response to the therapy and heterogeneous in both time- and location-dependent manner. Using a murine orthotopic anaplastic astrocytoma model, ALTS1C1, this study showed that brain tumor edge had a very unique microenvironment, having higher microvascular density (MVD) and better vessel function than the tumor core, but on the other hand was also positive for hypoxia markers, such as pimonidazole (PIMO), hypoxia inducible factor-1α (HIF-1α), and carbonic anhydrase IV (CAIX). The hypoxia at tumor edge was transient, named as peripheral hypoxia, and caused by different mechanisms from the chronic hypoxia in tumor core. The correlation of CAIX staining with astrocyte activation marker, glial fibrillary acid protein (GFAP), at the tumor edge indicated the involvement of astrocyte activation on the development of peripheral hypoxia. Peripheral hypoxia was a specific trait of orthotopic brain tumors at tumor edge, regardless of tumor origin. The hypoxic cells were resistant to the therapy, regardless of their location. Surviving cells, particularly those at the hypoxic region of tumor edge, are likely the cause of tumor recurrence after the therapy. New therapeutic platform that targets cells in tumor edge is likely to achieve better treatment outcomes.
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Affiliation(s)
- Chiu-Min Lin
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | - Ching-Fang Yu
- Department of Radiation Oncology, Chang Gung Memorial Hospital Linkou Branch, Taoyuan, Taiwan.,Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan
| | - Hsueh-Ya Huang
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.,Education & Medical Research National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
| | - Fang-Hsin Chen
- Department of Radiation Oncology, Chang Gung Memorial Hospital Linkou Branch, Taoyuan, Taiwan.,Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan.,Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Ji-Hong Hong
- Department of Radiation Oncology, Chang Gung Memorial Hospital Linkou Branch, Taoyuan, Taiwan.,Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan.,Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Shiun Chiang
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.,Institute of Nuclear Engineering and Science, National Tsing Hua University, Hsinchu, Taiwan.,Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu, Taiwan
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12
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Jensen MB, Guldberg TL, Harbøll A, Lukacova S, Kallehauge JF. Diffusion tensor magnetic resonance imaging driven growth modeling for radiotherapy target definition in glioblastoma. Acta Oncol 2017; 56:1639-1643. [PMID: 28893125 DOI: 10.1080/0284186x.2017.1374559] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND The clinical target volume (CTV) in radiotherapy is routinely based on gadolinium contrast enhanced T1 weighted (T1w + Gd) and T2 weighted fluid attenuated inversion recovery (T2w FLAIR) magnetic resonance imaging (MRI) sequences which have been shown to over- or underestimate the microscopic tumor cell spread. Gliomas favor spread along the white matter fiber tracts. Tumor growth models incorporating the MRI diffusion tensors (DTI) allow to account more consistently for the glioma growth. The aim of the study was to investigate the potential of a DTI driven growth model to improve target definition in glioblastoma (GBM). MATERIAL AND METHODS Eleven GBM patients were scanned using T1w, T2w FLAIR, T1w + Gd and DTI. The brain was segmented into white matter, gray matter and cerebrospinal fluid. The Fisher-Kolmogorov growth model was used assuming uniform proliferation and a difference in white and gray matter diffusion of a ratio of 10. The tensor directionality was tested using an anisotropy weighting parameter set to zero (γ0) and twenty (γ20). The volumetric comparison was performed using Hausdorff distance, Dice similarity coefficient (DSC) and surface area. RESULTS The median of the standard CTV (CTVstandard) was 180 cm3. The median surface area of CTVstandard was 211 cm2. The median surface area of respective CTVγ0 and CTVγ20 significantly increased to 338 and 376 cm2, respectively. The Hausdorff distance was greater than zero and significantly increased for both CTVγ0 and CTVγ20 with respective median of 18.7 and 25.2 mm. The DSC for both CTVγ0 and CTVγ20 were significantly below one with respective median of 0.74 and 0.72, which means that 74 and 72% of CTVstandard were included in CTVγ0 and CTVγ20, respectively. CONCLUSIONS DTI driven growth models result in CTVs with a significantly increased surface area, a significantly increased Hausdorff distance and decreased overlap between the standard and model derived volume.
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Affiliation(s)
- Morten B. Jensen
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | | | - Anja Harbøll
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Slávka Lukacova
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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13
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Protopapa M, Zygogianni A, Stamatakos GS, Antypas C, Armpilia C, Uzunoglu NK, Kouloulias V. Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J Neurooncol 2017; 136:1-11. [PMID: 29081039 DOI: 10.1007/s11060-017-2650-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 10/22/2017] [Indexed: 01/22/2023]
Abstract
Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor's point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments.
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Affiliation(s)
- Maria Protopapa
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Anna Zygogianni
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios S Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christos Antypas
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Christina Armpilia
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos K Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Vassilis Kouloulias
- Radiation Oncology Unit, 2nd Department of Radiology, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece. .,Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece.
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14
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Moghaddasi L, Bezak E. Development of an integrated Monte Carlo model for glioblastoma multiforme treated with boron neutron capture therapy. Sci Rep 2017; 7:7069. [PMID: 28765533 PMCID: PMC5539248 DOI: 10.1038/s41598-017-07302-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/27/2017] [Indexed: 12/04/2022] Open
Abstract
Glioblastomas (GBM) are notorious for their high fatality rate. Boron Neutron Capture Therapy (BNCT) being a biochemically targeted type of radiotherapy is a potent modality for GBM. In the current work, a BNCT treatment modelling framework for GBM was developed. Optimal Clinical Target Volume (CTV) margins for GBM-BNCT and the BNCT efficacy have been investigated. The model integrated a cell-based dosimetry model, an in-house-developed epithermal neutron beam model and previously-developed Microscopic Extension Probability (MEP) model. The system was defined as a cubic ICRP-brain phantom divided into 20 μm side voxels. The corresponding 10B concentrations in GBM and normal brain cells were applied. The in-silico model was irradiated with the epithermal neutron beam using 2 and 2.5 cm CTV margins. Results from the cell-based dosimetry and the MEP models were combined to calculate GBM cell survival fractions (SF) post BNCT and compared to x-ray radiotherapy (XRT) SFs. Compared to XRT, the SF within the beam decreased by five orders of magnitudes and the total SF was reduced three times following BNCT. CTV extension by 0.5 cm reduced the SF by additional (53.8 ± 0.3)%. In conclusion, BNCT results in a more efficient cell kill. The extension of the CTV margin, however, may not increase the treatment outcome significantly.
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Affiliation(s)
- Leyla Moghaddasi
- School of Physical Sciences, University of Adelaide, Adelaide, Australia. .,Department of Medical Physics, Adelaide Radiotherapy Centre, Adelaide, Australia.
| | - Eva Bezak
- School of Physical Sciences, University of Adelaide, Adelaide, Australia.,School of Health Sciences, University of South Australia, Adelaide, Australia.,Sansom Institute for Health Research, University of South Australia, Adelaide, Australia
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15
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Apolle R, Rehm M, Bortfeld T, Baumann M, Troost EGC. The clinical target volume in lung, head-and-neck, and esophageal cancer: Lessons from pathological measurement and recurrence analysis. Clin Transl Radiat Oncol 2017; 3:1-8. [PMID: 29658006 PMCID: PMC5893525 DOI: 10.1016/j.ctro.2017.01.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 01/19/2017] [Accepted: 01/19/2017] [Indexed: 12/25/2022] Open
Abstract
Radiotherapy research has achieved remarkable progress in target volume definition. Advances in medical imaging facilitate more precise localization of the gross tumor volume, alongside a more detailed understanding of the geometric uncertainties associated with treatment delivery that has enabled robust safety margins to be customized to the specific treatment scenario at hand. By contrast, the clinical target volume, meant to encompass gross tumor, as well as, adjacent sub-clinical disease, has evolved very little. It is more often defined by clinician experience and institutional convention than on a patient-specific basis. This disparity arises from the inherent invisibility of sub-clinical disease in current medical imaging. Its incidence and expanse can only be ascertained via indirect means. This article reviews two such strategies: histopathological measurements on resection specimen and analyses of locoregional recurrences after radiotherapy.
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Affiliation(s)
- Rudi Apolle
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany
| | - Maximilian Rehm
- OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,Department of Radiation Oncology, University Hospital and Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, Dresden, Germany
| | - Thomas Bortfeld
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael Baumann
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,Department of Radiation Oncology, University Hospital and Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Esther G C Troost
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,Department of Radiation Oncology, University Hospital and Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
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16
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Le M, Delingette H, Kalpathy-Cramer J, Gerstner ER, Batchelor T, Unkelbach J, Ayache N. Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:815-825. [PMID: 28113925 DOI: 10.1109/tmi.2016.2626443] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning. Depending on the clinical data available, we compare three different scenarios to personalize the model. First, we consider a single MRI acquisition before therapy, as it would usually be the case in clinical routine. Second, we use two MRI acquisitions at two distinct time points in order to personalize the model and plan radiotherapy. Third, we include the uncertainty in the segmentation process. We present the application of our approach on two patients diagnosed with high grade glioma. We introduce two methods to derive the radiotherapy prescription dose distribution, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density. We show how our method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration. We further present extensions of the method in order to spare adjacent organs at risk by re-distributing the dose. The presented approach and its proof of concept may help in the future to better target the tumor and spare organs at risk.
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17
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Delgado AF, Fahlström M, Nilsson M, Berntsson SG, Zetterling M, Libard S, Alafuzoff I, van Westen D, Lätt J, Smits A, Larsson EM. Diffusion Kurtosis Imaging of Gliomas Grades II and III - A Study of Perilesional Tumor Infiltration, Tumor Grades and Subtypes at Clinical Presentation. Radiol Oncol 2017; 51:121-129. [PMID: 28740446 PMCID: PMC5514651 DOI: 10.1515/raon-2017-0010] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 01/08/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Diffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by microcellular structures. We analyzed DKI in suspected low-grade gliomas prior to histopathological diagnosis. The aim was to investigate if diffusion parameters in the perilesional normal-appearing white matter (NAWM) differed from contralesional white matter, and to investigate differences between glioma malignancy grades II and III and glioma subtypes (astrocytomas and oligodendrogliomas). PATIENTS AND METHODS Forty-eight patients with suspected low-grade glioma were prospectively recruited to this institutional review board-approved study and investigated with preoperative DKI at 3T after written informed consent. Patients with histologically proven glioma grades II or III were further analyzed (n=35). Regions of interest (ROIs) were delineated on T2FLAIR images and co-registered to diffusion MRI parameter maps. Mean DKI data were compared between perilesional and contralesional NAWM (student's t-test for dependent samples, Wilcoxon matched pairs test). Histogram DKI data were compared between glioma types and glioma grades (multiple comparisons of mean ranks for all groups). The discriminating potential for DKI in assessing glioma type and grade was assessed with receiver operating characteristics (ROC) curves. RESULTS There were significant differences in all mean DKI variables between perilesional and contralesional NAWM (p=<0.000), except for axial kurtosis (p=0.099). Forty-four histogram variables differed significantly between glioma grades II (n=23) and III (n=12) (p=0.003-0.048) and 10 variables differed significantly between ACs (n=18) and ODs (n=17) (p=0.011-0.050). ROC curves of the best discriminating variables had an area under the curve (AUC) of 0.657-0.815. CONCLUSIONS Mean DKI variables in perilesional NAWM differ significantly from contralesional NAWM, suggesting altered microstructure by tumor infiltration not depicted on morphological MRI. Histogram analysis of DKI data identifies differences between glioma grades and subtypes.
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Affiliation(s)
- Anna F Delgado
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Markus Fahlström
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | | | - Shala G Berntsson
- Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden
| | - Maria Zetterling
- Department of Neuroscience, Neurosurgery, Uppsala University, Uppsala, Sweden
| | - Sylwia Libard
- Department of Immunology, Genetics and Pathology, Section of pathology, Uppsala University Hospital and Uppsala University, Uppsala, Sweden
| | - Irina Alafuzoff
- Department of Immunology, Genetics and Pathology, Section of pathology, Uppsala University Hospital and Uppsala University, Uppsala, Sweden
| | | | - Jimmy Lätt
- Department of Imaging and Function, Skåne University Healthcare, Lund, Sweden
| | - Anja Smits
- Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden
| | - Elna-Marie Larsson
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
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18
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Le M, Delingette H, Kalpathy-Cramer J, Gerstner ER, Batchelor T, Unkelbach J, Ayache N. MRI Based Bayesian Personalization of a Tumor Growth Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2329-2339. [PMID: 27164582 DOI: 10.1109/tmi.2016.2561098] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The mathematical modeling of brain tumor growth has been the topic of numerous research studies. Most of this work focuses on the reaction-diffusion model, which suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating the parameters of the reaction-diffusion model is difficult because of the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the complex dynamics of the tumor evolution. Our approach aims at analyzing the uncertainty in the patient specific parameters of a tumor growth model, by sampling from the posterior probability of the parameters knowing the magnetic resonance images of a given patient. The estimation of the posterior probability is based on: 1) a highly parallelized implementation of the reaction-diffusion equation using the Lattice Boltzmann Method (LBM), and 2) a high acceptance rate Monte Carlo technique called Gaussian Process Hamiltonian Monte Carlo (GPHMC). We compare this personalization approach with two commonly used methods based on the spherical asymptotic analysis of the reaction-diffusion model, and on a derivative-free optimization algorithm. We demonstrate the performance of the method on synthetic data, and on seven patients with a glioblastoma, the most aggressive primary brain tumor. This Bayesian personalization produces more informative results. In particular, it provides samples from the regions of interest and highlights the presence of several modes for some patients. In contrast, previous approaches based on optimization strategies fail to reveal the presence of different modes, and correlation between parameters.
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Meghdadi N, Soltani M, Niroomand-Oscuii H, Ghalichi F. Image based modeling of tumor growth. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2016; 39:601-13. [PMID: 27596102 DOI: 10.1007/s13246-016-0475-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2016] [Accepted: 08/16/2016] [Indexed: 01/11/2023]
Abstract
Tumors are a main cause of morbidity and mortality worldwide. Despite the efforts of the clinical and research communities, little has been achieved in the past decades in terms of improving the treatment of aggressive tumors. Understanding the underlying mechanism of tumor growth and evaluating the effects of different therapies are valuable steps in predicting the survival time and improving the patients' quality of life. Several studies have been devoted to tumor growth modeling at different levels to improve the clinical outcome by predicting the results of specific treatments. Recent studies have proposed patient-specific models using clinical data usually obtained from clinical images and evaluating the effects of various therapies. The aim of this review is to highlight the imaging role in tumor growth modeling and provide a worthwhile reference for biomedical and mathematical researchers with respect to tumor modeling using the clinical data to develop personalized models of tumor growth and evaluating the effect of different therapies.
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Affiliation(s)
- N Meghdadi
- Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran.,Computational Medicine Institute, Tehran, Iran
| | - M Soltani
- Division of Nuclear Medicine, Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287-0807, USA. .,Department of Mechanical Engineering, K. N. T. University of Technology, Tehran, Iran. .,Cancer Biology Research Center, Tehran University of Medical Sciences, Tehran, Iran. .,Computational Medicine Institute, Tehran, Iran.
| | - H Niroomand-Oscuii
- Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran.
| | - F Ghalichi
- Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran
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20
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Jafari-Khouzani K, Loebel F, Bogner W, Rapalino O, Gonzalez GR, Gerstner E, Chi AS, Batchelor TT, Rosen BR, Unkelbach J, Shih HA, Cahill DP, Andronesi OC. Volumetric relationship between 2-hydroxyglutarate and FLAIR hyperintensity has potential implications for radiotherapy planning of mutant IDH glioma patients. Neuro Oncol 2016; 18:1569-1578. [PMID: 27382115 DOI: 10.1093/neuonc/now100] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 04/13/2016] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Gliomas with mutant isocitrate dehydrogenase (IDH) produce high levels of 2-hydroxyglutarate (2HG) that can be quantitatively measured by 3D magnetic resonance spectroscopic imaging (MRSI). Current glioma MRI primarily relies upon fluid-attenuated inversion recovery (FLAIR) hyperintensity for treatment planning, although this lacks specificity for tumor cells. Here, we investigated the relationship between 2HG and FLAIR in mutant IDH glioma patients to determine whether 2HG mapping is valuable for radiotherapy planning. METHODS Seventeen patients with mutant IDH1 gliomas were imaged by 3 T MRI. A 3D MRSI sequence was employed to specifically image 2HG. FLAIR imaging was performed using standard clinical protocol. Regions of interest (ROIs) were determined for FLAIR and optimally thresholded 2HG hyperintensities. The overlap, displacement, and volumes of 2HG and FLAIR ROIs were calculated. RESULTS In 8 of 17 (47%) patients, the 2HG volume was larger than FLAIR volume. Across the entire cohort, the mean volume of 2HG was 35.3 cc (range, 5.3-92.7 cc), while the mean volume of FLAIR was 35.8 cc (range, 6.3-140.8 cc). FLAIR and 2HG ROIs had mean overlap of 0.28 (Dice coefficients range, 0.03-0.57) and mean displacement of 12.2 mm (range, 3.2-23.5 mm) between their centers of mass. CONCLUSIONS Our results indicate that for a substantial number of patients, the 2HG volumetric assessment of tumor burden is more extensive than FLAIR volume. In addition, there is only partial overlap and asymmetric displacement between the centers of FLAIR and 2HG ROIs. These results may have important implications for radiotherapy planning of IDH mutant glioma.
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Affiliation(s)
- Kourosh Jafari-Khouzani
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Franziska Loebel
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Wolfgang Bogner
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Otto Rapalino
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Gilberto R Gonzalez
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Elizabeth Gerstner
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Andrew S Chi
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Tracy T Batchelor
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Jan Unkelbach
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Helen A Shih
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Daniel P Cahill
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Ovidiu C Andronesi
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
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22
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Baumann M, Krause M, Overgaard J, Debus J, Bentzen SM, Daartz J, Richter C, Zips D, Bortfeld T. Radiation oncology in the era of precision medicine. Nat Rev Cancer 2016; 16:234-49. [PMID: 27009394 DOI: 10.1038/nrc.2016.18] [Citation(s) in RCA: 514] [Impact Index Per Article: 64.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Technological advances and clinical research over the past few decades have given radiation oncologists the capability to personalize treatments for accurate delivery of radiation dose based on clinical parameters and anatomical information. Eradication of gross and microscopic tumours with preservation of health-related quality of life can be achieved in many patients. Two major strategies, acting synergistically, will enable further widening of the therapeutic window of radiation oncology in the era of precision medicine: technology-driven improvement of treatment conformity, including advanced image guidance and particle therapy, and novel biological concepts for personalized treatment, including biomarker-guided prescription, combined treatment modalities and adaptation of treatment during its course.
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Affiliation(s)
- Michael Baumann
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay - National Center for Radiation Research in Oncology (NCRO), Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, and Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstrasse 74, 01307 Dresden
- National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307 Dresden
- German Cancer Consortium (DKTK) Dresden, Germany
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiation Oncology, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Mechthild Krause
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay - National Center for Radiation Research in Oncology (NCRO), Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, and Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstrasse 74, 01307 Dresden
- National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307 Dresden
- German Cancer Consortium (DKTK) Dresden, Germany
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiation Oncology, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Nørrebrogade 44, 8000 Aarhus C, Denmark
| | - Jürgen Debus
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), University of Heidelberg Medical School and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120 Heidelberg
- Heidelberg Ion Therapy Center (HIT), Department of Radiation Oncology, University of Heidelberg Medical School, Im Neuenheimer Feld 400, 69120 Heidelberg
- German Cancer Consortium (DKTK) Heidelberg, Germany
| | - Søren M Bentzen
- Department of Epidemiology and Public Health and Greenebaum Cancer Center, University of Maryland School of Medicine, 22 S Greene Street S9a03, Baltimore, Maryland 21201, USA
| | - Juliane Daartz
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital and Harvard Medical School, 1000 Blossom Street Cox 362, Boston, Massachusetts 02114, USA
| | - Christian Richter
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay - National Center for Radiation Research in Oncology (NCRO), Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, and Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstrasse 74, 01307 Dresden
- National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307 Dresden
- German Cancer Consortium (DKTK) Dresden, Germany
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Daniel Zips
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- German Cancer Consortium Tübingen, Postfach 2669, 72016 Tübingen
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, Hoppe-Seyler-Strasse 3, 72016 Tübingen, Germany
| | - Thomas Bortfeld
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital and Harvard Medical School, 1000 Blossom Street Cox 362, Boston, Massachusetts 02114, USA
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Costa FHS, Campos M, da Silva MAA. The universal growth rate behavior and regime transition in adherent cell colonies. J Theor Biol 2015; 387:181-8. [PMID: 26471071 DOI: 10.1016/j.jtbi.2015.09.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Revised: 09/17/2015] [Accepted: 09/25/2015] [Indexed: 11/16/2022]
Abstract
In this work, we used five cell lineages, cultivated in vitro, to show they follow a common functional form to the growth rate: a sigmoidal curve, suggesting that competition and cooperation (usual mechanisms for systems with this behavior) might be present. Both theoretical and experimental investigations, on the causes of this behavior, are challenging for the research field; since the sigmoidal form to the growth rate seems to absorb important properties of such systems, e.g., cell deformation and statistical interactions. We shed some light on this subject by showing how cell spreading affects the radius behavior of the growing colonies. Doing numerical time derivatives of the experimental data, we obtained the growth rates. Using reduced variables for the time and rates, we obtained the collapse of all colonies growth rates onto one curve with sigmoidal shape. This suggests a universal-type behavior, with regime transition related to a morphological transition of adherent cell colonies.
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Affiliation(s)
- F H S Costa
- Departamento de Física, FFCLRP; Universidade de São Paulo, 14040-901; Ribeirão Preto, São Paulo, Brazil.
| | - M Campos
- Departamento de Química e Ciências Ambientais, IBILCE, Universidade Estadual Paulista Júlio de Mesquita Filho, 15054-000 São José do Rio Preto, São Paulo, Brazil
| | - M A A da Silva
- Departamento de Física, FFCLRP; Universidade de São Paulo, 14040-901; Ribeirão Preto, São Paulo, Brazil; Departamento de Física e Química, FCFRP; Universidade de São Paulo, 14040-903; Ribeirão Preto, São Paulo, Brazil.
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Unkelbach J, Menze BH, Konukoglu E, Dittmann F, Le M, Ayache N, Shih HA. Radiotherapy planning for glioblastoma based on a tumor growth model: improving target volume delineation. Phys Med Biol 2014; 59:747-70. [PMID: 24440875 DOI: 10.1088/0031-9155/59/3/747] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Glioblastoma differ from many other tumors in the sense that they grow infiltratively into the brain tissue instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In current clinical practice, a uniform margin, typically two centimeters, is applied to account for microscopic spread of disease that is not directly assessable through imaging. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth, which arises from different factors: anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and infiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density. This paper analyzes the model with respect to implications for target volume definition and identifies its most critical components. A retrospective study involving ten glioblastoma patients treated at our institution has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated.
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Affiliation(s)
- Jan Unkelbach
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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