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Marcu LG. The Ever-Changing Role of Medical Physicists in the Era of Personalized Medicine. J Med Phys 2021; 45:197-198. [PMID: 33953493 PMCID: PMC8074720 DOI: 10.4103/jmp.jmp_113_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 11/24/2020] [Indexed: 12/03/2022] Open
Affiliation(s)
- Loredana G Marcu
- Department of Physics, Faculty of Informatics and Science, University of Oradea, Oradea, Romania.,Cancer Research Institute, University of South Australia, Adelaide, SA, Australia
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Moghaddasi L, Bezak E, Harriss-Phillips W. Monte-Carlo model development for evaluation of current clinical target volume definition for heterogeneous and hypoxic glioblastoma. Phys Med Biol 2016; 61:3407-26. [PMID: 27046324 DOI: 10.1088/0031-9155/61/9/3407] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Clinical target volume (CTV) determination may be complex and subjective. In this work a microscopic-scale tumour model was developed to evaluate current CTV practices in glioblastoma multiforme (GBM) external radiotherapy. Previously, a Geant4 cell-based dosimetry model was developed to calculate the dose deposited in individual GBM cells. Microscopic extension probability (MEP) models were then developed using Matlab-2012a. The results of the cell-based dosimetry model and MEP models were combined to calculate survival fractions (SF) for CTV margins of 2.0 and 2.5 cm. In the current work, oxygenation and heterogeneous radiosensitivity profiles were incorporated into the GBM model. The genetic heterogeneity was modelled using a range of α/β values (linear-quadratic model parameters) associated with different GBM cell lines. These values were distributed among the cells randomly, taken from a Gaussian-weighted sample of α/β values. Cellular oxygen pressure was distributed randomly taken from a sample weighted to profiles obtained from literature. Three types of GBM models were analysed: homogeneous-normoxic, heterogeneous-normoxic, and heterogeneous-hypoxic. The SF in different regions of the tumour model and the effect of the CTV margin extension from 2.0-2.5 cm on SFs were investigated for three MEP models. The SF within the beam was increased by up to three and two orders of magnitude following incorporation of heterogeneous radiosensitivities and hypoxia, respectively, in the GBM model. However, the total SF was shown to be overdominated by the presence of tumour cells in the penumbra region and to a lesser extent by genetic heterogeneity and hypoxia. CTV extension by 0.5 cm reduced the SF by a maximum of 78.6 ± 3.3%, 78.5 ± 3.3%, and 77.7 ± 3.1% for homogeneous and heterogeneous-normoxic, and heterogeneous hypoxic GBMs, respectively. Monte-Carlo model was developed to quantitatively evaluate SF for genetically heterogeneous and hypoxic GBM with two CTV margins and three MEP distributions. The results suggest that photon therapy may not provide cure for hypoxic and genetically heterogeneous GBM. However, the extension of the CTV margin by 0.5 cm could be beneficial to delay the recurrence time for this tumour type due to significant increase in tumour cell irradiation.
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Affiliation(s)
- L Moghaddasi
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA, Australia. School of Chemistry & Physics, University of Adelaide, Adelaide, SA, Australia
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Rockne RC, Trister AD, Jacobs J, Hawkins-Daarud AJ, Neal ML, Hendrickson K, Mrugala MM, Rockhill JK, Kinahan P, Krohn KA, Swanson KR. A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET. J R Soc Interface 2015; 12:rsif.2014.1174. [PMID: 25540239 PMCID: PMC4305419 DOI: 10.1098/rsif.2014.1174] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient's disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [(18)F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model-data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.
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Affiliation(s)
- Russell C Rockne
- Department of Neurological Surgery, Northwestern University and Feinberg School of Medicine, 676 N Saint Clair Street, Suite 1300, Chicago, IL 60611, USA Northwestern Brain Tumor Institute, Northwestern University, 675 N Saint Clair Street, Suite 2100, Chicago, IL 60611, USA,
| | - Andrew D Trister
- Department of Radiation Oncology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Joshua Jacobs
- Department of Neurological Surgery, Northwestern University and Feinberg School of Medicine, 676 N Saint Clair Street, Suite 1300, Chicago, IL 60611, USA Northwestern Brain Tumor Institute, Northwestern University, 675 N Saint Clair Street, Suite 2100, Chicago, IL 60611, USA
| | - Andrea J Hawkins-Daarud
- Department of Neurological Surgery, Northwestern University and Feinberg School of Medicine, 676 N Saint Clair Street, Suite 1300, Chicago, IL 60611, USA Northwestern Brain Tumor Institute, Northwestern University, 675 N Saint Clair Street, Suite 2100, Chicago, IL 60611, USA
| | - Maxwell L Neal
- Department of Pathology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Kristi Hendrickson
- Department of Radiation Oncology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Maciej M Mrugala
- Department of Neurology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Jason K Rockhill
- Department of Radiation Oncology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Paul Kinahan
- Department of Radiology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Kenneth A Krohn
- Department of Radiation Oncology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA Department of Radiology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Kristin R Swanson
- Department of Neurological Surgery, Northwestern University and Feinberg School of Medicine, 676 N Saint Clair Street, Suite 1300, Chicago, IL 60611, USA Northwestern Brain Tumor Institute, Northwestern University, 675 N Saint Clair Street, Suite 2100, Chicago, IL 60611, USA
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Moghaddasi L, Bezak E, Harriss-Phillips W. Evaluation of current clinical target volume definitions for glioblastoma using cell-based dosimetry stochastic methods. Br J Radiol 2015; 88:20150155. [PMID: 26140450 DOI: 10.1259/bjr.20150155] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE Determination of an optimal clinical target volume (CTV) is complex and remains uncertain. The aim of this study was to develop a glioblastoma multiforme (GBM) model to be used for evaluation of current CTV practices for external radiotherapy. METHODS The GBM model was structured as follows: (1) a Geant4 cellular model was developed to calculate the absorbed dose in individual cells represented by cubic voxels of 20 μm sides. The system was irradiated with opposing 6 MV X-ray beams. The beams encompassed planning target volumes corresponding to 2.0- and 2.5-cm CTV margins; (2) microscopic extension probability (MEP) models were developed using MATLAB(®) 2012a (MathWorks(®), Natick, MA), based on clinical studies reporting on GBM clonogenic spread; (3) the cellular dose distribution was convolved with the MEP models to evaluate cellular survival fractions (SFs) for both CTV margins. RESULTS A CTV margin of 2.5 cm, compared to a 2.0-cm CTV margin, resulted in a reduced total SF from 12.9% ± 0.9% to 3.6% ± 0.2%, 5.5% ± 0.4% to 1.2% ± 0.1% and 11.1% ± 0.7% to 3.0% ± 0.2% for circular, elliptical and irregular MEP distributions, respectively. CONCLUSION A Monte Carlo model was developed to quantitatively evaluate the impact of GBM CTV margins on total and penumbral SF. The results suggest that the reduction in total SF ranges from 3.5 to 5, when the CTV is extended by 0.5 cm. ADVANCES IN KNOWLEDGE The model provides a quantitative tool for evaluation of different CTV margins in terms of cell kill efficacy. Cellular platform of the tool allows future incorporation of cellular properties of GBM.
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Affiliation(s)
- L Moghaddasi
- 1 Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA, Australia.,2 School of Chemistry & Physics, University of Adelaide, Adelaide, SA, Australia
| | - E Bezak
- 2 School of Chemistry & Physics, University of Adelaide, Adelaide, SA, Australia.,3 School of Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - W Harriss-Phillips
- 1 Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA, Australia.,2 School of Chemistry & Physics, University of Adelaide, Adelaide, SA, Australia
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Radiobiological framework for the evaluation of stereotactic radiosurgery plans for invasive brain tumours. ISRN ONCOLOGY 2014; 2013:527251. [PMID: 24490086 PMCID: PMC3893765 DOI: 10.1155/2013/527251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 10/18/2013] [Indexed: 11/24/2022]
Abstract
This study presents a radiobiological formalism for the evaluation of the treatment plans with respect to the probability of controlling tumours treated with stereotactic radiosurgery accounting for possible infiltrations of malignant cells beyond the margins of the delineated target. Treatments plans devised for three anaplastic astrocytoma cases were assumed for this study representing cases with different difficulties for target coverage. Several scenarios were considered regarding the infiltration patterns. Tumour response was described in terms of tumour control probability (TCP) assuming a Poisson model taking into account the initial number of clonogenic cells and the cell survival. The results showed the strong impact of the pattern of infiltration of tumour clonogens outside the delineated target on the outcome of the treatment. The treatment plan has to take into account the existence of the possible microscopic disease around the visible lesion; otherwise the high gradients around the target effectively prevent the sterilisation of the microscopic spread leading to low probability of control, in spite of the high dose delivered to the target. From this perspective, the proposed framework offers a further criterion for the evaluation of stereotactic radiosurgery plans taking into account the possible infiltration of tumour cells around the visible target.
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Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma. Pediatr Res 2014; 75:302-14. [PMID: 24216542 DOI: 10.1038/pr.2013.217] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Accepted: 06/09/2013] [Indexed: 11/08/2022]
Abstract
BACKGROUND Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. METHODS A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. RESULTS New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. CONCLUSION The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma.
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Strino F, Parisi F, Micsinai M, Kluger Y. TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Res 2013; 41:e165. [PMID: 23892400 PMCID: PMC3783191 DOI: 10.1093/nar/gkt641] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.
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Affiliation(s)
- Francesco Strino
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA, NYU Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, 227 East 30th Street, New York, NY 10016, USA and Yale Cancer Center, New Haven, CT 06520, USA
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Simpson MJ, Treloar KK, Binder BJ, Haridas P, Manton KJ, Leavesley DI, McElwain DLS, Baker RE. Quantifying the roles of cell motility and cell proliferation in a circular barrier assay. J R Soc Interface 2013; 10:20130007. [PMID: 23427098 DOI: 10.1098/rsif.2013.0007] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Moving fronts of cells are essential features of embryonic development, wound repair and cancer metastasis. This paper describes a set of experiments to investigate the roles of random motility and proliferation in driving the spread of an initially confined cell population. The experiments include an analysis of cell spreading when proliferation was inhibited. Our data have been analysed using two mathematical models: a lattice-based discrete model and a related continuum partial differential equation model. We obtain independent estimates of the random motility parameter, D, and the intrinsic proliferation rate, λ, and we confirm that these estimates lead to accurate modelling predictions of the position of the leading edge of the moving front as well as the evolution of the cell density profiles. Previous work suggests that systems with a high λ/D ratio will be characterized by steep fronts, whereas systems with a low λ/D ratio will lead to shallow diffuse fronts and this is confirmed in the present study. Our results provide evidence that continuum models, based on the Fisher-Kolmogorov equation, are a reliable platform upon which we can interpret and predict such experimental observations.
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Affiliation(s)
- Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
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