In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Continuum Models
3.2. Discrete and Hybrid Models
3.3. Clinical Case Study: Primary GBM Tumor
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. GLIOMATH: Clinical and Radiological Data Collection
Appendix A.1. Preoperative Screening, Radiological Protocol and Surgery
Appendix A.2. Postoperative Management
Appendix B. Mathematical Model
References
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---|---|---|
Owen LN (1969) [28] | Relation between cell kinetics and growth of the gross tumor | Tumor growth and cell production vs. cell loss |
Swanson KR (2002) [29] | Quantification of the spatio-temporal growth and invasion of gliomas in three dimensions | Tumor growth and microscopic invasion |
Swanson KR (2000) [5] | Augmented diffusion rates of malignant cells in white matter as compared to grey matter | Pattern of microscopic and submicroscopic invasion of the brain by glioma cells |
Jbabdi S (2005) [30] | Implementation of modeled glioma diffusion by means of introduction of brain anisotropy, as detectable with diffusion tensor imaging | Pattern of glioma cells migration |
Cristini V (2009) [31] | Role of microenvironment vasculature and chemotaxis in glioma invasive behavior | Pattern of tumor invasiveness |
Macklin P (2007) [32] | Implementation the role the properties of microenvironment in detecting cancer morphology | Prediction of tumor 3D morphology and malignant properties |
Harpold HLP (2007) [21] | Analyzing the relation between tumor growth velocity and cellular proliferation rate | Survival time |
Swanson KR (2008) [20] | Analyzing tumor spreading velocity starting from patient-specific MRI | Survival time |
Wang CH (2009) [33] | Quantification of patient-specific kinetic rate of malignant cell proliferation since serial preoperative MRI | Diffusion rate and development of GBM for each patient |
Rockne R (2010) [34] | Incorporating the effect of radiation therapy in mathematical model of glioma growth | Tumor dimension after RT protocol |
Unkelbach J (2014) [35] | Analysis of malignant cell infiltration by means of FLAIR images and prediction of RT response | Optimization of patient-specific radiation therapy and dosing of fall-off rate |
Zhao Y (2015) [36] | Role of angiogenesis in tumor development and aggressiveness | Effect of antiangiogenic drugs |
Saut O (2014) [37] | Role of hypoxia in tumor development and invasion | Prediction of tumor behavior (proliferative vs. invasive phenotype) |
Colombo MC (2015) [23] | Analyzing patient-specific preoperative DTI in revealing personal heterogeneity and anisotropy of brain tissue | Tumor growth |
Lipkova J (2019) [38] | Integration complementary information from MRI and FET-PET to infer tumor cell density in GBM patient to tailor radiotherapy | Individual response to RT |
Acerbi F (2021) [24] | Introducing in a continuous mechanical model, the heterogeneity and the anisotropicity of the brain bundles from patient-specific DTI | Tumor growth, invasion and recurrence |
Authors | Key Features | Prediction |
---|---|---|
Duchting W (1992) [52] | Development of a 3D spheroid tumor model analyzing cellular cycle phases | Tumor response to different RT fractionation schemes |
Wasserman R (1996) [53] | Integrating patient-specific mechanical properties of the tumor, as derived from personal MRI | Tumor growth and neoplastic proliferation |
Kansal AR (2000) [54,55] | Detecting tumor behavior using a three-dimensional cellular automaton model | Tumor growth |
Dionysiou DD (2004) [56] | Integration in a single four-dimensional simulation model several groups of cells in different phases of the cell cycle | Tumor growth and response to RT |
Dionysiou DD (2008) [57] | Incorporation of genetic and molecular factors affecting radiosensitivity | Tumor growth and response to adjuvant therapies |
Zheng X (2005) [58] | Analyzing the relation among neovascularization (tumor angiogenesis) and cellular invasiveness using an adaptive, unstructured finite element mesh | Tumor response to RT and antiangiogenic drugs |
Frieboes HB (2007) [59] | Combination of analytical and stochastic models linking cellular properties and microenvironment vascularization | Tumor growth |
Kim Y (2013) [60] | Analyzing the relation between metabolic stress and biophysical interactions with microenvironment | Experimenting target therapies |
Angeli S (2018) [61] | Combination of cellular events which cause tumor proliferation and migration with biomechanical response at tissue level | Tumor infiltration and distant invasion |
Gallaher JA (2020) [62] | Combination of MRI data to estimate the role of microenvironment with biopsy data to detect molecular cell properties | Prediction of tumor recurrence and effect of adjuvant therapies |
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Falco, J.; Agosti, A.; Vetrano, I.G.; Bizzi, A.; Restelli, F.; Broggi, M.; Schiariti, M.; DiMeco, F.; Ferroli, P.; Ciarletta, P.; et al. In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case. J. Clin. Med. 2021, 10, 2169. https://doi.org/10.3390/jcm10102169
Falco J, Agosti A, Vetrano IG, Bizzi A, Restelli F, Broggi M, Schiariti M, DiMeco F, Ferroli P, Ciarletta P, et al. In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case. Journal of Clinical Medicine. 2021; 10(10):2169. https://doi.org/10.3390/jcm10102169
Chicago/Turabian StyleFalco, Jacopo, Abramo Agosti, Ignazio G. Vetrano, Alberto Bizzi, Francesco Restelli, Morgan Broggi, Marco Schiariti, Francesco DiMeco, Paolo Ferroli, Pasquale Ciarletta, and et al. 2021. "In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case" Journal of Clinical Medicine 10, no. 10: 2169. https://doi.org/10.3390/jcm10102169