A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle
TLDR
The concordance between predicted and actual survivals suggests that the mathematical model is realistic enough to allow precise definition of the effectiveness of individualised treatments and their site(s) of action on proliferation (ρ) and/or dispersal (D) of the tumour cells without knowledge of any other clinical or pathological information.Abstract:
The prediction of the outcome of individual patients with glioblastoma would be of great significance for monitoring responses to therapy. We hypothesise that, although a large number of genetic-metabolic abnormalities occur upstream, there are two ‘final common pathways' dominating glioblastoma growth – net rates of proliferation (ρ) and dispersal (D). These rates can be estimated from features of pretreatment MR images and can be applied in a mathematical model to predict tumour growth, impact of extent of tumour resection and patient survival. Only the pre-operative gadolinium-enhanced T1-weighted (T1-Gd) and T2-weighted (T2) volume data from 70 patients with previously untreated glioblastoma were used to derive a ratio D/ρ for each patient. We developed a ‘virtual control' for each patient with the same size tumour at the time of diagnosis, the same ratio of net invasion to proliferation (D/ρ) and the same extent of resection. The median durations of survival and the shapes of the survival curves of actual and ‘virtual' patients subjected to biopsy or subtotal resection (STR) superimpose exactly. For those actually receiving gross total resection (GTR), as shown by post-operative CT, the actual survival curve lies between the ‘virtual' results predicted for 100 and 125% resection of the T1-Gd volume. The concordance between predicted (virtual) and actual survivals suggests that the mathematical model is realistic enough to allow precise definition of the effectiveness of individualised treatments and their site(s) of action on proliferation (ρ) and/or dispersal (D) of the tumour cells without knowledge of any other clinical or pathological information.read more
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Nonlinear modelling of cancer: Bridging the gap between cells and tumours
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TL;DR: In this paper, the authors provide an overview of multiscale modelling focusing on the growth phase of tumours and bypassing the initial stage of tumourigenesis, and limit the scope further by considering models of tumor progression that do not distinguish tumour cells by their age and do not consider immune system interactions nor do they describe models of therapy.
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Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach
Russell C. Rockne,Jason K. Rockhill,Maciej M. Mrugala,Alexander M. Spence,Ira J. Kalet,K Hendrickson,Albert Lai,Timothy F. Cloughesy,E C Alvord,Kristin R. Swanson +9 more
TL;DR: It is suggested that a mathematical model can create a virtual in silico tumor with the same growth kinetics as a particular patient and can not only predict treatment response in individual patients in vivo but also provide a basis for evaluation of response in each patient to any given therapy.
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Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology
Kristin R. Swanson,Russell C. Rockne,Jonathan Claridge,Mark A. J. Chaplain,Ellsworth C. Alvord,Alexander R. A. Anderson +5 more
TL;DR: Through the application of the proposed model for tumor-microenvironment interactions, predictable patterns of dynamic changes in glioma histology distinct from changes in cellular phenotype may be identified, thus providing a powerful clinical tool.
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Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical Model
Christina Wang,Jason K. Rockhill,Maciej M. Mrugala,Danielle L. Peacock,Albert Lai,Katy Jusenius,Joanna M. Wardlaw,Timothy F. Cloughesy,Alexander M. Spence,Russell C. Rockne,Ellsworth C. Alvord,Kristin R. Swanson +11 more
TL;DR: This is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma.
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Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations
Ender Konukoglu,Olivier Clatz,Bjoern H. Menze,Bram Stieltjes,Marc-André Weber,Emmanuel Mandonnet,Hervé Delingette,Nicholas Ayache +7 more
TL;DR: A parameter estimation method for reaction-diffusion tumor growth models using time series of medical images and it is shown that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells.
References
More filters
Journal ArticleDOI
A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival
Michel Lacroix,Dima Abi-Said,Daryl R. Fourney,Ziya L. Gokaslan,Weiming Shi,Franco DeMonte,Frederick F. Lang,Ian E. McCutcheon,Samuel J. Hassenbusch,Eric C. Holland,Kenneth R. Hess,Christopher Michael,Daniel J. Miller,Raymond Sawaya +13 more
TL;DR: Gross-total tumor resection is associated with longer survival in patients with GBM, especially when other predictive variables are favorable.
Journal ArticleDOI
Design and construction of a realistic digital brain phantom
D. L. Collins,Alex P. Zijdenbos,V. Kollokian,John G. Sled,Noor Jehan Kabani,Colin J. Holmes,Alan C. Evans +6 more
TL;DR: The authors present a realistic, high-resolution, digital, volumetric phantom of the human brain, which can be used to simulate tomographic images of the head and is the ideal tool to test intermodality registration algorithms.
Journal ArticleDOI
The WHO Classification of Tumors of the Nervous System
Paul Kleihues,David N. Louis,Bernd W. Scheithauer,Lucy B. Rorke,Guido Reifenberger,Peter C. Burger,Webster K. Cavenee +6 more
TL;DR: The new World Health Organization (WHO) classification of nervous system tumors, published in 2000, emerged from a 1999 international consensus conference of neuropathologists, and new entities include chordoid glioma of the third ventricle, cerebellar liponeurocytoma, atypical teratoid/rhabdoid tumor, and perineurioma.
Journal ArticleDOI
Commentary on the WHO Classification of Tumors of the Nervous System
TL;DR: The new World Health Organization (WHO) classification of nervous system tumors, published in 2000, emerged from a 1999 international consensus conference of neuropathologists, and new entities include chordoid glioma of the third ventricle, cerebellar liponeurocytoma, atypical teratoid/rhabdoid tumor, and perineurioma.