Cancer modelling
Free Access
Math. Model. Nat. Phenom.
Volume 15, 2020
Cancer modelling
Article Number 10
Number of page(s) 19
Published online 28 February 2020
  1. S. Abrol et al., Radiomic phenotyping in brain cancer to unravel hidden information in medical images. Top. Magn. Reson. Imag. 26 (2017) 43–53. [Google Scholar]
  2. J.C.L. Alfonso et al., The biology and mathematical modelling of glioma invasion: a review. J. R. Soc. Interface. 14 (2017) 20170490. [CrossRef] [PubMed] [Google Scholar]
  3. A. Alvarez-Arenas, J. Belmonte-Beitia and G.F. Calvo, Nonlinear waves in a simple model of high-grade glioma. Appl. Math. Nonlinear Sc. 1 (2016) 405–422. [CrossRef] [Google Scholar]
  4. D.G. Altman, Practical Statistics for Medical Research, 4th edn. Chapman & Hall, London (1991). [Google Scholar]
  5. A. Amelot et al., Surgical decision making from image-based biophysical modeling of glioblastoma: not ready for primetime. Neurosurgery 80 (2017) 793–799. [CrossRef] [PubMed] [Google Scholar]
  6. A. Baldock et al., From patient-specific mathematical neuro-oncology to precision medicine. Front. Oncol. 3 (2013) 62. [CrossRef] [Google Scholar]
  7. A. Baldock et al., Patient-specific metrics of invasiveness reveal significant prognostic benefit of resection in a predictable subset of gliomas. PLoS One 9 (2014) e99057. [CrossRef] [PubMed] [Google Scholar]
  8. P.K. Burgess et al., The interaction of growth rates and diffusion coefficients in a three-dimensional mathematical model of gliomas. J. Neuropathol. 56 (1997) 703–740. [CrossRef] [Google Scholar]
  9. R. Corless, G.H. Gonnet, D.E.G. Hare, D.J. Jeffrey and D.E. Knuth, On the Lambert function. Adv. Comput. Math. 5 (1996) 329–359. [Google Scholar]
  10. Y. Cui et al., Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 278 (2016) 546–553. [CrossRef] [PubMed] [Google Scholar]
  11. Y. Cui et al., Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur. Radiol. 27 (2017) 3583–3592. [CrossRef] [PubMed] [Google Scholar]
  12. D.A. Darling, The Kolmogorov-Smirnov, Cramer-von Mises Tests. J. Stat. Model. Anal. 28 (1957) 823–838. [Google Scholar]
  13. B.M. Ellingson, Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr. Neurol. Neurosci. Rep. 15 (2015) 506. [CrossRef] [PubMed] [Google Scholar]
  14. B.M. Ellingson et al., Emerging techniques and technologies in brain tumor imaging. Neuro. Oncol. 16 (2014) 12–23. [Google Scholar]
  15. B.M. Ellingson et al., Baseline pretreatment contrast enhancing tumor volume including central necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials. Neuro. Oncol. 19 (2017) 89–98. [CrossRef] [PubMed] [Google Scholar]
  16. D.L. Evans, J.H. Drew and L.M. Leemis, The distribution of the Kolmogorov-Smirnov, Cramer-von mises, and Anderson-Darling Test Statistic for exponential populations with estimated parameters. Commun. Stat. Simul. Comput. 3 (2007) 1396–1421. [Google Scholar]
  17. R.J. Gillies, P.E. Kinahan and H. Hricak, Radiomics: images are more than pictures, they are data. Radiology. 278 (2016) 563–577. [CrossRef] [PubMed] [Google Scholar]
  18. M. Ingrisch et al., Radiomic analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest. Radiol. 52 (2017) 360–366. [Google Scholar]
  19. P. Kickingereder et al., Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280 (2016) 880–889. [CrossRef] [PubMed] [Google Scholar]
  20. R.L. Klank, S.S. Rosenfeld and D.J. Odde, A Brownian dynamics tumor progression simulator with application to glioblastoma. Converg. Sci. Phys. Oncol. 4 (2018) 015001. [CrossRef] [PubMed] [Google Scholar]
  21. J. Lao et al., A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci. Rep. 7 (2017) 10353. [CrossRef] [PubMed] [Google Scholar]
  22. Q. Li et al., A fully-automatic multiparametric radiomics model: towards reproducible and prognostic imaging signature for prediction of overall survival in glioblastoma multiforme. Sci. Rep. 7 (2017) 14331. [CrossRef] [PubMed] [Google Scholar]
  23. D.N. Louis et al., The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114 (2007) 97–109. [CrossRef] [PubMed] [Google Scholar]
  24. A. Martínez-González, G.F. Calvo, L. Pérez-Romasanta and V.M. Pérez-García, Hypoxic cell waves around necrotic cores in glioblastoma: a mathematical model and its therapeutical implications. Bull. Math. Biol. 74 (2012) 2875–2896. [Google Scholar]
  25. A. Martínez-González, G.F. Calvo, J.M. Ayuso, I. Ochoa, L.J. Fernández and V.M. Pérez-García, Hypoxia in gliomas: opening therapeutic opportunities using a mathematical-based approach. Adv. Exp. Med. Biol. 936 (2016) 11–29. [CrossRef] [PubMed] [Google Scholar]
  26. D. Molina et al., Geometrical measures obtained from pretreatment postcontrast T1 weighted MRIs predict survival benefits from bevacizumab in glioblastoma patients. PLoS One 11 (2016) e0161484. [CrossRef] [PubMed] [Google Scholar]
  27. D. Molina, L. Vera, J. Pérez-Beteta, E. Arana and V.M. Pérez-García, Survival prediction in glioblastoma: man versus machine. Scientific Report n°5982 (2019). [Google Scholar]
  28. J. Murray, Mathematical Biology. Springer, Berlin (2003). [CrossRef] [Google Scholar]
  29. S. Narang et al., Radiomics in glioblastoma: current status, challenges and opportunities. Trasl. Cancer Res. 5 (2016) 383–397. [CrossRef] [Google Scholar]
  30. J. Pérez-Beteta et al., Glioblastoma: does the pretreatment geometry matter? A postcontrast T1 MRI-based study. Eur. Radiol. 27 (2017) 163–169. [Google Scholar]
  31. J. Pérez-Beteta et al., Tumor surface regularity at MR imaging predicts survival and response to surgery in patients with glioblastoma. Radiology 288 (2018) 218–225. [CrossRef] [PubMed] [Google Scholar]
  32. J. Pérez-Beteta et al., Morphological MRI-based features and extent of resection predict survival in glioblastoma. Eur. Radiol. 29 (2019) 1968–1977. [CrossRef] [PubMed] [Google Scholar]
  33. J. Pérez-Beteta, A. Mártinez-González and V.M. Pérez-García, A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours. J. R. Soc. Interface 15 (2018) 20180503. [CrossRef] [PubMed] [Google Scholar]
  34. J. Pérez-Beteta et al., Morphological features on MR images classify multiple glioblastomas in different prognostic groups. Am. J. Radiol. 40 (2019) 634–640. [Google Scholar]
  35. V.M. Pérez-García, G.F. Calvo, J. Belmonte-Beitia, D. Diego and L. Pérez-Romasanta, Bright solitary waves in malignant gliomas. Phys. Rev. E 84 (2011) 021921. [Google Scholar]
  36. M. Protopapa et al., Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J. Neurooncol. 136 (2018) 1–11. [PubMed] [Google Scholar]
  37. N.M. Razali and Y.B. Wah, Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2 (2011) 21–33. [Google Scholar]
  38. P. Sprent and N.C. Smeeton, Applied Nonparametric Statistical Methods. Chapman & Hall, London (2007). [Google Scholar]
  39. S. Strogatz, Nonlinear Dynamics and Chaos: Studies in Nonlinearity. CRC Press, Boca Raton (2007). [Google Scholar]
  40. K.R. Swanson, R.C. Rostomily and E.C. Alvord Jr., A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle. Br. J. Cancer 98 (2007) 113–119. [CrossRef] [PubMed] [Google Scholar]
  41. V. Verma et al., The rise of radiomics and implications for oncologic management. J. Natl. Cancer Inst. 109 (2017). [Google Scholar]
  42. C.H. Wang et al., Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. Cancer Res. 69 (2009) 9133–9140. [Google Scholar]
  43. P. Wangaryattawanich et al., Multicenter imaging outcomes study of the cancer genome atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro. Oncol. 17 (2015) 1525–1537. [CrossRef] [PubMed] [Google Scholar]
  44. M. Zhou et al., Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction. J. Magn. Reson. Imag. 46 (2017) 115–123. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.