Archived Issues

We congratulate you on acceptance of your manuscript.

Prediction Analysis of Glioblastoma Multiforme Tumor Spread

March 17, 2022
Giovanna Napoleone

Abstract: Glioblastoma multiforme is a malignant grade 4 brain tumor that is almost always fatal and is typically characterized by the excessive growth of necrotic areas within the tumor’s microenvironment. Therefore, the relationship between healthy tumor cells and spread of necrotic tumor cells from the Ivy Glioblastoma Atlas Project (IvyGAP) database was analyzed using ImageJ both linearly and within the fractal dimension to assess growth predictability. Both the linear regression (y = 6.99x + 490000 µm) and fractal dimension complexity value regression (y = -7.01x + 40.8 µm) were found to be significant (p < 0.5), demonstrating a future possibility of predicting tumor growth spread based on necrotic growth, possibly due to necrotic area being interrelated to tumor vasculature. If necrotic area growth can be mathematically characterized by healthy tumor cell area growth, glioblastoma multiforme disease stage development may be better predicted prior to tumor cell expansion, resulting in better means to prevent further proliferation of the disease.

Keywords:  loneliness, social isolation, resilience, whole school, whole community, whole child, mindfulness, cognitive behavioral therapy, social support, community dogs, mental health


References

  1. Ahn, S.-H., Park, H., Ahn, Y.-H., Kim, S., Cho, M.-S., Kang, J. L., & Choi, Y.-H. (2016).

  2. Necrotic cells influence migration and invasion of glioblastoma via NF-κB/AP-1-mediated IL-8 regulation. Scientific Reports, 6, 24552.

  3. Alfonso, J. C. L., Talkenberger, K., Seifert, M., Klink, B., Hawkins-Daarud, A., Swanson, K. R., Hatzikirou, H., & Deutsch, A. (2017). The biology and mathematical modelling of glioma invasion: a review. Journal of the Royal Society, Interface / the Royal Society, 14(136). https://doi.org/10.1098/rsif.2017.0490

  4. Allen Institute for Brain Science (2015). Ivy Glioblastoma Atlas Project.Glioblastoma.alleninstitute.org

  5. Bianciardi G., Sorce F., Pontenani A., Ginori F., Scaramuzzino, S., & Tripodi. (2018). Fractal Approaches to Image Analysis in Oncopathology. In Austin Journal of Medical Oncology, 5(2): 1040. Retrieved from https://www.researchgate.net/publication/343079726_Fractal_Approaches_to_Image_An alysis_in_Oncopathology

  6. Bohman, L.-E., Swanson, K. R., Moore, J. L., Rockne, R., Mandigo, C., Hankinson, T., Assanah, M., Canoll, P., & Bruce, J. N. (2010). Magnetic resonance imaging characteristics of glioblastoma multiforme: implications for understanding glioma ontogeny. In Neurosurgery (Vol. 67, Issue 5, pp. 1319–1328)

  7. Brat, D.J., Castellano-Sanchez, A. A., Hunter, S., Pecot, M., Cohen, C.,

  8. Hammond, E. H., Devi, S.N., Kaur, B., & Van Meir, E. G. (2004). Pseudopalisades in Glioblastoma Are Hypoxic, Express Extracellular Matrix Proteases, and Are Formed by an Actively Migrating Cell Population. In Cancer Research, (64) (3) 920-927. Retrieved from https://doi.org/10.1158/0008-5472.CAN-03-2073

  9. Chapter 4: Calculating Fractal Dimensions. (n.d.). Fractal Explorer. Retrieved from https://www.wahl.org/fe/HTML_version/link/FE4W/c4.htm

  10. Clark, K., Vendt, B., Smith, K., et al. (2013) The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In the Journal of Digital Imaging, 26(6): 1045-1057. https://doi.org/10.1007/s10278-013-9622-7.

  11. De Vleeschouwer S., Bergers G. (2017) Glioblastoma: To Target the Tumor Cell or the Microenvironment? In Codon Publications, Figure 1. Retrieved from: https://www.ncbi.nlm.nih.gov/books/NBK469984/figure/chapter16.f1/doi: 10.15586/codon.glioblastoma.2017.ch16

  12. Falconer, Kenneth (2013). Fractals: A Very Short Introduction. Retrieved from https://global.oup.com/academic/product/fractals-a-very-short-introduction-97801996759 82

  13. Ferreira, T., Rasband, W. (2012). ImageJ User Guide. Retrieved from https://imagej.nih.gov/ij/docs/guide/user-guide.pdf

  14. FracLac for ImageJ (2004-2005). Retrieved from https://imagej.nih.gov/ij/plugins/fraclac/fraclac-manual.pdf

  15. Hambardzumyan, D., & Bergers, G. (2015). Glioblastoma: Defining Tumor Niches. Trends in Cancer Research, 1(4), 252–265.

  16. Hanif, F., Muzaffar, K., Perveen, K., Malhi, S. M., & Simjee, S. U. (2017). Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through Clinical Presentation and Treatment. Asian Pacific Journal of Cancer Prevention: APJCP, 18(1), 3–9.

  17. Horsfield, K., Dart, G., Olson, D.E., Filley, G. F., & Cumming, G. (1971). Models of the human bronchial tree. In Journal of Applied Physiology, 31:2, 207-217. Retrieved from https://journals.physiology.org/doi/pdf/10.1152/jappl.1971.31.2.207

  18. Ishii, A., Kimura, T., Sadahiro, H., Kawano, H., Takubo, K., Suzuki, M., & Ikeda, E. (2016).

  19. Histological Characterization of the Tumorigenic “Peri-Necrotic Niche” Harboring Quiescent Stem-Like Tumor Cells in Glioblastoma. In PLOS ONE (Vol. 11, Issue 1, p. e0147366). https://doi.org/10.1371/journal.pone.0147366

  20. Johansson, E., Grassi, E. S., Pantazopoulou, V., Tong, B., Lindgren, D., Berg, T. J., Pietras, E. J., Axelson, H., & Pietras, A. (2017). CD44 Interacts with HIF-2α to Modulate the Hypoxic Phenotype of Perinecrotic and Perivascular Glioma Cells. In Cell Reports (Vol. 20, Issue 7, pp. 1641–1653)