Diagnostic Issues in Treating Brain Tumors

  • Nicholas J. PatronasEmail author
  • Athanasios D. Gouliamos


In this chapter we have addressed diagnostic problems that arise during the traetment of gliomas. We have also reviwed the available methods and the imaging features of these abnormalities that have been shown to improve our diagnisstic accuracy. In addition, in this chapter we have provided a brief review of the literature that relates to the genomic alterations of the brain tumors. A number of different biomarkers have been found to play a major role in determining the biological behavior of brain neoplasms with seemingly identical appearance on conventional MR imaging. Such biomarkers have also been used in selecting the appropriate medical management.


  1. 1.
    Patronas NJ, Di Chiro G, Brooks RA et al (1982) [18F]Fluorodeoxyglucose and positron emission tomography in the evaluation of radiation necrosis of the brain. Radiology 144:885–889CrossRefGoogle Scholar
  2. 2.
    Di Chiro G, Oldfield E, Wright DC et al (1987) Cerebral necrosis after radiotherapy and/or intraarterial chemotherapy for brain tumors: PET and neuropathological studies. AJR Am J Roentgenol 8:1083–1091Google Scholar
  3. 3.
    Alexiou GA, Tsiouris S, Kyritsis AP et al (2009) Glioma recurrence versus radiation necrosis: accuracy of current imaging modalities. J Neurooncol 95:1–11CrossRefGoogle Scholar
  4. 4.
    Nguyen HS, Mibach N, Hurrell SL et al (2016) Progressing bevacizumab-induced diffusion restriction is associated with coagulative necrosis surrounded by viable tumor and decreased overall survival in patients with recurrent glioblastoma. AJNR Am J Neuroradiol 37:2201–2208CrossRefGoogle Scholar
  5. 5.
    Ho SK, Myeong JG, Namkug K et al (2014) Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 273(3):831–843CrossRefGoogle Scholar
  6. 6.
    Dennis G Jr, Sherman BT, Hosack DA et al (2003) DAVID: database for annotation, visualization, and integrated discover. Genome Biol 4:P3CrossRefGoogle Scholar
  7. 7.
    Verhaak RGW, Hoadley KA, Purdon E et al (2010) An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGER and NF1. Cancer Cell 17:98–110CrossRefGoogle Scholar
  8. 8.
    Gevaert O, Mitchell LA, Achrol AS et al (2014) Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273(1):168–174CrossRefGoogle Scholar
  9. 9.
    Diehn M, Nardini C, Wang DS et al (2008) Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 105(13):5213–5218CrossRefGoogle Scholar
  10. 10.
    Pope WB, Chen JH, Dong J (2008) Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. Radiology 249(1):268–277CrossRefGoogle Scholar
  11. 11.
    Itakura H, Achrol AS, Mitchell LA et al (2015) Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 7(303):303ra138CrossRefGoogle Scholar
  12. 12.
    Gutman DA, Cooper LAD, Hwang SN et al (2013) MR imaging predictors of molecular profiles and survival: Multi-institutional study of the TCGA glioblastoma data set. Radiology 267(2):560–569CrossRefGoogle Scholar
  13. 13.
    Zinn PO, Majadan B, Sathyan P et al (2011) Radiogenomics mapping of edema/ cellular invasion MRI-phenotype in glioblastoma multiforme. PLoS One 6(10):e25451CrossRefGoogle Scholar
  14. 14.
    Barajas RF Jr, Hodgson JG, Dong J et al (2010) Glioblastomas multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. Radiology 254(2):564–576CrossRefGoogle Scholar
  15. 15.
    Qi S, Yu L, Li L et al (2014) Isocitrate dehydrogenase mutation is associated with tumor location and magnetic resonance imaging characteristics in astrocytic neoplasms. Oncol Lett 7(6):1895–1902CrossRefGoogle Scholar
  16. 16.
    Brown R, Zlatescu M, Sijben A et al (2008) The use of magnetic resonance imaging to noninvasively detect genetic signature in oligodendroglioma. Clin Cancer Res 14(8):2357–2362CrossRefGoogle Scholar
  17. 17.
    Carrillo JA, Lai A, Nghiemphu PL (2012) Relationship between tumor enhancement, IDH1 mutation status, MGMT promoter methylation and survival in glioblastomas. AJNR Am J Neuroradiol 33:1349–1355CrossRefGoogle Scholar
  18. 18.
    Korfiatis P, Kline TL, Coufalova L et al (2016) MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys 43(6):2835–2844CrossRefGoogle Scholar
  19. 19.
    Drabycz S, Roldan G, de Robles P et al (2010) An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging. Neuroimage 49(2):1398–1405CrossRefGoogle Scholar
  20. 20.
    Goyen M (2014) Radiogenomic imaging-linking diagnostic imaging and molecular diagnostics. World J Radiol 6(8):519–522CrossRefGoogle Scholar
  21. 21.
    Macyszyn L, Akbari H, Pisaria JM et al (2016) Imaging patterns predict patient survival and molecular subtypes in glioblastoma via machine learning techniques. Neuro Oncol 18(3):417–425CrossRefGoogle Scholar
  22. 22.
    Zhang B, Chang K, Ramkinssoon S et al (2016) Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 19(1):109–117CrossRefGoogle Scholar
  23. 23.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577CrossRefGoogle Scholar
  24. 24.
    Gatenby RA, Grove O, Gilles RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269(1):8–15CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nicholas J. Patronas
    • 1
    Email author
  • Athanasios D. Gouliamos
    • 2
  1. 1.Neuroradiology Section, Radiology and Imaging SciencesNational Institute of HealthBethesdaUSA
  2. 2.School of MedicineNational and Kapodistrian University of AthensAthensGreece

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