Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Imaging in Neuro-Oncology

  • Chapter
  • First Online:
Neurorehabilitation in Neuro-Oncology

Abstract

In 2016 the World Health Organization released an update of the classification of brain tumors, based on the increased understanding, over the past two decades, of the genetic basis of tumorigenesis. The new classification, for the first time, integrated both genotypic and phenotypic parameters, leading to a molecular stratification of brain tumors, with significant implications not only for neuropathological diagnosis, but also for prognosis and therapy. The knowledge of this new classification and of the molecular markers that correlate with specific tumor subtypes is essential for radiologist. Understanding how these molecular phenotypes are reflected on imaging is thus becoming increasingly important to define novel magnetic resonance imaging biomarkers that can help clinical decision-making. To this end, advanced imaging techniques, such as spectroscopy, diffusion-weighted imaging, diffusion tensor imaging, perfusion imaging, and functional magnetic resonance imaging, have shown to be promising to increase the accuracy of molecular subtyping of brain tumors by conventional magnetic resonance imaging.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131:803–20.

    Article  Google Scholar 

  2. Castellano A, Falini A. Progress in neuro-imaging of brain tumors. Curr Opin Oncol. 2016;28:484–93.

    Article  CAS  Google Scholar 

  3. Hempel JM, Schittenhelm J, Klose U, Bender B, Bier G, Skardelly M, et al. In vivo molecular profiling of human glioma: cross-sectional observational study using dynamic susceptibility contrast magnetic resonance perfusion imaging. Clin Neuroradiol. 2018:21.

    Google Scholar 

  4. Reuss DE, Kratz A, Sahm F, Capper D, Schrimpf D, Koelsche C, et al. Adult IDH wild type astrocytomas biologically and clinically resolve into other tumor entities. Acta Neuropathol. 2015;130:407–17.

    Article  CAS  Google Scholar 

  5. Reuss DE, Mamatjan Y, Schrimpf D, Capper D, Hovestadt V, Kratz A, et al. IDH mutant diffuse and anaplastic astrocytomas have similar age at presentation and little difference in survival: a grading problem for WHO. Acta Neuropathol. 2015;129:867–73.

    Article  CAS  Google Scholar 

  6. Ebrahimi A, Skardelly M, Bonzheim I, Ott I, Mühleisen H, Eckert F, et al. ATRX immunostaining predicts IDH and H3F3A status in gliomas. Acta Neuropathol Commun. 2016;4:60.

    Article  Google Scholar 

  7. Pekmezci M, Rice T, Molinaro AM, Walsh KM, Decker PA, Hansen H, et al. Adult infiltrating gliomas with WHO 2016 integrated diagnosis: additional prognostic roles of ATRX and TERT. Acta Neuropathol. 2017;133:1001–16.

    Article  CAS  Google Scholar 

  8. Abedalthagafi M, Phillips JJ, Kim GE, Mueller S, Haas-Kogen DA, Marshall RE, et al. The alternative lengthening of telomere phenotype is significantly associated with loss of ATRX expression in high-grade pediatric and adult astrocytomas: a multi-institutional study of 214 astrocytomas. Mod Pathol. 2013;26:1425–32.

    Article  CAS  Google Scholar 

  9. Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352:997–1003.

    Article  CAS  Google Scholar 

  10. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352:987–96.

    Article  CAS  Google Scholar 

  11. van den Bent MJ, Baumert B, Erridge SC, Vogelbaum MA, Nowak AK, Sanson M, et al. Interim results from the CATNON trial (EORTC study 26053-22054) of treatment with concurrent and adjuvant temozolomide for 1p/19q non-co-deleted anaplastic glioma: a phase 3, randomised, open-label intergroup study. Lancet. 2017;390:1645–53.

    Article  Google Scholar 

  12. Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology. 2015;276:313.

    Article  Google Scholar 

  13. Falini A, Romano A, Bozzao A. Tumours. Neurol Sci. 2008;29:S327–32.

    Article  Google Scholar 

  14. Del Sole A, Falini A, Ravasi L, Ottobrini L, De Marchis D, Bombardieri E, et al. Anatomical and biochemical investigation of primary brain tumours. Eur J Nucl Med. 2001;28:1851–72.

    Article  Google Scholar 

  15. Jacobs AH, Kracht LW, Gossmann A, Rüger MA, Thomas AV, Thiel A, et al. Imaging in neurooncology. NeuroRx. 2005;2:333–47.

    Article  Google Scholar 

  16. Cha S. Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol. 2006;27:475–87.

    CAS  PubMed  Google Scholar 

  17. Lin Y, Xing Z, She D, Yang X, Zheng Y, Xiao Z, et al. IDH mutant and 1p/19q co-deleted oligodendrogliomas: tumor grade stratification using diffusion-, susceptibility-, and perfusion-weighted MRI. Neuroradiology. 2017;59:555–62.

    Article  Google Scholar 

  18. Ellingson BM, Lai A, Harris RJ, Selfridge JM, Yong WH, Das K, et al. Probabilistic radiographic atlas of glioblastoma phenotypes. AJNR Am J Neuroradiol. 2013;34:533–40.

    Article  CAS  Google Scholar 

  19. Han Y, Yan LF, Wang XB, Sun YZ, Zhang X, Liu ZC, et al. Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis. BMC Cancer. 2018;18:215.

    Article  Google Scholar 

  20. Xi YB, Guo F, Xu ZL, Li C, Wei W, Tian P, et al. Radiomics signature: a potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. J Magn Reson Imaging. 2018;47:1380–7.

    Article  Google Scholar 

  21. Demaerel P, Johannik K, Van Hecke P, Van Ongeval C, Verellen S, Marchal G, et al. Localized 1H NMR spectroscopy in fifty cases of newly diagnosed intracranial tumors. J Comput Assist Tomogr. 1991;15:67–76.

    Article  CAS  Google Scholar 

  22. Negendank WG, Sauter R, Brown TR, Evelhoch JL, Falini A, Gotsis ED, et al. Proton magnetic resonance spectroscopy in patients with glial tumors: a multicenter study. J Neurosurg. 1996;84:449–58.

    Article  CAS  Google Scholar 

  23. Preul MC, Caramanos Z, Collins DL, Villemure JG, Leblanc R, Olivier A, et al. Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy. Nat Med. 1996;2:323–5.

    Article  CAS  Google Scholar 

  24. Somorjai RL, Dolenko B, Nikulin AK, Pizzi N, Scarth G, Zhilkin P, et al. Classification of 1H MR spectra of human brain neoplasms: the influence of preprocessing and computerized consensus diagnosis on classification accuracy. J Magn Reson Imaging. 1996;6:437–44.

    Article  CAS  Google Scholar 

  25. Tedeschi G, Lundbom N, Raman R, Bonavita S, Duyn JH, Alger JR, et al. Increased choline signal coinciding with malignant degeneration of cerebral gliomas: a serial proton magnetic resonance spectroscopy imaging study. J Neurosurg. 1997;87:516–24.

    Article  CAS  Google Scholar 

  26. Dowling C, Bollen AW, Noworolski SM, McDermott MW, Barbaro NM, Day MR, et al. Preoperative proton MR spectroscopic imaging of brain tumors: correlation with histopathologic analysis of resection specimens. AJNR Am J Neuroradiol. 2001;22:604–12.

    CAS  PubMed  Google Scholar 

  27. Dang L, White DW, Gross S, Bennett BD, Bittinger MA, Driggers EM, et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature. 2009;462:739–44.

    Article  CAS  Google Scholar 

  28. Choi C, Ganji SK, DeBerardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, et al. 2-Hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med. 2012;18:624–9.

    Article  CAS  Google Scholar 

  29. Andronesi OC, Kim G, Gerstner E, Batchelor T, Tzika AA, Fantin VR, et al. Detection of 2-hydoxyglutarate in IDH-mutated glioma patients by spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med. 2012;4:116ra4.

    Article  Google Scholar 

  30. Emir UE, Larkin SJ, De Pennington N, Voets N, Plaha P, Stacey R, et al. Noninvasive quantification of 2-hydroxyglutarate in human gliomas with IDH1 and IDH2 mutations. Cancer Res. 2016;76:43–9.

    Article  CAS  Google Scholar 

  31. Pope WB, Prins RM, Thomas MA, Nagarajan R, Yen KE, Bittinger MA, et al. Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy. J Neurooncol. 2012;107:197–205.

    Article  CAS  Google Scholar 

  32. Leather T, Jenkinson MD, Das K, Poptani H. Magnetic resonance spectroscopy for detection of 2-hydroxyglutarate as a biomarker for IDH mutation in gliomas. Metabolites. 2017;7:29.

    Article  Google Scholar 

  33. Tsuruda J, Chew W, Moseley M, Norman D. Diffusion-weighted MR imaging of the brain: value of differentiating between extra-axial cysts and epidermoid tumors. AJNR Am J Neuroradiol. 1990;11:925–31.

    CAS  PubMed  Google Scholar 

  34. Maeda M, Kawamura Y, Tamagawa Y, Matsuda T, Itoh S, Kimura H, et al. Intravoxel incoherent motion (IVIM) MRI in intracranial, extra-axial tumors and cysts. J Comput Assist Tomogr. 1992;16:514–8.

    Article  CAS  Google Scholar 

  35. Filippi CG, Edgar MA, Ulug A, Prowda JC, Heier LA, Zimmerman RD. Appearance of meningiomas on diffusion- weighted images: correlating diffusion constants with histopathologic findings. AJNR Am J Neuroradiol. 2001;22:65–72.

    CAS  PubMed  Google Scholar 

  36. Tien R, Felsberg G, Friedman H, Brown M, MacFall J. MR imaging of high grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences. AJR Am J Roentgenol. 1994;162:671–7.

    Article  CAS  Google Scholar 

  37. Brunberg J, Chenevert T, Mckeever P, Ross DA, Junck LR, Muraszko KM, et al. In vivo MR determination of water diffusion coefficients and diffusion anisotropy: correlation with structural alteration in gliomas of the cerebral hemisphere. AJNR Am J Neuroradiol. 1995;16:361–7.

    CAS  PubMed  Google Scholar 

  38. Yang D, Korogi Y, Sugahara T, Kitajima M, Shigematsu Y, Liang L, et al. Cerebral gliomas: prospective comparison of multivoxel 2D chemical-shift imaging proton MR spectroscopy, echoplanar perfusion and diffusion weighted MRI. Neuroradiology. 2002;44:656–66.

    Article  CAS  Google Scholar 

  39. Bulakbasi N, Kocaoglu M, Ors F, Tayfun C, Uçöz T. Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. AJNR Am J Neuroradiol. 2003;24:225–33.

    PubMed  Google Scholar 

  40. Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, et al. The role of diffusion weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22:1081–8.

    CAS  PubMed  Google Scholar 

  41. Thust SC, Hassanein S, Bisdas S, Rees JH, Hyare H, Maynard JA, et al. Apparent diffusion coefficient for molecular subtyping of non-gadolinium-enhancing WHO grade II/III glioma: volumetric segmentation versus two-dimensional region of interest analysis. Eur Radiol. 2018;28:3779–88.

    Article  CAS  Google Scholar 

  42. Villanueva-Meyer JE, Wood MD, Choi BS, Mabray MC, Butowski NA, Tihan T, et al. MRI features and IDH mutational status of grade II diffuse gliomas: impact on diagnosis and prognosis. AJR Am J Roentgenol. 2018;210:621–8.

    Article  Google Scholar 

  43. Ahn SS, Shin NY, Chang JH, Kim SH, Kim EH, Kim DW, et al. Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging. J Neurosurg. 2014;121:367–73.

    Article  Google Scholar 

  44. Gupta A, Omuro AM, Shah AD, Graber JJ, Shi W, Zhang Z, et al. Continuing the search for MR imaging biomarkers for MGMT promoter methylation status: conventional and perfusion MRI revisited. Neuroradiology. 2012;54:641–3.

    Article  Google Scholar 

  45. Moon WJ, Choi JW, Roh HG, Lim SD, Koh YC. Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging. Neuroradiology. 2012;54:555–63.

    Article  Google Scholar 

  46. Romano A, Calabria LF, Tavanti F, Minniti G, Rossi-Espagnet MC, Coppola V, et al. Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: correlation with MGMT promoter methylation status. Eur Radiol. 2013;23:513–20.

    Article  Google Scholar 

  47. Rundle-Thiele D, Day B, Stringer B, Fay M, Martin J, Jeffree RL, et al. Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method. J Med Radiat Sci. 2015;62:92–8.

    Article  Google Scholar 

  48. Choi YS, Ahn SS, Kim DW, Chang JH, Kang SG, Kim EH, et al. Incremental prognostic value of ADC histogram analysis over MGMT promoter methylation status in patients with glioblastoma. Radiology. 2016;281:175–84.

    Article  Google Scholar 

  49. Sunwoo L, Choi SH, Park CK, Kim JW, Yi KS, Lee WJ, et al. Correlation of apparent diffusion coefficient values measured by diffusion MRI and MGMT promoter methylation semiquantitatively analyzed with MS-MLPA in patients with glioblastoma multiforme. J Magn Reson Imaging. 2013;37:351–8.

    Article  Google Scholar 

  50. Pope WB, Lai A, Mehta R, Kim HJ, Qiao J, Young JR, et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol. 2011;32:882–9.

    Article  CAS  Google Scholar 

  51. Laundre BJ, Jellison BJ, Badie B, Alexander AL, Field AS. Diffusion tensor imaging of corticospinal tract before and after mass resection as correlated with clinical motor findings: preliminary data. AJNR Am J Neuroradiol. 2005;26:791–6.

    PubMed  Google Scholar 

  52. Field AS, Alexander AL, Wu YC, Hasan KM, Witwer B, Badie B. Diffusion tensor eigenvector directional color imaging patterns in the evaluation of cerebral white matter tracts altered by tumor. J Magn Reson Imaging. 2004;20:555–62.

    Article  Google Scholar 

  53. Price SJ, Burnet NG, Donovan T, Green HA, Peña A, Antoun NM, et al. Diffusion tensor imaging of brain tumors at 3T: a potential tool for assessing white matter tract invasion? Clin Radiol. 2003;58:455–62.

    Article  CAS  Google Scholar 

  54. Romano A, Fasoli F, Ferrante M. Fiber density index, fractional anisotropy, ADC and clinical motor findings in the white matter of patients with glioblastoma. Eur Radiol. 2008;18:331–6.

    Article  Google Scholar 

  55. Dong Q, Welsh RC, Chenevert TL, Carlos RC, Maly-Sundgren P, Gomez-Hassan DM, et al. Clinical application of diffusion tensor imaging. J Magn Reson Imaging. 2004;19:6–18.

    Article  Google Scholar 

  56. Arfanakis K, Gui M, Lazar M. Optimization of white matter tractography for pre-surgical planning and image-guided surgery. Oncol Rep. 2006;15:1061–4.

    PubMed  Google Scholar 

  57. Romano A, Ferrante M, Cipriani V. Role of magnetic resonance tractography in the preoperative planning and intraoperative assessment of patients with intra-axial brain tumours. Radiol Med. 2007;112:906–20.

    Article  CAS  Google Scholar 

  58. Bello L, Gambini A, Castellano A, Carrabba G, Acerbi F, Fava E, et al. Motor and language DTI Fiber tracking combined with intraoperative subcortical mapping for surgical removal of gliomas. NeuroImage. 2008;39:369–82.

    Article  Google Scholar 

  59. Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol. 2003;24:1989–98.

    PubMed  Google Scholar 

  60. Aronen HJ, Pardo FS, Kennedy DN, Belliveau JW, Packard SD, Hsu DW, et al. High microvascular blood volume is associated with high glucose uptake and tumor angiogenesis in human gliomas. Clin Cancer Res. 2000;6:2189–200.

    CAS  PubMed  Google Scholar 

  61. Knopp EA, Cha S, Johnson G, Mazumdar A, Golfinos JG, Zagzag D, et al. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology. 1999;211:791–8.

    Article  CAS  Google Scholar 

  62. Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GR 4th, et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas. AJNR Am J Neuroradiol. 2004;25:214–21.

    PubMed  Google Scholar 

  63. Spampinato MV, Smith JK, Kwock L, Ewend M, Grimme JD, Camacho DL, et al. Cerebral blood volume measurements and proton MR spectroscopy in grading of oligodendroglial tumors. AJR Am J Roentgenol. 2007;188:204–12.

    Article  Google Scholar 

  64. Danchaivijitr N, Waldman AD, Tozer DJ, Benton CE, Brasil Caseiras G, et al. Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion- weighted MR imaging predict malignant transformation. Radiology. 2008;247:170–8.

    Article  Google Scholar 

  65. Cha S, Lupo JM, Chen MH, Lamborn KR, McDermott MW, Berger MS, et al. Differentiation of glioblastoma multiforme and single brain metastasis by peak height and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. AJNR Am J Neuroradiol. 2007;28:1078–84.

    Article  CAS  Google Scholar 

  66. Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D. Noninvasive assessment of IDH mutational status in World Health Organization grade II and III astrocytomas using DWI and DSC-PWI combined with conventional MR imaging. AJNR Am J Neuroradiol. 2017;38:1138–44.

    Article  CAS  Google Scholar 

  67. Kickingereder P, Sahm F, Radbruch A, Wick W, Heiland S, Deimling AV, et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep. 2015;5:16238.

    Article  CAS  Google Scholar 

  68. Ryoo I, Choi SH, Kim JH, Sohn CH, Kim SC, Shin HS, et al. Cerebral blood volume calculated by dynamic susceptibility contrast-enhanced perfusion MR imaging: preliminary correlation study with glioblastoma genetic profiles. PLoS One. 2013;19:8.

    Google Scholar 

  69. Chahal M, Xu Y, Lesniak D, Graham K, Famulski K, Christensen JG, et al. MGMT modulates glioblastoma angiogenesis and response to the tyrosine kinase inhibitor sunitinib. Neuro-Oncology. 2010;12:822–33.

    Article  CAS  Google Scholar 

  70. Bernarding J, Braun J, Koennecke HC. Diffusion and perfusion-weighted MR imaging in a patient with acute demyelinating encephalomyelitis (ADEM). J Magn Reson Imaging. 2002;15:96–100.

    Article  Google Scholar 

  71. Leach MO, Brindle KM, Evelhoch JL, et al. Assessment of anti-angiogenic and anti-vascular therapeutics using magnetic resonance imaging: recommendations for appropriate methodology for clinical trials. In: Proceedings of the Eleventh Meeting of the International Society for Magnetic Resonance in Medicine. Berkeley: International Society for Magnetic Resonance in Medicine; 2003. p. 1268.

    Google Scholar 

  72. Wenz F, Rempp K, Hess T, Debus J, Brix G, Engenhart R, et al. Effect of radiation on blood volume in low-grade astrocytomas and normal brain tissue: quantification with dynamic susceptibility contrast MR imaging. AJR Am J Roentgenol. 1996;166:187–93.

    Article  CAS  Google Scholar 

  73. Duffau H. Lessons from brain mapping in surgery for low-grade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol. 2005;4:476–86.

    Article  Google Scholar 

  74. Sunaert S, Yousry TA. Clinical applications of functional magnetic resonance imaging. Neuroimaging Clin N Am. 2001;11:221–36.

    CAS  PubMed  Google Scholar 

  75. Oritz C, Haughton V. Functional MR imaging: paradigms for clinical preoperative mapping. Magn Reson Imaging Clin N Am. 2003;11:529–42.

    Article  Google Scholar 

  76. Bizzi A, Blasi V, Falini A, Ferroli P, Cadioli M, Danesi U, et al. Presurgical functional MR imaging of language and motor functions: validation with intraoperative electrocortical mapping. Radiology. 2008;248:579–89.

    Article  Google Scholar 

  77. Smits M, Visch-Brink E, Schraa-Tam CK, Koudstaal PJ, van der Lugt A. Functional MR imaging of language processing: an overview of easy-to implement paradigms for patient care and clinical research. Radiographics. 2006;26(Suppl 1):S145–58.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Radiation Oncology Unit, University of Pittsburgh Medical Center, Hillman Cancer Center, San Pietro Hospital, Rome, Italy

    Giuseppe Minniti & Claudia Scaringi

  2. IRCCS Neuromed, Pozzilli, IS, Italy

    Giuseppe Minniti

  3. Department of Neuroradiology, Sant’ Andrea Hospital, University Sapienza, Rome, Italy

    Andrea Romano & Alessandro Bozzao

  4. Radiation Oncology Unit, Sant’ Andrea Hospital, University Sapienza, Rome, Italy

    Claudia Scaringi

Authors
  1. Giuseppe Minniti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Romano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudia Scaringi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alessandro Bozzao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuseppe Minniti .

Editor information

Editors and Affiliations

  1. Department of Rehabilitation, Neurorehabilitation Unit, Habilita S.p.A., Zingonia/Ciserano, Bergamo, Italy

    Michelangelo Bartolo

  2. University and City of Health and Science, Department Neuro-Oncology, Turin, Torino, Italy

    Riccardo Soffietti

  3. Department of Medical Psychology, VU University Medical Center Department of Medical Psychology, Amsterdam, The Netherlands

    Martin Klein

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Minniti, G., Romano, A., Scaringi, C., Bozzao, A. (2019). Imaging in Neuro-Oncology. In: Bartolo, M., Soffietti, R., Klein, M. (eds) Neurorehabilitation in Neuro-Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-95684-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95684-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95683-1

  • Online ISBN: 978-3-319-95684-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation