Advertisement

Journal of Neuro-Oncology

, Volume 119, Issue 3, pp 491–502 | Cite as

The evolving role of neurological imaging in neuro-oncology

  • E. J. Fontana
  • T. Benzinger
  • C. Cobbs
  • J. Henson
  • S. J. Fouke
Topic Review

Abstract

Neuroimaging has played a critical role in the management of patients with neurological disease, since the first ventriculogram was performed in 1918 by Walter Dandy (Mezger et al. Langenbecks Arch Surg 398(4):501–514, 2013). Over the last century, technology has evolved significantly, and within the last decade, the role of imaging in the management of patients with neuro-oncologic disease has shifted from a tool for gross identification of intracranial pathology, to an integral part of real-time neurological surgery. Current neurological imaging provides detailed information about anatomical structure, neurological function, and metabolic and metabolism—important characteristics that help clinicians and surgeons non-invasively manage patients with brain tumors. It is valuable to review the evolution of neurological imaging over the past several decades, focusing on its role in the management of patients with intracranial tumors. Novel neuro-imaging tools and developing technology with the potential to further transform clinical practice will be discussed, as will the key role neurological imaging plays in neurosurgical planning and intraoperative navigation. With increasingly complex imaging modalities creating growing amounts of raw data, validation of techniques, data analysis, and integrating various pieces of imaging data into individual patient management plans, remain significant challenges for clinicians. We thus suggest mechanisms that might ultimately allow for evidence based integration of imaging in the management of patients with neuro-oncologic disease.

Keywords

Imaging MRI PET Brain tumor 

Notes

Conflict of interest

None of the authors (specifically Elizabeth Fontana, John Henson, Charles Cobbs, Tammie Benzinger, or Sarah Fouke) have a financial relationship with the organization sponsoring the research/review discussed within this manuscript.

References

  1. 1.
    Mezger U, Jendrewski C, Bartels M (2013) Navigation in surgery. Langenbecks Arch Surg 398(4):501–514PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Kotecha R, Toledo-Pereyra LH (2011) Advanced imaging technology in surgical innovation. J Invest Surg 24(6):243–249PubMedCrossRefGoogle Scholar
  3. 3.
    Hounsfield GN (1980) Computed medical imaging. Nobel lecture, Decemberr 8, 1979. J Comput Assist Tomogr 4(5):665–674PubMedCrossRefGoogle Scholar
  4. 4.
    De Vita E et al (2003) High resolution MRI of the brain at 4.7 Tesla using fast spin echo imaging. Br J Radiol 76(909):631–637PubMedCrossRefGoogle Scholar
  5. 5.
    van de Langenberg R et al (2009) Follow-up assessment of vestibular schwannomas: volume quantification versus two-dimensional measurements. Neuroradiology 51(8):517–524PubMedCrossRefPubMedCentralGoogle Scholar
  6. 6.
    Bemporad JA, Sze G (2001) Magnetic resonance imaging of spinal cord vascular malformations with an emphasis on the cervical spine. Neuroimaging Clin N Am 11(1):111–129Google Scholar
  7. 7.
    Paek SL et al (2013) Early experience of pre- and post-contrast 7.0T MRI in brain tumors. J Korean Med Sci 28(9):1362–1372PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Svolos P et al (2013) Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques. Magn Reson Imaging 31(9):1567–1577PubMedCrossRefGoogle Scholar
  9. 9.
    Law M et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247(2):490–498PubMedCrossRefPubMedCentralGoogle Scholar
  10. 10.
    Al-Okaili RN et al (2006) Advanced MR imaging techniques in the diagnosis of intraaxial brain tumors in adults. Radiographics 26(Suppl 1):S173–S189PubMedCrossRefGoogle Scholar
  11. 11.
    Bulakbasi N et al (2003) Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. AJNR Am J Neuroradiol 24(2):225–233PubMedGoogle Scholar
  12. 12.
    Erdem E et al (2001) Diffusion-weighted imaging and fluid attenuated inversion recovery imaging in the evaluation of primitive neuroectodermal tumors. Neuroradiology 43(11):927–933PubMedCrossRefGoogle Scholar
  13. 13.
    Guo AC et al (2002) Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 224(1):177–183PubMedCrossRefGoogle Scholar
  14. 14.
    O’Donnell LJ, Westin CF (2011) An introduction to diffusion tensor image analysis. Neurosurg Clin N Am 22(2):185–196PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Hein PA et al (2004) Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol 25(2):201–209PubMedGoogle Scholar
  16. 16.
    Provenzale JM, Mukundan S, Barboriak DP (2006) Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology 239(3):632–649PubMedCrossRefGoogle Scholar
  17. 17.
    Rollin N et al (2006) Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology 48(3):150–159PubMedCrossRefGoogle Scholar
  18. 18.
    Tout DA et al (2005) Left ventricular function parameters obtained from gated myocardial perfusion SPECT imaging: a comparison of two data processing systems. Nucl Med Commun 26(2):103–107PubMedCrossRefGoogle Scholar
  19. 19.
    Thomsen H, Steffensen E, Larsson EM (2012) Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas. Acta Radiol 53(1):95–101PubMedCrossRefGoogle Scholar
  20. 20.
    Law M (2009) Advanced imaging techniques in brain tumors. Cancer Imaging 9(Spec No A):S4–S9PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Hu LS et al (2012) Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol 14(7):919–930PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Bulik M et al (2013) Potential of MR spectroscopy for assessment of glioma grading. Clin Neurol Neurosurg 115(2):146–153PubMedCrossRefGoogle Scholar
  23. 23.
    Nelson SJ (2003) Multivoxel magnetic resonance spectroscopy of brain tumors. Mol Cancer Ther 2(5):497–507PubMedGoogle Scholar
  24. 24.
    Fayed N, Modrego PJ, Medrano J (2009) Comparative test-retest reliability of metabolite values assessed with magnetic resonance spectroscopy of the brain. The LCModel versus the manufacturer software. Neurol Res 31(5):472–477PubMedCrossRefGoogle Scholar
  25. 25.
    Server A et al (2011) Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 80(2):462–470PubMedCrossRefGoogle Scholar
  26. 26.
    Fukuda H, Kubota K, Matsuzawa T (2013) Pioneering and fundamental achievements on the development of positron emission tomography (PET) in oncology. Tohoku J Exp Med 230(3):155–169PubMedCrossRefGoogle Scholar
  27. 27.
    Hoekstra OS et al (1993) Early response monitoring in malignant lymphoma using fluorine-18 fluorodeoxyglucose single-photon emission tomography. Eur J Nucl Med 20(12):1214–1217PubMedCrossRefGoogle Scholar
  28. 28.
    Di Chiro G et al (1982) Glucose utilization of cerebral gliomas measured by [18F] fluorodeoxyglucose and positron emission tomography. Neurology 32(12):1323–1329PubMedCrossRefGoogle Scholar
  29. 29.
    Patronas NJ et al (1982) Work in progress: [18F] fluorodeoxyglucose and positron emission tomography in the evaluation of radiation necrosis of the brain. Radiology 144(4):885–889PubMedCrossRefGoogle Scholar
  30. 30.
    Lin NU et al (2013) Challenges relating to solid tumour brain metastases in clinical trials, part 1: patient population, response, and progression. A report from the RANO group. Lancet Oncol 14(10):e396–e406PubMedCrossRefGoogle Scholar
  31. 31.
    Horky LL, Treves ST (2011) PET and SPECT in brain tumors and epilepsy. Neurosurg Clin N Am 22(2):169–184PubMedCrossRefGoogle Scholar
  32. 32.
    Soares DP, Law M (2009) Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin Radiol 64(1):12–21PubMedCrossRefGoogle Scholar
  33. 33.
    Chen W (2007) Clinical applications of PET in brain tumors. J Nucl Med 48(9):1468–1481PubMedCrossRefGoogle Scholar
  34. 34.
    Ricci PE et al (1998) Differentiating recurrent tumor from radiation necrosis: time for re-evaluation of positron emission tomography? AJNR Am J Neuroradiol 19(3):407–413PubMedGoogle Scholar
  35. 35.
    Yamada S et al (1995) High accumulation of fluorine-18-fluorodeoxyglucose in turpentine-induced inflammatory tissue. J Nucl Med 36(7):1301–1306PubMedGoogle Scholar
  36. 36.
    Mankoff DA, Shields AF, Krohn KA (2005) PET imaging of cellular proliferation. Radiol Clin North Am 43(1):153–167PubMedCrossRefGoogle Scholar
  37. 37.
    Gulyas B, Halldin C (2012) New PET radiopharmaceuticals beyond FDG for brain tumor imaging. Q J Nucl Med Mol Imaging 56(2):173–190PubMedGoogle Scholar
  38. 38.
    Heiss WD et al (1996) F-Dopa as an amino acid tracer to detect brain tumors. J Nucl Med 37(7):1180–1182PubMedGoogle Scholar
  39. 39.
    Chen W et al (2006) 18F-FDOPA PET imaging of brain tumors: comparison study with 18F-FDG PET and evaluation of diagnostic accuracy. J Nucl Med 47(6):904–911PubMedGoogle Scholar
  40. 40.
    Becherer A et al (2003) Brain tumour imaging with PET: a comparison between [18F]fluorodopa and [11C]methionine. Eur J Nucl Med Mol Imaging 30(11):1561–1567PubMedCrossRefGoogle Scholar
  41. 41.
    Hoegerle S et al (2003) 18F-DOPA positron emission tomography for the detection of glomus tumours. Eur J Nucl Med Mol Imaging 30(5):689–694PubMedCrossRefGoogle Scholar
  42. 42.
    Beuthien-Baumann B et al (2003) 3-O-methyl-6-[18F]fluoro-L-DOPA and its evaluation in brain tumour imaging. Eur J Nucl Med Mol Imaging 30(7):1004–1008PubMedCrossRefGoogle Scholar
  43. 43.
    Rapp M et al (2013) Diagnostic performance of 18F-FET PET in newly diagnosed cerebral lesions suggestive of glioma. J Nucl Med 54(2):229–235PubMedCrossRefGoogle Scholar
  44. 44.
    Popperl G et al (2004) Value of O-(2-[18F]fluoroethyl)-L-tyrosine PET for the diagnosis of recurrent glioma. Eur J Nucl Med Mol Imaging 31(11):1464–1470PubMedCrossRefGoogle Scholar
  45. 45.
    Couldwell WT, Apuzzo ML (1990) Initial experience related to the use of the Cosman-Roberts-Wells stereotactic instrument. Technical note. J Neurosurg 72(1):145–148PubMedCrossRefGoogle Scholar
  46. 46.
    Smith KR, Frank KJ, Bucholz RD (1994) The NeuroStation—a highly accurate, minimally invasive solution to frameless stereotactic neurosurgery. Comput Med Imaging Graph 18(4):247–256PubMedCrossRefGoogle Scholar
  47. 47.
    Owen CM, Linskey ME (2009) Frame-based stereotaxy in a frameless era: current capabilities, relative role, and the positive- and negative predictive values of blood through the needle. J Neurooncol 93(1):139–149PubMedCrossRefGoogle Scholar
  48. 48.
    Fouke SJ et al (2014) The comprehensive neuro-oncology data repository (CONDR): a research infrastructure to develop and validate imaging biomarkers. Neurosurgery 74(1):88–98PubMedCrossRefGoogle Scholar
  49. 49.
    Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27(4):859–867PubMedGoogle Scholar
  50. 50.
    Perlmutter JS et al (1987) Regional asymmetries of cerebral blood flow, blood volume, and oxygen utilization and extraction in normal subjects. J Cereb Blood Flow Metab 7(1):64–67PubMedCrossRefGoogle Scholar
  51. 51.
    Raichle ME (1998) Behind the scenes of functional brain imaging: a historical and physiological perspective. Proc Natl Acad Sci U S A 95(3):765–772PubMedCrossRefPubMedCentralGoogle Scholar
  52. 52.
    Shulman GL et al (1997) Common Blood Flow Changes across Visual Tasks: I. Increases in Subcortical Structures and Cerebellum but Not in Nonvisual Cortex. J Cogn Neurosci 9(5):624–647PubMedCrossRefGoogle Scholar
  53. 53.
    Bandettini PA (2012) Twenty years of functional MRI: the science and the stories. Neuroimage 62(2):575–588PubMedCrossRefGoogle Scholar
  54. 54.
    Biswal B et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541PubMedCrossRefGoogle Scholar
  55. 55.
    Le Bihan D (2012) Diffusion, confusion and functional MRI. Neuroimage 62(2):1131–1136PubMedCrossRefGoogle Scholar
  56. 56.
    Yetkin FZ et al (1997) Functional MR activation correlated with intraoperative cortical mapping. AJNR Am J Neuroradiol 18(7):1311–1315PubMedGoogle Scholar
  57. 57.
    Hirsch J et al (2000) An integrated functional magnetic resonance imaging procedure for preoperative mapping of cortical areas associated with tactile, motor, language, and visual functions. Neurosurgery 47(3): 711–721; discussion 721–2Google Scholar
  58. 58.
    Schulder M et al (1998) Functional image-guided surgery of intracranial tumors located in or near the sensorimotor cortex. J Neurosurg 89(3):412–418PubMedCrossRefGoogle Scholar
  59. 59.
    Bello L et al (2008) Motor and language DTI fiber tracking combined with intraoperative subcortical mapping for surgical removal of gliomas. Neuroimage 39(1):369–382PubMedCrossRefGoogle Scholar
  60. 60.
    Wu JS et al (2007) Clinical evaluation and follow-up outcome of diffusion tensor imaging-based functional neuronavigation: a prospective, controlled study in patients with gliomas involving pyramidal tracts. Neurosurgery 61(5): 935–948; discussion 948–9Google Scholar
  61. 61.
    Kuhnt D, Bauer MH, Nimsky C (2012) Brain shift compensation and neurosurgical image fusion using intraoperative MRI: current status and future challenges. Crit Rev Biomed Eng 40(3):175–185PubMedCrossRefGoogle Scholar
  62. 62.
    Moiyadi AV et al (2013) Usefulness of three-dimensional navigable intraoperative ultrasound in resection of brain tumors with a special emphasis on malignant gliomas. Acta Neurochir 155(12):2217–2225PubMedCrossRefGoogle Scholar
  63. 63.
    Renovanz M et al (2014) Navigated versus non-navigated intraoperative ultrasound: is there any impact on the extent of resection of high-grade gliomas? A retrospective clinical analysis. J Neurol Surg A Cent Eur NeurosurgGoogle Scholar
  64. 64.
    Kremer P et al (2006) Intraoperative MRI for interventional neurosurgical procedures and tumor resection control in children. Childs Nerv Syst 22(7):674–678PubMedCrossRefGoogle Scholar
  65. 65.
    Senft C et al (2010) Low field intraoperative MRI-guided surgery of gliomas: a single center experience. Clin Neurol Neurosurg 112(3):237–243PubMedCrossRefGoogle Scholar
  66. 66.
    Fahlbusch R et al (2001) Intraoperative magnetic resonance imaging during transsphenoidal surgery. J Neurosurg 95(3):381–390PubMedCrossRefGoogle Scholar
  67. 67.
    Mohammadi AM et al (2013) Use of high-field intra-operative magnetic resonance imaging to enhance the extent of resection of enhancing and non-enhancing gliomas. NeurosurgeryGoogle Scholar
  68. 68.
    Macdonald DR et al (1990) Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 8(7):1277–1280PubMedGoogle Scholar
  69. 69.
    Vogelbaum MA et al (2013) Application of novel response/progression measures for surgically delivered therapies for gliomas: response assessment in neuro-oncology (RANO) working group. Neurosurgery 70(1): 234–243; discussion 243–4Google Scholar
  70. 70.
    Wen PY et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28(11):1963–1972PubMedCrossRefGoogle Scholar
  71. 71.
    Eisenhauer EA et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247PubMedCrossRefGoogle Scholar
  72. 72.
    Lin NU et al (2009) Multicenter phase II study of lapatinib in patients with brain metastases from HER2-positive breast cancer. Clin Cancer Res 15(4):1452–1459PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • E. J. Fontana
    • 1
  • T. Benzinger
    • 2
  • C. Cobbs
    • 1
  • J. Henson
    • 1
  • S. J. Fouke
    • 1
  1. 1.Swedish Neuroscience InstituteSeattleUSA
  2. 2.Washington University School of Medicine in St. Louis, Missouri, Mallinckrodt Institute of RadiologySt. LouisUSA

Personalised recommendations