Advertisement

Neuro-oncology: Assessing Response in Paediatric Brain Tumours

  • Felice D’ArcoEmail author
  • Kshitij Mankad
  • Marvin Nelson
  • Benita Tamrazi
Chapter
Part of the Pediatric Oncology book series (PEDIATRICO)

Abstract

Response assessment in pediatric neuro-oncology presents unique challenges compared to adult practice. In fact the majority of pediatric tumors are of low grade and can have rather indolent courses with both contrast enhancing and nonenhancing components, often not easily measurable, compared to the adult population where most of the tumors are high-grade gliomas.

In addition, there are profound differences in pediatric tumors when compared to their adult counterparts relating to location, histology, molecular biology and imaging characteristics. This has been highlighted in the 2016 World Health Organization classification of central nervous system tumors which identified specific molecular characteristics of pediatric tumours with different biological behaviours and thus potential differences in terms of response assessment.

There is currently no consensus-based agreement on standards to define response or progression for pediatric brain tumors in trials and, consequently, in clinical practice. Most pediatric neuro-oncology trials are still based on the methods extrapolated from adult experience. Hence, there is definite scope for further development in this field.

This chapter endeavours to address these very specific issues; it reviews currently existing criteria and recommendations and discusses the feasibility and limitations of the multiparametric approach that should be adopted to assessing tumor response to therapy in children.

Keywords

Pediatric neuro-oncology Response assessment Pseudoprogression Pseudoresponse Volumetrics Diffusion Perfusion Spectroscopy 

Notes

Acknowledgements

Miss Jess Cooper, MRI Superintendent Radiographer, Great Ormond Street Hospital, London

References

  1. 1.
    Warren KE, Poussaint TY, Vezina G, Hargrave D, Packer RJ, Goldman S, et al. Challenges with defining response to antitumor agents in pediatric neuro-oncology: a report from the response assessment in pediatric neuro-oncology (RAPNO) working group. Pediatr Blood Cancer. 2013;60(9):1397–401.PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    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(6):803–20.PubMedCrossRefGoogle Scholar
  3. 3.
    Chhabda S, Carney O, D’Arco F, Jacques TS, Mankad K. The 2016 World Health Organization Classification of tumours of the Central Nervous System: what the paediatric neuroradiologist needs to know. Quant Imaging Med Surg. 2016;6(5):486–9.PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963–72.PubMedCrossRefPubMedCentralGoogle Scholar
  5. 5.
    Gaudino S, Quaglio F, Schiarelli C, Martucci M, Tartaglione T, Gualano MR, et al. Spontaneous modifications of contrast enhancement in childhood non-cerebellar pilocytic astrocytomas. Neuroradiology. 2012;54(9):989–95.PubMedCrossRefPubMedCentralGoogle Scholar
  6. 6.
    Gnekow AK, Kortmann RD, Pietsch T, Emser A. Low grade chiasmatic-hypothalamic glioma-carboplatin and vincristine chemotherapy effectively defers radiotherapy within a comprehensive treatment strategy -- report from the multicenter treatment study for children and adolescents with a low grade glioma -- HIT-LGG 1996 -- of the Society of Pediatric Oncology and Hematology (GPOH). Klin Padiatr. 2004;216(6):331–42.PubMedCrossRefPubMedCentralGoogle Scholar
  7. 7.
    Macdonald DR, Cascino TL, Schold SC, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol. 1990;8(7):1277–80.PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Jaspan T, Morgan PS, Warmuth-Metz M, Sanchez Aliaga E, Warren D, Calmon R, et al. Response assessment in pediatric neuro-oncology: implementation and expansion of the RANO criteria in a randomized phase II trial of pediatric patients with newly diagnosed high-grade gliomas. AJNR Am J Neuroradiol. 2016;37(9):1581–7.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Chinot OL, Macdonald DR, Abrey LE, Zahlmann G, Kerloëguen Y, Cloughesy TF. Response assessment criteria for glioblastoma: practical adaptation and implementation in clinical trials of antiangiogenic therapy. Curr Neurol Neurosci Rep. 2013;13(5):347.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Brandsma D, van den Bent MJ. Pseudoprogression and pseudoresponse in the treatment of gliomas. Curr Opin Neurol. 2009;22(6):633–8.PubMedCrossRefGoogle Scholar
  11. 11.
    Barajas RF, Chang JS, Segal MR, Parsa AT, McDermott MW, Berger MS, et al. Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2009;253(2):486–96.PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Avery RA, Mansoor A, Idrees R, Biggs E, Alsharid MA, Packer RJ, et al. Quantitative MRI criteria for optic pathway enlargement in neurofibromatosis type 1. Neurology. 2016;86:2264.PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Avery RA, Ferner RE, Listernick R, Fisher MJ, Gutmann DH, Liu GT. Visual acuity in children with low grade gliomas of the visual pathway: implications for patient care and clinical research. J Neurooncol. 2012;110(1):1–7.PubMedCrossRefGoogle Scholar
  14. 14.
    Ge M, Li S, Wang L, Li C, Zhang J. The role of diffusion tensor tractography in the surgical treatment of pediatric optic chiasmatic gliomas. J Neurooncol. 2015;122:357.PubMedCrossRefGoogle Scholar
  15. 15.
    Kornreich L, Blaser S, Schwarz M, Shuper A, Vishne TH, Cohen IJ, et al. Optic pathway glioma: correlation of imaging findings with the presence of neurofibromatosis. AJNR Am J Neuroradiol. 2001;22(10):1963–9.PubMedPubMedCentralGoogle Scholar
  16. 16.
    Chateil JF, Soussotte C, Pédespan JM, Brun M, Le Manh C, Diard F. MRI and clinical differences between optic pathway tumours in children with and without neurofibromatosis. Br J Radiol. 2001;74(877):24–31.PubMedCrossRefGoogle Scholar
  17. 17.
    Zuccoli G, Ferrozzi F, Sigorini M, Virdis R, Bassi P, Bellomi M. Early spontaneous regression of a hypothalamic/chiasmatic mass in neurofibromatosis type 1: MR findings. Eur Radiol. 2000;10(7):1076–8.PubMedCrossRefGoogle Scholar
  18. 18.
    Dodgshun AJ, Elder JE, Hansford JR, Sullivan MJ. Long-term visual outcome after chemotherapy for optic pathway glioma in children: site and age are strongly predictive. Cancer. 2015;121:4190.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Listernick R, Ferner RE, Liu GT, Gutmann DH. Optic pathway gliomas in neurofibromatosis-1: controversies and recommendations. Ann Neurol. 2007;61(3):189–98.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Wen PY, Chang SM, Van den Bent MJ, Vogelbaum MA, Macdonald DR, Lee EQ. Response assessment in neuro-oncology clinical trials. J Clin Oncol. 2017;35(21):2439–49.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Taylor T, Jaspan T, Milano G, Gregson R, Parker T, Ritzmann T, et al. Radiological classification of optic pathway gliomas: experience of a modified functional classification system. Br J Radiol. 2008;81(970):761–6.PubMedCrossRefGoogle Scholar
  22. 22.
    Warren KE, Vezina G, Poussaint TY, Warmuth-Metz M, Chamberlain MC, Packer RJ, et al. Response assessment in medulloblastoma and leptomeningeal seeding tumors: recommendations from the response assessment in Pediatric Neuro-Oncology Committee. Neuro Oncol. 2018;20:13.PubMedCrossRefGoogle Scholar
  23. 23.
    Packer RJ, Gajjar A, Vezina G, Rorke-Adams L, Burger PC, Robertson PL, et al. Phase III study of craniospinal radiation therapy followed by adjuvant chemotherapy for newly diagnosed average-risk medulloblastoma. J Clin Oncol. 2006;24(25):4202–8.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Perreault S, Ramaswamy V, Achrol AS, Chao K, Liu TT, Shih D, et al. MRI surrogates for molecular subgroups of medulloblastoma. AJNR Am J Neuroradiol. 2014;35(7):1263–9.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    D’Arco F, Khan F, Mankad K et al. Differential diagnosis of posterior fossa tumours in children: new insights. Pediatr Radiol. 2018;48(13):1955–63.Google Scholar
  26. 26.
    Gajjar A, Bowers DC, Karajannis MA, Leary S, Witt H, Gottardo NG. Pediatric brain tumors: innovative genomic information is transforming the diagnostic and clinical landscape. J Clin Oncol. 2015;33(27):2986–98.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Löbel U, Sedlacik J, Reddick WE, Kocak M, Ji Q, Broniscer A, et al. Quantitative diffusion-weighted and dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging analysis of T2 hypointense lesion components in pediatric diffuse intrinsic pontine glioma. AJNR Am J Neuroradiol. 2011;32(2):315–22.PubMedCrossRefGoogle Scholar
  28. 28.
    Warren KE. Diffuse intrinsic pontine glioma: poised for progress. Front Oncol. 2012;2:205.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Burzynski SR, Janicki TJ, Burzynski GS, Marszalek A. The response and survival of children with recurrent diffuse intrinsic pontine glioma based on phase II study of antineoplastons A10 and AS2-1 in patients with brainstem glioma. Childs Nerv Syst. 2014;30(12):2051–61.PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Svolos P, Reddick WE, Edwards A, Sykes A, Li Y, Glass JO, et al. Measurable supratentorial white matter volume changes in patients with diffuse intrinsic pontine glioma treated with an anti-vascular endothelial growth factor agent, steroids, and radiation. AJNR Am J Neuroradiol. 2017;38(6):1235–41.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Löbel U, Hwang S, Edwards A, Li Y, Li X, Broniscer A, et al. Discrepant longitudinal volumetric and metabolic evolution of diffuse intrinsic Pontine gliomas during treatment: implications for current response assessment strategies. Neuroradiology. 2016;58:1027.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Chamberlain M, Soffietti R, Raizer J, Rudà R, Brandsma D, Boogerd W, et al. Leptomeningeal metastasis: a response assessment in neuro-oncology critical review of endpoints and response criteria of published randomized clinical trials. Neuro Oncol. 2014;16(9):1176–85.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Chamberlain M, Junck L, Brandsma D, Soffietti R, Rudà R, Raizer J, et al. Leptomeningeal metastases: a RANO proposal for response criteria. Neuro Oncol. 2017;19(4):484–92.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Warren KE, Patronas N, Aikin AA, Albert PS, Balis FM. Comparison of one-, two-, and three-dimensional measurements of childhood brain tumors. J Natl Cancer Inst. 2001;93(18):1401–5.PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    Kilday J-P, Branson H, Rockel C, Laughlin S, Mabbott D, Bouffet E, et al. Tumor volumetric measurements in surgically inaccessible pediatric low-grade glioma. J Pediatr Hematol Oncol. 2015;37(1):e31–6.PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Rees J, Watt H, Jäger HR, Benton C, Tozer D, Tofts P, et al. Volumes and growth rates of untreated adult low-grade gliomas indicate risk of early malignant transformation. Eur J Radiol. 2009;72(1):54–64.PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Connor SEJ, Gunny R, Hampton T, O’gorman R. Magnetic resonance image registration and subtraction in the assessment of minor changes in low grade glioma volume. Eur Radiol. 2004;14(11):2061–6.PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Dombi E, Ardern-Holmes SL, Babovic-Vuksanovic D, Barker FG, Connor S, Evans DG, et al. Recommendations for imaging tumor response in neurofibromatosis clinical trials. Neurology. 2013;81(21 Suppl 1):S33–40.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Henson JW, Ulmer S, Harris GJ. Brain tumor imaging in clinical trials. AJNR Am J Neuroradiol. 2008;29(3):419–24.PubMedCrossRefPubMedCentralGoogle Scholar
  40. 40.
    D’Arco F, O’Hare P, Dashti F et al. Volumetric assessment of tumor size changes in pediatric low grade gliomas: feasability and comparison with linear measurements. Neuroradiology. 2018;60(4):427–36.Google Scholar
  41. 41.
    Weber M-A, Giesel FL, Stieltjes B. MRI for identification of progression in brain tumors: from morphology to function. Expert Rev Neurother. 2008;8(10):1507–25.PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    Maier SE, Gudbjartsson H, Patz S, Hsu L, Lovblad KO, Edelman RR, et al. Line scan diffusion imaging: characterization in healthy subjects and stroke patients. AJR Am J Roentgenol. 1998;171(1):85–93.PubMedCrossRefGoogle Scholar
  43. 43.
    Helenius J, Soinne L, Perkiö J, Salonen O, Kangasmäki A, Kaste M, et al. Diffusion-weighted MR imaging in normal human brains in various age groups. AJNR Am J Neuroradiol. 2002;23(2):194–9.PubMedGoogle Scholar
  44. 44.
    Steen RG. Edema and tumor perfusion: characterization by quantitative 1H MR imaging. AJR Am J Roentgenol. 1992;158(2):259–64.PubMedCrossRefPubMedCentralGoogle Scholar
  45. 45.
    Maier SE, Sun Y, Mulkern RV. Diffusion imaging of brain tumors. NMR Biomed. 2010;23(7):849–64.PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Lyng H, Haraldseth O, Rofstad EK. Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med. 2000;43(6):828–36.PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Asao C, Korogi Y, Kitajima M, Hirai T, Baba Y, Makino K, et al. Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol. 2005;26(6):1455–60.PubMedPubMedCentralGoogle Scholar
  48. 48.
    Han C, Zhao L, Zhong S, Wu X, Guo J, Zhuang X, et al. A comparison of high b-value vs standard b-value diffusion-weighted magnetic resonance imaging at 3.0 T for medulloblastomas. Br J Radiol. 2015;88(1054):20150220.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Mardor Y, Roth Y, Lidar Z, Jonas T, Pfeffer R, Maier SE, et al. Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. Cancer Res. 2001;61(13):4971–3.PubMedPubMedCentralGoogle Scholar
  50. 50.
    Mardor Y, Pfeffer R, Spiegelmann R, Roth Y, Maier SE, Nissim O, et al. Early detection of response to radiation therapy in patients with brain malignancies using conventional and high b-value diffusion-weighted magnetic resonance imaging. J Clin Oncol. 2003;21(6):1094–100.PubMedCrossRefPubMedCentralGoogle Scholar
  51. 51.
    Sinha S, Bastin ME, Whittle IR, Wardlaw JM. Diffusion tensor MR imaging of high-grade cerebral gliomas. AJNR Am J Neuroradiol. 2002;23(4):520–7.PubMedPubMedCentralGoogle Scholar
  52. 52.
    Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol. 2004;25(3):356–69.PubMedGoogle Scholar
  53. 53.
    van der Heide UA, Houweling AC, Groenendaal G, Beets-Tan RGH, Lambin P. Functional MRI for radiotherapy dose painting. Magn Reson Imaging. 2012;30(9):1216–23.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain. AJR Am J Roentgenol. 2014;202(1):W26–33.PubMedCrossRefGoogle Scholar
  55. 55.
    Van Cauter S, De Keyzer F, Sima DM, Sava AC, D’Arco F, Veraart J, et al. Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro Oncol. 2014;16(7):1010–21.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Goshima S, Kanematsu M, Noda Y, Kondo H, Watanabe H, Bae KT. Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. AJR Am J Roentgenol. 2015;204(5):W543–9.PubMedCrossRefGoogle Scholar
  57. 57.
    Hu F, Tang W, Sun Y, Wan D, Cai S, Zhang Z, et al. The value of diffusion kurtosis imaging in assessing pathological complete response to neoadjuvant chemoradiation therapy in rectal cancer: a comparison with conventional diffusion-weighted imaging. Oncotarget. 2017;8:75597.PubMedPubMedCentralGoogle Scholar
  58. 58.
    Hyare H, Thust S, Rees J. Advanced MRI techniques in the monitoring of treatment of gliomas. Curr Treat Options Neurol. 2017;19(3):11.PubMedCrossRefGoogle Scholar
  59. 59.
    Taal W, Brandsma D, de Bruin HG, Bromberg JE, Swaak-Kragten AT, Smitt PAES, et al. Incidence of early pseudo-progression in a cohort of malignant glioma patients treated with chemoirradiation with temozolomide. Cancer. 2008;113(2):405–10.PubMedCrossRefGoogle Scholar
  60. 60.
    Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9(5):453–61.PubMedCrossRefGoogle Scholar
  61. 61.
    Verma N, Cowperthwaite MC, Burnett MG, Markey MK. Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro Oncol. 2013;15(5):515–34.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Meyzer C, Dhermain F, Ducreux D, Habrand J-L, Varlet P, Sainte-Rose C, et al. A case report of pseudoprogression followed by complete remission after proton-beam irradiation for a low-grade glioma in a teenager: the value of dynamic contrast-enhanced MRI. Radiat Oncol. 2010;5:9.PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Negretti L, Blanchard P, Couanet D, Kieffer V, Goma G, Habrand JL, et al. Pseudoprogression after high-dose busulfan-thiotepa with autologous stem cell transplantation and radiation therapy in children with brain tumors: impact on survival. Neuro Oncol. 2012;14(11):1413–21.PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Chassot A, Canale S, Varlet P, Puget S, Roujeau T, Negretti L, et al. Radiotherapy with concurrent and adjuvant temozolomide in children with newly diagnosed diffuse intrinsic pontine glioma. J Neurooncol. 2012;106(2):399–407.PubMedCrossRefGoogle Scholar
  65. 65.
    Ceschin R, Kurland BF, Abberbock SR, Ellingson BM, Okada H, Jakacki RI, et al. Parametric response mapping of apparent diffusion coefficient as an imaging biomarker to distinguish pseudoprogression from true tumor progression in peptide-based vaccine therapy for pediatric diffuse intrinsic pontine glioma. AJNR Am J Neuroradiol. 2015;36(11):2170–6.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Prager AJ, Martinez N, Beal K, Omuro A, Zhang Z, Young RJ. Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol. 2015;36:877.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Hatzoglou V, Ulaner GA, Zhang Z, Beal K, Holodny AI, Young RJ. Comparison of the effectiveness of MRI perfusion and fluorine-18 FDG PET-CT for differentiating radiation injury from viable brain tumor: a preliminary retrospective analysis with pathologic correlation in all patients. Clin Imaging. 2013;37(3):451–7.PubMedCrossRefGoogle Scholar
  68. 68.
    Shin KE, Ahn KJ, Choi HS, Jung SL, Kim BS, Jeon SS, et al. DCE and DSC MR perfusion imaging in the differentiation of recurrent tumour from treatment-related changes in patients with glioma. Clin Radiol. 2014;69:e264.PubMedCrossRefGoogle Scholar
  69. 69.
    Essig M, Shiroishi MS, Nguyen TB, Saake M, Provenzale JM, Enterline D, et al. Perfusion MRI: the five most frequently asked technical questions. AJR Am J Roentgenol. 2013;200(1):24–34.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Petrella JR, Provenzale JM. MR perfusion imaging of the brain: techniques and applications. AJR Am J Roentgenol. 2000;175(1):207–19.PubMedCrossRefGoogle Scholar
  71. 71.
    Barajas RF, Chang JS, Sneed PK, Segal MR, McDermott MW, Cha S. Distinguishing recurrent intra-axial metastatic tumor from radiation necrosis following gamma knife radiosurgery using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. AJNR Am J Neuroradiol. 2009;30(2):367–72.PubMedCrossRefGoogle Scholar
  72. 72.
    Carceller F, Fowkes LA, Khabra K, Moreno L, Saran F, Burford A, et al. Pseudoprogression in children, adolescents and young adults with non-brainstem high grade glioma and diffuse intrinsic pontine glioma. J Neurooncol. 2016;129(1):109–21.PubMedCrossRefGoogle Scholar
  73. 73.
    Gururangan S, Chi SN, Young Poussaint T, Onar-Thomas A, Gilbertson RJ, Vajapeyam S, et al. Lack of efficacy of bevacizumab plus irinotecan in children with recurrent malignant glioma and diffuse brainstem glioma: a Pediatric Brain Tumor Consortium study. J Clin Oncol. 2010;28(18):3069–75.PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Gururangan S, Fangusaro J, Young Poussaint T, Onar-Thomas A, Gilbertson RJ, Vajapeyam S, et al. Lack of efficacy of bevacizumab + irinotecan in cases of pediatric recurrent ependymoma--a Pediatric Brain Tumor Consortium study. Neuro Oncol. 2012;14(11):1404–12.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Thompson EM, Guillaume DJ, Dósa E, Li X, Nazemi KJ, Gahramanov S, et al. Dual contrast perfusion MRI in a single imaging session for assessment of pediatric brain tumors. J Neurooncol. 2012;109(1):105–14.PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Patel P, Baradaran H, Delgado D, Askin G, Christos P, John Tsiouris A, et al. MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol. 2017;19(1):118–27.PubMedCrossRefGoogle Scholar
  77. 77.
    Yoo R-E, Choi SH. Recent application of advanced MR imaging to predict pseudoprogression in high-grade glioma patients. Magn Reson Med Sci. 2016;15(2):165–77.PubMedCrossRefGoogle Scholar
  78. 78.
    Noguchi T, Yoshiura T, Hiwatashi A, Togao O, Yamashita K, Nagao E, et al. Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. AJNR Am J Neuroradiol. 2008;29(4):688–93.PubMedCrossRefGoogle Scholar
  79. 79.
    Yeom KW, Mitchell LA, Lober RM, Barnes PD, Vogel H, Fisher PG, et al. Arterial spin-labeled perfusion of pediatric brain tumors. AJNR Am J Neuroradiol. 2014;35(2):395–401.PubMedCrossRefGoogle Scholar
  80. 80.
    Choi YJ, Kim HS, Jahng G-H, Kim SJ, Suh DC. Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging. Acta Radiol. 2013;54(4):448–54.PubMedCrossRefGoogle Scholar
  81. 81.
    Guo AC, Cummings TJ, Dash RC, Provenzale JM. Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology. 2002;224(1):177–83.PubMedCrossRefGoogle Scholar
  82. 82.
    Hayashida Y, Hirai T, Morishita S, Kitajima M, Murakami R, Korogi Y, et al. Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. AJNR Am J Neuroradiol. 2006;27(7):1419–25.PubMedGoogle Scholar
  83. 83.
    Herneth AM, Guccione S, Bednarski M. Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol. 2003;45(3):208–13.PubMedCrossRefGoogle Scholar
  84. 84.
    Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol. 2004;25(2):201–9.PubMedGoogle Scholar
  85. 85.
    Matsusue E, Fink JR, Rockhill JK, Ogawa T, Maravilla KR. Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology. 2010;52(4):297–306.PubMedCrossRefGoogle Scholar
  86. 86.
    Zeng Q-S, Li C-F, Liu H, Zhen J-H, Feng D-C. Distinction between recurrent glioma and radiation injury using magnetic resonance spectroscopy in combination with diffusion-weighted imaging. Int J Radiat Oncol Biol Phys. 2007;68(1):151–8.PubMedCrossRefGoogle Scholar
  87. 87.
    Lee WJ, Choi SH, Park C-K, Yi KS, Kim TM, Lee S-H, et al. Diffusion-weighted MR imaging for the differentiation of true progression from pseudoprogression following concomitant radiotherapy with temozolomide in patients with newly diagnosed high-grade gliomas. Acad Radiol. 2012;19(11):1353–61.PubMedCrossRefPubMedCentralGoogle Scholar
  88. 88.
    Cha J, Kim ST, Kim HJ, Kim BJ, Kim YK, Lee JY, et al. Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR Am J Neuroradiol. 2014;35(7):1309–17.PubMedCrossRefGoogle Scholar
  89. 89.
    Park JE, Kim HS, Goh MJ, Kim SJ, Kim JH. Pseudoprogression in patients with glioblastoma: assessment by using volume-weighted voxel-based multiparametric clustering of MR imaging data in an independent test set. Radiology. 2015;275(3):792–802.PubMedCrossRefPubMedCentralGoogle Scholar
  90. 90.
    Caroline I, Rosenthal MA. Imaging modalities in high-grade gliomas: pseudoprogression, recurrence, or necrosis? J Clin Neurosci. 2012;19(5):633–7.PubMedCrossRefGoogle Scholar
  91. 91.
    Terakawa Y, Tsuyuguchi N, Iwai Y, Yamanaka K, Higashiyama S, Takami T, et al. Diagnostic accuracy of 11C-methionine PET for differentiation of recurrent brain tumors from radiation necrosis after radiotherapy. J Nucl Med. 2008;49(5):694–9.PubMedCrossRefPubMedCentralGoogle Scholar
  92. 92.
    Galldiks N, Dunkl V, Stoffels G, Hutterer M, Rapp M, Sabel M, et al. Diagnosis of pseudoprogression in patients with glioblastoma using O-(2-[18F]fluoroethyl)-L-tyrosine PET. Eur J Nucl Med Mol Imaging. 2015;42(5):685–95.Google Scholar
  93. 93.
    Morana G, Piccardo A, Tortora D, Puntoni M, Severino M, Nozza P, et al. Grading and outcome prediction of pediatric diffuse astrocytic tumors with diffusion and arterial spin labeling perfusion MRI in comparison with 18F-DOPA PET. Eur J Nucl Med Mol Imaging. 2017;44:2084.PubMedCrossRefGoogle Scholar
  94. 94.
    Zarifi M, Tzika AA. Proton MRS imaging in pediatric brain tumors. Pediatr Radiol. 2016;46(7):952–62.Google Scholar
  95. 95.
    Martín Noguerol T, Sánchez-González J, Martínez Barbero JP, García-Figueiras R, Baleato-González S, Luna A. Clinical imaging of tumor metabolism with 1h magnetic resonance spectroscopy. Magn Reson Imaging Clin N Am. 2016;24(1):57–86.PubMedCrossRefGoogle Scholar
  96. 96.
    Smith EA, Carlos RC, Junck LR, Tsien CI, Elias A, Sundgren PC. Developing a clinical decision model: MR spectroscopy to differentiate between recurrent tumor and radiation change in patients with new contrast-enhancing lesions. AJR Am J Roentgenol. 2009;192(2):W45–52.PubMedCrossRefGoogle Scholar
  97. 97.
    Elias AE, Carlos RC, Smith EA, Frechtling D, George B, Maly P, et al. MR spectroscopy using normalized and non-normalized metabolite ratios for differentiating recurrent brain tumor from radiation injury. Acad Radiol. 2011;18(9):1101–8.PubMedCrossRefGoogle Scholar
  98. 98.
    Walecki J, Sokól M, Pieniazek P, Maciejewski B, Tarnawski R, Krupska T, et al. Role of short TE 1H-MR spectroscopy in monitoring of post-operation irradiated patients. Eur J Radiol. 1999;30(2):154–61.PubMedCrossRefGoogle Scholar
  99. 99.
    Dhermain FG, Hau P, Lanfermann H, Jacobs AH, van den Bent MJ. Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas. Lancet Neurol. 2010;9(9):906–20.PubMedCrossRefGoogle Scholar
  100. 100.
    Alexander A, Murtha A, Abdulkarim B, Mehta V, Wheatley M, Murray B, et al. Prognostic significance of serial magnetic resonance spectroscopies over the course of radiation therapy for patients with malignant glioma. Clin Invest Med. 2006;29(5):301–11.PubMedGoogle Scholar
  101. 101.
    Quon H, Brunet B, Alexander A, Murtha A, Abdulkarim B, Fulton D, et al. Changes in serial magnetic resonance spectroscopy predict outcome in high-grade glioma during and after postoperative radiotherapy. Anticancer Res. 2011;31(10):3559–65.PubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Felice D’Arco
    • 1
    Email author
  • Kshitij Mankad
    • 1
  • Marvin Nelson
    • 2
  • Benita Tamrazi
    • 2
  1. 1.Department of Radiology, Great Ormond Street Hospital for ChildrenNHS Foundation TrustLondonUK
  2. 2.Department of RadiologyChildren’s Hospital Los AngelesLos AngelesUSA

Personalised recommendations