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Surveillance magnetic resonance imaging for isolated optic pathway gliomas: is gadolinium necessary?

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Abstract

Background

Pediatric optic pathway gliomas are typically indolent but have a variable clinical course. Treatment is dictated by symptoms and changes on contrast-enhanced MRI examinations. Gadolinium retention in children has motivated parsimonious use of gadolinium-based contrast agents.

Objectives

To determine surveillance MR factors that motivate changes in tumor-directed therapies and extrapolate cost-efficacy of a non-contrast follow-up protocol.

Materials and methods

Using an imaging database search we identified children with isolated optic pathway gliomas and ≥3 follow-up contrast-enhanced MRIs. We reviewed medical records and imaging for: (1) coincident changes on contrast-enhanced MRI and tumor-directed therapy, (2) demographics and duration of follow-up, (3) motivations for intervention, (4) assessment of gadolinium-based contrast agents’ utility and (5) health care utilization data. We assessed cost impact in terms of relative value unit (RVU) burden.

Results

We included 17 neurofibromatosis type 1 (NF1) and 21 non-NF1 patients who underwent a median 16.9 and 24.3 cumulative contrast-enhanced MR exams over 7.7 years and 8.1 years of follow-up, respectively. Eight children (one with NF1) had intervention based on contrast-enhanced MR findings alone. For these eight, increased tumor size was the only common feature, and it was apparent on non-contrast T2 sequences. For the median patient, a non-contrast follow-up protocol could result in 15.9 (NF1) and 23.3 (non-NF1) fewer gadolinium-based contrast agent administrations, and a 39% lower yearly RVU burden.

Conclusion

Pediatric patients with isolated optic pathway gliomas undergo a large number of routine contrast-enhanced MR follow-up exams. Gadolinium might not be needed for these exams to inform management decisions. Secondary benefits of a non-contrast follow-up protocol include decreased cost and risk to the patient.

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References

  1. Gale EM, Caravan P, Rao AG et al (2017) Gadolinium-based contrast agents in pediatric magnetic resonance imaging. Pediatr Radiol 47:507–521

    Article  PubMed  Google Scholar 

  2. McDonald JS, McDonald RJ, Jentoft ME et al (2017) Intracranial gadolinium deposition following gadodiamide-enhanced magnetic resonance imaging in pediatric patients: a case-control study. JAMA Pediatr 171:705–707

    Article  PubMed  Google Scholar 

  3. Kanda T, Fukusato T, Matsuda M et al (2015) Gadolinium-based contrast agent accumulates in the brain even in subjects without severe renal dysfunction: evaluation of autopsy brain specimens with inductively coupled plasma mass spectroscopy. Radiology 276:228–232

    Article  PubMed  Google Scholar 

  4. Miller JH, Hu HH, Pokorney A et al (2015) MRI brain signal intensity changes of a child during the course of 35 gadolinium contrast examinations. Pediatrics 136:e1637–e1640

    Article  PubMed  Google Scholar 

  5. Mithal LB, Patel PS, Mithal D et al (2017) Use of gadolinium-based magnetic resonance imaging contrast agents and awareness of brain gadolinium deposition among pediatric providers in North America. Pediatr Radiol 47:657–664

    Article  PubMed  Google Scholar 

  6. Roberts DR, Chatterjee AR, Yazdani M et al (2016) Pediatric patients demonstrate progressive T1-weighted hyperintensity in the dentate nucleus following multiple doses of gadolinium-based contrast agent. AJNR Am J Neuroradiol 37:2340–2347

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Murata N, Gonzalez-Cuyar LF, Murata K et al (2016) Macrocyclic and other non-group 1 gadolinium contrast agents deposit low levels of gadolinium in brain and bone tissue: preliminary results from 9 patients with normal renal function. Investig Radiol 51:447–453

    Article  CAS  Google Scholar 

  8. Roberts DR, Lindhorst SM, Welsh CT et al (2016) High levels of gadolinium deposition in the skin of a patient with normal renal function. Investig Radiol 51:280–289

    CAS  Google Scholar 

  9. Maximova N, Gregori M, Zennaro F et al (2016) Hepatic gadolinium deposition and reversibility after contrast agent-enhanced MR imaging of pediatric hematopoietic stem cell transplant recipients. Radiology 281:418–426

    Article  PubMed  Google Scholar 

  10. Rogosnitzky M, Branch S (2016) Gadolinium-based contrast agent toxicity: a review of known and proposed mechanisms. Biometals 29:365–376

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Semelka RC, Ramalho M, AlObaidy M, Ramalho J (2016) Gadolinium in humans: a family of disorders. AJR Am J Roentgenol 207:229–233

    Article  PubMed  Google Scholar 

  12. U. S. Food and Drug Administration (2017) Drug safety communications. FDA evaluating the risk of brain deposits with repeated use of gadolinium-based contrast agents for magnetic resonance imaging. https://www.fda.gov/downloads/Drugs/DrugSafety/UCM455390.pdf. Accessed 17 April 2017

  13. European Medicines Agency (2017) PRAC concludes assessment of gadolinium agents used in body scans and recommends regulatory actions, including suspension for some marketing authorisations. http://www.ema.europa.eu/docs/en_GB/document_library/Press_release/2017/03/WC500223209.pdf. Accessed 17 April 2017

  14. American College of Radiology (2017) ACR manual on contrast media version 10.3 (78). https://www.acr.org/-/media/ACR/Files/Clinical-Resources/Contrast_Media.pdf. Accessed 12 April 2018

  15. American College of Radiology (2017) News releases: ACR response to the European PRAC recommendations (April 4, 2017). http://www.publicnow.com/view/DA714236788989CE92F38E18EA3CCB6FCC9DFDE3?2017-04-04-17:31:30+01:00-xxx935. Accessed 12 April 2018

  16. Goischke HK (2017) Safety assessment of gadolinium-based contrast agents (GBCAs) requires consideration of long-term adverse effects in all human tissues. Mult Scler J Exp Transl Clin 3:2055217317704450

    PubMed  PubMed Central  Google Scholar 

  17. Gupta A, Al-Dasuqi K, Xia F et al (2017) The use of noncontrast quantitative MRI to detect gadolinium-enhancing multiple sclerosis brain lesions: a systematic review and meta-analysis. AJNR Am J Neuroradiol 38:1317–1322

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Heideman RL (1993) Tumors of the central nervous system. In: Pizzo PA, Poplack DG (eds) Principles and practice of pediatric oncology. Lippincott-Raven, Philadelphia, pp 633–681

    Google Scholar 

  19. Ostrom QT, Gittleman H, Fulop J et al (2015) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro Oncol 17:iv1–iv62

    Article  PubMed  PubMed Central  Google Scholar 

  20. Albers AC, Gutmann DH (2009) Gliomas in patients with neurofibromatosis type 1. Expert Rev Neurother 9:535–539

    Article  PubMed  Google Scholar 

  21. Recht LD (2017) Optic pathway glioma. In: UpToDate. https://www.uptodate.com/contents/optic-pathway-glioma. Accessed 12 April 2018

  22. Wan MJ, Ullrich NJ, Manley PE et al (2016) Long-term visual outcomes of optic pathway gliomas in pediatric patients without neurofibromatosis type 1. J Neuro Oncol 129:173–178

    Article  Google Scholar 

  23. Gaudino S, Martucci M, Russo R et al (2017) MR imaging of brain pilocytic astrocytoma: beyond the stereotype of benign astrocytoma. Childs Nerv Syst 33:35–54

    Article  PubMed  Google Scholar 

  24. Hernaiz Driever P, von Hornstein S, Pietsch T et al (2010) Natural history and management of low-grade glioma in NF-1 children. J Neuro Oncol 100:199–207

    Article  Google Scholar 

  25. Kornreich L, Blaser S, Schwarz M et al (2001) Optic pathway glioma: correlation of imaging findings with the presence of neurofibromatosis. AJNR Am J Neuroradiol 22:1963–1969

    PubMed  CAS  Google Scholar 

  26. Kerrison JB (2005) Chapter 38: phacomatoses. In: Miller NR (ed) Walsh & Hoyt's clinical neuro-ophthalmology, 6th edn. Lippincott Williams & Wilkins, Philadelphia, pp 1823–1898

    Google Scholar 

  27. Chateil JF, Soussotte C, Pedespan JM et al (2001) MRI and clinical differences between optic pathway tumours in children with and without neurofibromatosis. Br J Radiol 74:24–31

    Article  PubMed  CAS  Google Scholar 

  28. Prada CE, Hufnagel RB, Hummel TR et al (2015) The use of magnetic resonance imaging screening for optic pathway gliomas in children with neurofibromatosis type 1. J Pediatr 167:851–856

  29. Listernick R, Ferner RE, Liu GT, Gutmann DH (2007) Optic pathway gliomas in neurofibromatosis-1: controversies and recommendations. Ann Neurol 61:189–198

    Article  PubMed  CAS  Google Scholar 

  30. Balcer LJ, Liu GT, Heller G et al (2001) Visual loss in children with neurofibromatosis type 1 and optic pathway gliomas. Relation to tumor location by magnetic resonance imaging. Am J Ophthalmol 131:442–445

    Article  PubMed  CAS  Google Scholar 

  31. Listernick R, Louis DN, Packer RJ, Gutmann DH (1997) Optic pathway gliomas in children with neurofibromatosis 1: consensus statement from the NF1 optic pathway glioma task force. Neuro Oncol 41:143–149

    CAS  Google Scholar 

  32. Liu GT (2006) Optic gliomas of the anterior visual pathway. Curr Opin Ophthalmol 17:427–431

    Article  PubMed  CAS  Google Scholar 

  33. Thiagalingam S, Flaherty M, Billson F, North K (2004) Neurofibromatosis type 1 and optic pathway gliomas: follow-up of 54 patients. Ophthalmology 111:568–577

    Article  PubMed  Google Scholar 

  34. Fisher MJ, Loguidice M, Gutmann DH et al (2012) Visual outcomes in children with neurofibromatosis type 1-associated optic pathway glioma following chemotherapy: a multicenter retrospective analysis. Neuro-Oncology 14:790–797

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ater JL, Zhou T, Holmes E et al (2012) Randomized study of two chemotherapy regimens for treatment of low-grade glioma in young children: a report from the Children's oncology group. J Clin Oncol 30:2641–2647

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kelly JP, Leary S, Khanna P, Weiss AH (2012) Longitudinal measures of visual function, tumor volume, and prediction of visual outcomes after treatment of optic pathway gliomas. Ophthalmology 119:1231–1237

    Article  PubMed  Google Scholar 

  37. Shofty B, Mauda-Havakuk M, Weizman L et al (2015) The effect of chemotherapy on optic pathway gliomas and their sub-components: a volumetric MR analysis study. Pediatr Blood Cancer 62:1353–1359

    Article  PubMed  CAS  Google Scholar 

  38. van den Bent MJ, Wefel JS, Schiff D et al (2011) Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol 12:583–593

    Article  PubMed  Google Scholar 

  39. Wen PY, Chang SM, Van den Bent MJ et al (2017) Response assessment in neuro-oncology clinical trials. J Clin Oncol 35:2439–2449

    Article  PubMed  PubMed Central  Google Scholar 

  40. R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org. Accessed 14 March 2018

  41. Centers for Medicare and Medicaid Services (2017) Searchable Medicare physician fee schedule. http://www.cms.gov/apps/physician-fee-schedule/overview.aspx. Accessed 12 Sept 2017

  42. Clemens J, Gottlieb JD (2017) In the shadow of a giant: Medicare's influence on private physician payments. J Polit Econ 125:1–39

    Article  PubMed  Google Scholar 

  43. The Henry J. Kaiser Family Foundation (2016) State health facts — Medicaid-to-Medicare fee index (2016). http://www.kff.org/state-category/medicaid-chip/medicaid-physician-fees/. Accessed 15 Sept 2017

  44. Taylor T, Jaspan T, Milano G et al (2008) Radiological classification of optic pathway gliomas: experience of a modified functional classification system. Br J Radiol 81:761–766

    Article  PubMed  CAS  Google Scholar 

  45. Dillman JR, Ellis JH, Cohan RH et al (2007) Frequency and severity of acute allergic-like reactions to gadolinium-containing i.v. contrast media in children and adults. AJR Am J Roentgenol 189:1533–1538

    Article  PubMed  Google Scholar 

  46. Astrup J (2003) Natural history and clinical management of optic pathway glioma. Br J Neurosurg 17:327–335

    Article  PubMed  CAS  Google Scholar 

  47. Dodge HW Jr, Love JG, Craig WM et al (1958) Gliomas of the optic nerves. AMA Arch Neurol Psychiatry 79:607–621

  48. Wen PY, Macdonald DR, Reardon DA et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972

    Article  PubMed  Google Scholar 

  49. Pollack IF, Hoffman HJ, Humphreys RP, Becker L (1993) The long-term outcome after surgical treatment of dorsally exophytic brain-stem gliomas. J Neurosurg 78:859–863

    Article  PubMed  CAS  Google Scholar 

  50. Lee AG (2007) Neuroophthalmological management of optic pathway gliomas. Neurosurg Focus 23:E1

    Article  PubMed  Google Scholar 

  51. Lambron J, Rakotonjanahary J, Loisel D et al (2016) Can we improve accuracy and reliability of MRI interpretation in children with optic pathway glioma? Proposal for a reproducible imaging classification. Neuroradiology 58:197–208

    Article  PubMed  Google Scholar 

  52. Weizman L, Sira LB, Joskowicz L et al (2014) Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. Med Phys 41:052303

    Article  PubMed  PubMed Central  Google Scholar 

  53. Weizman L, Ben Sira L, Joskowicz L et al (2012) Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI. Med Image Anal 16:177–188

    Article  PubMed  CAS  Google Scholar 

  54. Shofty B, Weizman L, Joskowicz L et al (2011) MRI internal segmentation of optic pathway gliomas: clinical implementation of a novel algorithm. Childs Nerv Syst 27:1265–1272

    Article  PubMed  Google Scholar 

  55. Gaudino S, Quaglio F, Schiarelli C et al (2012) Spontaneous modifications of contrast enhancement in childhood non-cerebellar pilocytic astrocytomas. Neuroradiology 54:989–995

    Article  PubMed  Google Scholar 

  56. Beni-Adani L, Gomori M, Spektor S, Constantini S (2000) Cyst wall enhancement in pilocytic astrocytoma: neoplastic or reactive phenomena. Pediatr Neurosurg 32:234–239

    Article  PubMed  CAS  Google Scholar 

  57. Takeuchi H, Kubota T, Sato K, Arishima H (2004) Ultrastructure of capillary endothelium in pilocytic astrocytomas. Brain Tumor Pathol 21:23–26

    Article  PubMed  Google Scholar 

  58. Strong JA, Hatten HP Jr, Brown MT et al (1993) Pilocytic astrocytoma: correlation between the initial imaging features and clinical aggressiveness. AJR Am J Roentgenol 161:369–372

  59. Jittapiromsak N, Hou P, Liu HL et al (2017) Prognostic role of conventional and dynamic contrast-enhanced MRI in optic pathway gliomas. J Neuroimaging 27:594–601

    Article  PubMed  Google Scholar 

  60. Jost SC, Ackerman JW, Garbow JR et al (2008) Diffusion-weighted and dynamic contrast-enhanced imaging as markers of clinical behavior in children with optic pathway glioma. Pediatr Radiol 38:1293–1299

    Article  PubMed  Google Scholar 

  61. Singhal S, Birch JM, Kerr B et al (2002) Neurofibromatosis type 1 and sporadic optic gliomas. Arch Dis Child 87:65–70

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Campian J, Gutmann DH (2017) CNS tumors in neurofibromatosis. J Clin Oncol 35:2378–2385

    Article  PubMed  PubMed Central  Google Scholar 

  63. Guillamo JS, Creange A, Kalifa C et al (2003) Prognostic factors of CNS tumours in neurofibromatosis 1 (NF1): a retrospective study of 104 patients. Brain 126:152–160

    Article  PubMed  Google Scholar 

  64. Mentzel HJ, Seidel J, Fitzek C et al (2005) Pediatric brain MRI in neurofibromatosis type I. Eur Radiol 15:814–822

    Article  PubMed  Google Scholar 

  65. Bonawitz C, Castillo M, Chin CT et al (1998) Usefulness of contrast material in MR of patients with neurofibromatosis type 1. AJNR Am J Neuroradiol 19:541–546

    PubMed  CAS  Google Scholar 

  66. Jost G, Lenhard DC, Sieber MA et al (2016) Signal increase on unenhanced T1-weighted images in the rat brain after repeated, extended doses of gadolinium-based contrast agents: comparison of linear and macrocyclic agents. Investig Radiol 51:83–89

    Article  CAS  Google Scholar 

  67. Sieber MA, Steger-Hartmann T, Lengsfeld P, Pietsch H (2009) Gadolinium-based contrast agents and NSF: evidence from animal experience. J Magn Reson Imaging 30:1268–1276

    Article  PubMed  Google Scholar 

  68. Radbruch A, Haase R, Kickingereder P et al (2017) Pediatric brain: no increased signal intensity in the dentate nucleus on unenhanced T1-weighted MR images after consecutive exposure to a macrocyclic gadolinium-based contrast agent. Radiology 283:828–836

    Article  PubMed  Google Scholar 

  69. Flood TF, Stence NV, Maloney JA, Mirsky DM (2017) Pediatric brain: repeated exposure to linear gadolinium-based contrast material is associated with increased signal intensity at unenhanced T1-weighted MR imaging. Radiology 282:222–228

    Article  PubMed  Google Scholar 

  70. Abraham JL, Thakral C (2008) Tissue distribution and kinetics of gadolinium and nephrogenic systemic fibrosis. Eur J Radiol 66:200–207

    Article  PubMed  Google Scholar 

  71. Costello JR, Kalb B, Martin DR (2016) Incidence and risk factors for gadolinium-based contrast agent immediate reactions. Top Magn Reson Imaging 25:257–263

    Article  PubMed  Google Scholar 

  72. Jung JW, Kang HR, Kim MH et al (2012) Immediate hypersensitivity reaction to gadolinium-based MR contrast media. Radiology 264:414–422

    Article  PubMed  Google Scholar 

  73. Stecker EC, Schroeder SA (2013) Adding value to relative-value units. N Engl J Med 369:2176–2179

    Article  PubMed  CAS  Google Scholar 

  74. The Henry J. Kaiser Family Foundation (2017) Next steps for CHIP: what is at stake for children? http://files.kff.org/attachment/Fact-Sheet-Next-Steps-for-CHIP-What-is-at-Stake-for-Children. Accessed 14 Sept 2017

  75. Patton DD, Woolfenden JM (1989) A utility-based model for comparing the cost-effectiveness of diagnostic studies. Investig Radiol 24:263–271

    Article  CAS  Google Scholar 

  76. Hollingworth W, Jarvik JG (2007) Technology assessment in radiology: putting the evidence in evidence-based radiology. Radiology 244:31–38

    Article  PubMed  Google Scholar 

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Acknowledgment

Dr. Maloney is supported, in part, through a National Institutes of Health, National Research Service Award training grant (grant number 5 T32 DK0072).

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Correspondence to Dennis W. W. Shaw.

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This article was awarded the John Kirkpatrick Young Investigator Award at the Society for Pediatric Radiology 2017 meeting.

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Maloney, E., Stanescu, A.L., Perez, F.A. et al. Surveillance magnetic resonance imaging for isolated optic pathway gliomas: is gadolinium necessary?. Pediatr Radiol 48, 1472–1484 (2018). https://doi.org/10.1007/s00247-018-4154-4

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