105
Consenso peruano para el uso de la resonancia magnética en el diagnóstico y seguimiento de pacientes con esclerosis.
Rev Neuropsiquiatr. 2022; 85(2): 95-106
of multiple sclerosis lesions. AJNR Am J Neuroradiol.
1997;18:1279-85.
29. Rovira A, Auger C, Huerga E, et al. Cumulative dose
of macrocyclic gadolinium-based contrast agent
improves detection of enhancing lesions in patients
with multiple sclerosis. AJNR Am J Neuroradiol.
2017;38:1486-93.
30. Pestalozza IF, Pozzilli C, Di Legge S, et al. Monthly
brain magnetic resonance imaging scans in
patients with clinically isolated syndrome. Mult Scler.
2005;11:390-4.
31. Lebrun C, Bensa C, Debouverie M, et al. Association
between clinical conversion to multiple sclerosis in
radiologically isolated syndrome and magnetic
resonance imaging, cerebrospinal uid, and visual
evoked potential: follow-up of 70 patients. Arch
Neurol. 2009;66:841-6.
32. Jacobi C, Hahnel S, Martinez-Torres F, et al.
Prospective combined brain and spinal cord MRI in
clinically isolated syndromes and possible early
multiple sclerosis: impact on dissemination in space
and time. Eur J Neurol. 2008;15:1359-64.
33. Lebrun-Frenay C, Kantarci O, Siva A, et al.
Radiologically Isolated Syndrome: 10-Year Risk
Estimate of a Clinical Event. Ann Neurol.
2020;88:407-17.
34. Okuda DT, Siva A, Kantarci O, et al. Radiologically
isolated syndrome: 5-year risk for an initial clinical
event. PLoS One. 2014;9:e90509.
35. Freedman MS, Selchen D, Arnold DL, et al.
Treatment optimization in MS: Canadian MS
Working Group updated recommendations. Can J
Neurol Sci. 2013; 40:307-23.
36. Rotstein DL, Healy BC, Malik MT, Chitnis T, Weiner
HL. Evaluation of no evidence of disease activity in
a 7-year longitudinal multiple sclerosis cohort. JAMA
neurology. 2015;72:152-8.
37. Ortiz P, Bareno J, Cabrera L, Rueda K, Rovira A.
Magnetic resonance imaging with gadolinium in the
acute phase of relapses in multiple sclerosis. Rev
Neurol. 2017; 64: 241-6.
38. Rio J, Comabella M, Montalban X. Predicting
responders to therapies for multiple sclerosis. Nat
Rev Neurol. 2009;5:553-60.
39. Rio J, Ruiz-Pena JL. Short-term suboptimal response
criteria for predicting long-term non-response to rst-
line disease modifying therapies in multiple sclerosis:
A systematic review and meta-analysis. J Neurol Sci.
2016;361:158-67.
40. Rio J, Tintore M, Sastre-Garriga J, et al. Change
in the clinical activity of multiple sclerosis after
treatment switch for suboptimal response. Eur J
Neurol. 2012;19:899-904.
41. Sormani MP, De Stefano N. Dening and scoring
response to IFN-beta in multiple sclerosis. Nat Rev
Neurol. 2013;9:504-12.
42. Brisset JC, Kremer S, Hannoun S, et al. New OFSEP
recommendations for MRI assessment of multiple
sclerosis patients: Special consideration for
gadolinium deposition and frequent acquisitions. J
Neuroradiol. 2020;47:250-8.
43. Freedman MS, Devonshire V, Duquette P, et al.
Treatment Optimization in Multiple Sclerosis:
Canadian MS Working Group Recommendations.
Can J Neurol Sci. 2020; 47:437-55.
44. Lycklama G, Thompson A, Filippi M, et al. Spinal-
cord MRI in multiple sclerosis. Lancet Neurol.
2003;2: 555-62.
45. Eden D, Gros C, Badji A, et al. Spatial distribution
of multiple sclerosis lesions in the cervical spinal
cord. Brain. 2019;142:633-46.
46. Giovannoni G, Turner B, Gnanapavan S, Oah C,
Schmierer K, Marta M. Is it time to target no evident
disease activity (NEDA) in multiple sclerosis? Mult
Scler Relat Disord. 2015;4:329-33.
47. Battaglini M, Gentile G, Luchetti L, et al. Lifespan
normative data on rates of brain volume changes.
Neurobiol Aging. 2019;81:30-7.
48. De Stefano N, Stromillo ML, Giorgio A, et al.
Establishing pathological cut-os of brain atrophy
rates in multiple sclerosis. J Neurol Neurosurg
Psychiatry. 2016;87:93-9.
49. Di Filippo M, Anderson VM, Altmann DR, et al.
Brain atrophy and lesion load measures over 1 year
relate to clinical status after 6 years in patients with
clinically isolated syndromes. J Neurol Neurosurg
Psychiatry. 2010;81:204-8.
50. Minneboo A, Jasperse B, Barkhof F, et al. Predicting
short-term disability progression in early multiple
sclerosis: added value of MRI parameters. J Neurol
Neurosurg Psychiatry. 2008;79:917-23.
51. Khaleeli Z, Ciccarelli O, Manfredonia F, et al.
Predicting progression in primary progressive
multiple sclerosis: a 10-year multicenter study. Ann
Neurol. 2008;63:790-3.
52. Sormani MP, Kappos L, Radue EW, et al. Dening
brain volume cutos to identify clinically relevant
atrophy in RRMS. Mult Scler. 2017;23:656-64.
53. Zivadinov R, Jakimovski D, Gandhi S, et al. Clinical
relevance of brain atrophy assessment in multiple
sclerosis. Implications for its use in a clinical routine.
Expert Rev Neurother. 2016;16:777-93.
54. Reuter M, Tisdall MD, Qureshi A, Buckner RL, van
der Kouwe AJW, Fischl B. Head motion during MRI
acquisition reduces gray matter volume and thickness
estimates. Neuroimage. 2015;107:107-15.
55. Biberacher V, Schmidt P, Keshavan A, et al. Intra- and
interscanner variability of magnetic resonance
imaging based volumetry in multiple sclerosis.
Neuroimage. 2016; 142:188-97.
56. Cheriyan J, Kim S, Wolansky LJ, Cook SD, Cadavid
D. Impact of inammation on brain volume in