TY - GEN
T1 - Is it possible to differentiate the impact of pediatric monophasic demyelinating disorders and multiple sclerosis after a first episode of demyelination?
AU - Aubert-Broche, Bérengère
AU - Fonov, Vladimir
AU - Weier, Katrin
AU - Narayanan, Sridar
AU - Arnold, Douglas L.
AU - Banwell, Brenda
AU - Collins, D. Louis
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - A first episode of acute demyelination of the central nervous system may be a monophasic transient illness or represent the first attack of multiple sclerosis (MS). This study investigates if it is possible to distinguish these two groups of patients retrospectively at the time of the first episode, in a pediatric population. For each patient, the method consists in fitting an individual brain growth curve using multiple follow-up time-points, and using this curve to predict 4 metrics at the first attack: brain volume, brain growth rate, thalamus volume normalized by the brain volume (called normalized thalamus) and normalized thalamus growth rate. These metrics were compared to age-and-sex matched healthy controls by computing z-scores. In this study, 85 patients were scanned up to 8 years after the first attack. During this follow-up period, 23 patients were subsequently diagnosed with MS (MS group). Among the 62 patients with a transient illness, 9 suffered from monophasic acute disseminated encephalomyelitis (ADEM group). The 53 remaining formed the non-ADEM monophasic (MONO) group. The normalized thalamus growth rate was the only metric that distinguished patient groups: the z-scores were significantly smaller for MS than for the MONO group (p<0.01). Whereas 93% of monophasic subjects were correctly classified with a linear discriminant analysis, only 13% of the MS subjects were correctly classified, due to a large inter-individual variability in this group.
AB - A first episode of acute demyelination of the central nervous system may be a monophasic transient illness or represent the first attack of multiple sclerosis (MS). This study investigates if it is possible to distinguish these two groups of patients retrospectively at the time of the first episode, in a pediatric population. For each patient, the method consists in fitting an individual brain growth curve using multiple follow-up time-points, and using this curve to predict 4 metrics at the first attack: brain volume, brain growth rate, thalamus volume normalized by the brain volume (called normalized thalamus) and normalized thalamus growth rate. These metrics were compared to age-and-sex matched healthy controls by computing z-scores. In this study, 85 patients were scanned up to 8 years after the first attack. During this follow-up period, 23 patients were subsequently diagnosed with MS (MS group). Among the 62 patients with a transient illness, 9 suffered from monophasic acute disseminated encephalomyelitis (ADEM group). The 53 remaining formed the non-ADEM monophasic (MONO) group. The normalized thalamus growth rate was the only metric that distinguished patient groups: the z-scores were significantly smaller for MS than for the MONO group (p<0.01). Whereas 93% of monophasic subjects were correctly classified with a linear discriminant analysis, only 13% of the MS subjects were correctly classified, due to a large inter-individual variability in this group.
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U2 - 10.1007/978-3-319-14905-9_4
DO - 10.1007/978-3-319-14905-9_4
M3 - Conference contribution
AN - SCOPUS:84927919030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 38
EP - 48
BT - Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - 3rd International Workshop, STIA 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers
A2 - Durrleman, Stanley
A2 - Fletcher, Tom
A2 - Gerig, Guido
A2 - Niethammer, Marc
A2 - Pennec, Xavier
PB - Springer Verlag
T2 - 3rd International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014
Y2 - 18 September 2014 through 18 September 2014
ER -