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• W2967669976 abstract "It can be challenging to depict brain volume abnormalities in the pediatric population on magnetic resonance imaging (MRI). The aim of the study was to evaluate the inter-radiologist reliability in brain MRI interpretation, including brain volume assessment and the efficiency of an automated brain segmentation.We performed a single-center prospective study including 44 patients aged six months to five years recruited from the University Hospital, having a 1.5T brain MRI using a MP2RAGE sequence. All MRI were randomly and blindly reviewed by one junior and two senior pediatric radiologists. Inter-observer agreements were assessed using Fleiss' kappa coefficient. Brain volumetry (total intracranial volume (TIV), brain parenchyma, and cerebrospinal fluid volumes) was estimated using the MorphoBox prototype. Clinical head circumference (HC) and z scores were reported. A Pearson correlation coefficient was calculated between brain volumes with HC.Twenty-four brain MRI examinations were normal and twenty were pathological. Brain volume abnormalities were poorly detected by junior and senior radiologists: sensitivities 16.67% [confidence interval 4.7-44.8], 33.33% [13-60] and 30.7% [12-58] and specificities 93.75% [79-98], 84.38% [68-93] and 77% [60-88], respectively. Brain volume apart, interobserver kappa coefficients were 0.93 between junior and seniors as well as between seniors. Brain volumes were significantly correlated with HC (P<0.0001). In patients with normal MRI, brain parenchyma volumes increased regularly with age. Low brain volume was easier to identify with automated quantification.Brain volume was poorly appreciated by radiologists. The fully automated brain segmentation used can provide quantitative data to better diagnose, describe, and follow-up brain volume abnormalities." @default.
• W2967669976 created "2019-08-22" @default.
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• W2967669976 date "2021-06-01" @default.
• W2967669976 modified "2023-10-16" @default.
• W2967669976 title "Improving diagnosis accuracy of brain volume abnormalities during childhood with an automated MP2RAGE-based MRI brain segmentation" @default.
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• W2967669976 doi "https://doi.org/10.1016/j.neurad.2019.06.005" @default.
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