Predictive Value of Neonatal MRI with Respect to Late MRI Findings and Clinical Outcome. A Study in Infants with Periventricular Densities on Neonatal Ultrasound

Sie, L.T.L.; Hart, A.A.M.; Hof, J van; Groot, Laila de; Lems, Willem F.; Lafeber, H N; Valk, Jacob; Knaap, Marjo S. van der

doi:10.1055/s-2005-837574

Cited by 57 publications

(44 citation statements)

References 26 publications

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“…28 Others have revealed that MRI may provide additive information to predict neuromotor outcomes, 29 complementary to specific findings such as periventricular echodensities by CUS, 30 or neurologic examination. 31,32 Our results suggest that some type of near-term imaging (late CUS or brain MRI) adds value over perinatal/neonatal factors and early CUS alone.…”

Section: Discussionmentioning

confidence: 99%

Neuroimaging and Neurodevelopmental Outcome in Extremely Preterm Infants

et al. 2015

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BACKGROUND: Extremely preterm infants are at risk for neurodevelopmental impairment (NDI). Early cranial ultrasound (CUS) is usual practice, but near-term brain MRI has been reported to better predict outcomes. We prospectively evaluated MRI white matter abnormality (WMA) and cerebellar lesions, and serial CUS adverse findings as predictors of outcomes at 18 to 22 months' corrected age.METHODS: Early and late CUS, and brain MRI were read by masked central readers, in a large cohort (n = 480) of infants ,28 weeks' gestation surviving to near term in the Neonatal Research Network. Outcomes included NDI or death after neuroimaging, and significant gross motor impairment or death, with NDI defined as cognitive composite score ,70, significant gross motor impairment, and severe hearing or visual impairment. Multivariable models evaluated the relative predictive value of neuroimaging while controlling for other factors.RESULTS: Of 480 infants, 15 died and 20 were lost. Increasing severity of WMA and significant cerebellar lesions on MRI were associated with adverse outcomes. Cerebellar lesions were rarely identified by CUS. In full multivariable models, both late CUS and MRI, but not early CUS, remained independently associated with NDI or death (MRI cerebellar lesions: odds ratio, 3.0 [95% confidence interval: 1.3-6.8]; late CUS: odds ratio, 9.8 [95% confidence interval: 2.8-35]), and significant gross motor impairment or death. In models that did not include late CUS, MRI moderate-severe WMA was independently associated with adverse outcomes.CONCLUSIONS: Both late CUS and near-term MRI abnormalities were associated with outcomes, independent of early CUS and other factors, underscoring the relative prognostic value of near-term neuroimaging.WHAT'S KNOWN ON THIS SUBJECT: White matter abnormality (WMA) on neuroimaging is considered a crucial link with adverse neurodevelopmental outcome in preterm infants. Brain MRI is more sensitive in detecting WMA than cranial ultrasound (CUS), but questions remain about timing and prognostic value of modalities.WHAT THIS STUDY ADDS: Near-term CUS and MRI abnormalities were associated with adverse 18-to 22-month outcomes, independent of early CUS and other factors, underscoring the relative prognostic value of later neuroimaging in this large, extremely preterm cohort surviving to near-term. Trained research staff at each center collected maternal, demographic, perinatal, and neonatal data by using common definitions that were developed by NICHD NRN investigators and described in previous publications. [13][14][15][16] Data were transmitted to the NRN Data Coordinating Center at RTI International, which stored, managed, and analyzed all data. Neuroimaging: CUS and Brain MRI Cranial UltrasoundAn "early" CUS at 4 to 14 days, and a "late" CUS at 35 to 42 weeks' postmenstrual age (PMA) were obtained for NEURO study participants. CUS imaging was per local center clinical protocol. Mastoid, posterior fossa, or cine views were not specifically required. Central reader interpretat...

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Section: Discussionmentioning

confidence: 99%

Neuroimaging and Neurodevelopmental Outcome in Extremely Preterm Infants

et al. 2015

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“…Furthermore, the aetiology of CP in individual cases is often difficult to precisely establish [Paneth et al, 2006], and the variability in the cerebral injury covered by the term CP means that outcomes predicted by current diagnosis can be inconsistent [Bartlett and Palisano, 2002]. Multiple semi-quantitative scoring systems have been developed to address these issues and to standardise the assessment of MRIs [Cioni et al, 1997;Fiori et al, 2014;Inder et al, 2003;Kidokoro et al, 2013;Miller et al, 2005;Shiran et al, 2014;Sie et al, 2005;Skiöld et al, 2013]. In these methods, specific criteria of brain injury are graded based on their severity, or anatomical regions are given a discrete score based on whether the region contained injury or was healthy, and then summed to provide a total measure of injury.…”

Section: Imaging Of Brain Injurymentioning

confidence: 99%

“…In these methods, specific criteria of brain injury are graded based on their severity [Inder et al, 2003;Kidokoro et al, 2013;Miller et al, 2005;Shiran et al, 2014;Sie et al, 2005;Skiöld et al, 2013], or anatomical regions are given a discrete score based on whether the region contained injury or was healthy Shiran et al, 2014]. The sum of these individual sub-scores of injury provides a total measure of injury.…”

Section: Introductionmentioning

confidence: 99%

“…Several manual scoring systems have been developed to standardise the radiological assessment of MRIs Inder et al, 2003;Kidokoro et al, 2013;Miller et al, 2005;Shiran et al, 2014;Sie et al, 2005;Skiöld et al, 2013]. In these methods, specific criteria of brain injury are graded based on their severity [Inder et al, 2003;Kidokoro et al, 2013;Miller et al, 2005;Shiran et al, 2014;Sie et al, 2005;Skiöld et al, 2013], or anatomical regions are given a discrete score based on whether the region contained injury or was healthy Shiran et al, 2014].…”

Section: Introductionmentioning

confidence: 99%

“…Although several of these MRI scoring methods were compared with clinical scores of patient function [Shiran et al, 2014;Sie et al, 2005], they inherently weight the sub-scores through separate injury scales. As with automatically computed biomarkers, there is a need to optimise the weights of these manual clinical scores in order to account for the relative impact that different injuries have on patient function, which would provide a more accurate prediction of patient outcome.…”

Section: Introductionmentioning

confidence: 99%

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Automated injury segmentation to assist in the treatment of children with cerebral palsy

Pagnozzi¹

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Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. It is the most common physical disability of children worldwide and can lead to a wide range of functional impairments. Accurate diagnosis and prognosis, in terms of the type and severity of functional impairment, is difficult due to the wide range of injuries that may occur and the variable effect of plasticity; which leads to inconsistency in the clinical outcomes of children with CP. The use of Magnetic resonance imaging (MRI) to identify and locate brain lesions has facilitated the diagnosis and qualitative classification of children with CP.Currently, the quantification of image findings from structural MRIs is not automated and remains labour intensive, hence is not widely performed for clinical assessment. Automated brain image segmentation techniques could reduce the clinical time required toprovide an accurate and reproducible quantification of injury. Although such approaches have been used to study other neurological disorders, the heterogeneous appearance of injury and the large anatomical distortion that occurs in CP require modification of existing algorithms to be sufficiently reliable. As a result, they have not been widely applied toMRIs of children with CP.In this thesis, a series of automated image quantification techniques are presented for analysing the MRI data obtained from children with CP. These techniques identify and quantify the severity of the three main types of injury observed in children with CP; including ventricular enlargement, cortical malformations and white and grey matter injury.The developed automated pipeline involves a number of technical developments and contributions to the automated analysis of CP MRI data. A brain tissue segmentation approach based on the Expectation Maximisation (EM)/Markov Random Field (MRF) approach was developed, with a modified MRF implementation that expects different tissue labels within a given neighbourhood to have a corresponding intensity gradient.Following this segmentation step, three different approaches were used to identify and quantify the three distinct types of injury observed from children with CP, which are all important for clinical assessment. Biomarkers from each type of injury obtained from these approaches were used as independent variables in a devised statistical methodology designed to elucidate significant and generalisable correlations between image-derived measures of injury and patient function.ii Ventricular enlargement was quantified using a model of lateral ventricular shape that encapsulates healthy variation observed in ventricular shape. The residual of this model to a target shape reveals a volume of injury, allowing a measure of involvement of critical adjacent anatomies, such as the thalamus, caudate nucleus and lenticular nucleus, to be computed. This measure of involvement was devised as a way to translate the indirect injury of ventricular enlargement to the direct in...

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