Fully-Automated Identification of Imaging Biomarkers for Post-Operative Cerebellar Mutism Syndrome Using Longitudinal Paediatric MRI.
Neuroinformatics
; 18(1): 151-162, 2020 01.
Article
en En
| MEDLINE
| ID: mdl-31254271
ABSTRACT
Post-operative cerebellar mutism syndrome (POPCMS) in children is a post- surgical complication which occurs following the resection of tumors within the brain stem and cerebellum. High resolution brain magnetic resonance (MR) images acquired at multiple time points across a patient's treatment allow the quantification of localized changes caused by the progression of this syndrome. However, MR images are not necessarily acquired at regular intervals throughout treatment and are often not volumetric. This restricts the analysis to 2D space and causes difficulty in intra- and inter-subject comparison. To address these challenges, we have developed an automated image processing and analysis pipeline. Multi-slice 2D MR image slices are interpolated in space and time to produce a 4D volumetric MR image dataset providing a longitudinal representation of the cerebellum and brain stem at specific time points across treatment. The deformations within the brain over time are represented using a novel metric known as the Jacobian of deformations determinant. This metric, together with the changing grey-level intensity of areas within the brain over time, are analyzed using machine learning techniques in order to identify biomarkers that correspond with the development of POPCMS following tumor resection. This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that are related to the development of POPCMS following tumor resection in the posterior fossa.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Complicaciones Posoperatorias
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Procesamiento de Imagen Asistido por Computador
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Imagen por Resonancia Magnética
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Cerebelo
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Mutismo
Tipo de estudio:
Diagnostic_studies
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Etiology_studies
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Observational_studies
Límite:
Adolescent
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Child
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Child, preschool
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Female
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Humans
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Infant
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Male
Idioma:
En
Año:
2020
Tipo del documento:
Article