Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI

Article

Spiteri, M., Guillemaut, J., Windridge, D., Avula, S., Kumar, R. and Lewis, E. 2020. Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI. Neuroinformatics. 18 (1), pp. 151-162. https://doi.org/10.1007/s12021-019-09427-w
TypeArticle
TitleFully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI
AuthorsSpiteri, M., Guillemaut, J., Windridge, D., Avula, S., Kumar, R. and Lewis, E.
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.

KeywordsMRI; POPCMS; Posterior fossa; Longitudinal; Machine learning
PublisherSpringer
JournalNeuroinformatics
ISSN1539-2791
Electronic1559-0089
Publication dates
Online28 Jun 2019
Print31 Jan 2020
Publication process dates
Deposited19 Feb 2020
Accepted28 Jun 2019
Output statusPublished
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Copyright Statement

© The Author(s) 2019
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Digital Object Identifier (DOI)https://doi.org/10.1007/s12021-019-09427-w
Web of Science identifierWOS:000514280700009
LanguageEnglish
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Abbey, C., Hope, C., Sterr, A., Elangovan, P., Geades, N., Windridge, D., Young, K., Wells, K. and Mello-Thoms, C. 2013. High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP). SPIE Proceedings Vol. 8673. https://doi.org/10.1117/12.2007557
Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene
Taya, S., Windridge, D. and Osman, M. 2012. Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene. PLoS ONE. 7 (6). https://doi.org/10.1371/journal.pone.0039060
Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. 2010. Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security. 5 (3), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535