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23 October 2015 Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI
Michaela Spiteri, David Windridge, Shivaram Avula M.D., Ram Kumar M.D., Emma Lewis
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Journal of Medical Imaging, Vol. 2, Issue 4, 044502 (October 2015). https://doi.org/10.1117/1.JMI.2.4.044502
Abstract
Up to 25% of children who undergo brain tumor resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterized by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in structures within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Longitudinal MRI datasets of 28 patients were acquired consisting of pre-, intra-, and postoperative scans. A semiautomated segmentation process was used to segment the ION on each MR image. A full set of imaging features describing the first- and second-order statistics and size of the ION were extracted for each image. Feature selection techniques were used to identify the most relevant features among the MRI features, demographics, and data based on neuroradiological assessment. A support vector machine was used to analyze the discriminative features selected by a generative k-nearest neighbor algorithm. The results indicate the presence of hyperintensity in the left ION as the most diagnostically relevant feature, providing a statistically significant improvement in the classification of patients (p=0.01) when using this feature alone.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Michaela Spiteri, David Windridge, Shivaram Avula M.D., Ram Kumar M.D., and Emma Lewis "Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI," Journal of Medical Imaging 2(4), 044502 (23 October 2015). https://doi.org/10.1117/1.JMI.2.4.044502
Published: 23 October 2015
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Cited by 11 scholarly publications.
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KEYWORDS
Ions
Magnetic resonance imaging
Image segmentation
Information operations
Tumors
Feature selection
Surgery
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