Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

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Shams , B , Wang , Z , Roine , T , Aydogan , D B , Vajkoczy , P , Lippert , C , Picht , T & Fekonja , L S 2022 , ' Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract ' , Brain communications , vol. 4 , no. 3 , 141 . https://doi.org/10.1093/braincomms/fcac141

Title: Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract
Author: Shams, Boshra; Wang, Ziqian; Roine, Timo; Aydogan, Dogu Baran; Vajkoczy, Peter; Lippert, Christoph; Picht, Thomas; Fekonja, Lucius S.
Contributor organization: HUS Psychiatry
Department of Psychiatry
Date: 2022-05-02
Language: eng
Number of pages: 17
Belongs to series: Brain communications
ISSN: 2632-1297
DOI: https://doi.org/10.1093/braincomms/fcac141
URI: http://hdl.handle.net/10138/346329
Abstract: Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77%. They show that these metrics are more effective than demographic and clinical variables. Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.
Subject: machine learning
support vector machine
tractography
diffusion MRI
corticospinal tract
CONSTRAINED SPHERICAL DECONVOLUTION
WHITE-MATTER MICROSTRUCTURE
FRACTIONAL ANISOTROPY
FIBER DENSITY
TRACTOGRAPHY
SELECTION
COMPLEX
IMAGES
TISSUE
DTI
3112 Neurosciences
3124 Neurology and psychiatry
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: publishedVersion


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