Literature DB >> 30797207

Prediction of postoperative deficits using an improved diffusion-weighted imaging maximum a posteriori probability analysis in pediatric epilepsy surgery.

Min-Hee Lee1,2, Nolan B O'Hara3,2, Yasuo Nakai1, Aimee F Luat1,4, Csaba Juhasz1,4,5,3,2, Sandeep Sood5, Eishi Asano1,4,3, Jeong-Won Jeong1,4,3,2.   

Abstract

OBJECTIVEThis study is aimed at improving the clinical utility of diffusion-weighted imaging maximum a posteriori probability (DWI-MAP) analysis, which has been reported to be useful for predicting postoperative motor, language, and visual field deficits in pediatric epilepsy surgery. The authors determined the additive value of a new clustering mapping method in which average direct-flip distance (ADFD) reclassifies the outliers of original DWI-MAP streamlines by referring to their minimum distances to the exemplar streamlines (i.e., medoids).METHODSThe authors studied 40 children with drug-resistant focal epilepsy (mean age 8.7 ± 4.8 years) who had undergone resection of the presumed epileptogenic zone and had five categories of postoperative deficits (i.e., hemiparesis involving the face, hand, and/or leg; dysphasia requiring speech therapy; and/or visual field cut). In pre- and postoperative images of the resected hemisphere, DWI-MAP identified a total of nine streamline pathways: C1 = face motor area, C2 = hand motor area, C3 = leg motor area, C4 = Broca's area-Wernicke's area, C5 = premotor area-Broca's area, C6 = premotor area-Wernicke's area, C7 = parietal area-Wernicke's area, C8 = premotor area-parietal area, and C9 = occipital lobe-lateral geniculate nucleus. For each streamline of the identified pathway, the minimal ADFD to the nine exemplars corrected the pathway membership. Binary logistic regression analysis was employed to determine how accurately two fractional predictors, Δ1-9 (postoperative volume change of C1-9) and γ1-9 (preoperatively planned volume of C1-9 resected), predicted postoperative motor, language, and visual deficits.RESULTSThe addition of ADFD to DWI-MAP analysis improved the sensitivity and specificity of regression models for predicting postoperative motor, language, and visual deficits by 28% for Δ1-3 (from 0.62 to 0.79), 13% for Δ4-8 (from 0.69 to 0.78), 13% for Δ9 (from 0.77 to 0.87), 7% for γ1-3 (from 0.81 to 0.87), 1% for γ4-8 (from 0.86 to 0.87), and 24% for γ9 (from 0.75 to 0.93). Preservation of the eloquent pathways defined by preoperative DWI-MAP analysis with ADFD (up to 97% of C1-4,9) prevented postoperative motor, language, and visual deficits with sensitivity and specificity ranging from 88% to 100%.CONCLUSIONSThe present study suggests that postoperative functional outcome substantially differs according to the extent of resected white matter encompassing eloquent cortex as determined by preoperative DWI-MAP analysis. The preservation of preoperative DWI-MAP-defined pathways may be crucial to prevent postoperative deficits. The improved DWI-MAP analysis may provide a complementary noninvasive tool capable of guiding the surgical margin to minimize the risk of postoperative deficits for children.

Entities:  

Keywords:  ADFD = average direct-flip distance; AED = antiepileptic drug; AUC = area under the curve; BA = Broca’s area; DWI = diffusion-weighted imaging; ECoG = electrocorticography; ESM = electrical stimulation mapping; FN = false negative; MAP = maximum a posteriori probability; ROC = receiver operating characteristic; TP = true positive; diffusion-weighted imaging tractography; epilepsy; fMRI = functional MRI; functional brain atlas; functionally important regions; outcome prediction; subcortical areas; β* = optimized β

Year:  2019        PMID: 30797207      PMCID: PMC9019725          DOI: 10.3171/2018.11.PEDS18601

Source DB:  PubMed          Journal:  J Neurosurg Pediatr        ISSN: 1933-0707            Impact factor:   2.713


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2.  Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

Authors:  Min-Hee Lee; Nolan O'Hara; Masaki Sonoda; Naoto Kuroda; Csaba Juhasz; Eishi Asano; Ming Dong; Jeong-Won Jeong
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3.  Novel diffusion tractography methodology using Kalman filter prediction to improve preoperative benefit-risk analysis in pediatric epilepsy surgery.

Authors:  Min-Hee Lee; Nolan B O'Hara; Hirotaka Motoi; Aimee F Luat; Csaba Juhász; Sandeep Sood; Eishi Asano; Jeong-Won Jeong
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