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

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

Journal

Journal of neurosurgery. Pediatrics
ISSN: 1933-0715
Titre abrégé: J Neurosurg Pediatr
Pays: United States
ID NLM: 101463759

Informations de publication

Date de publication:
22 Feb 2019
Historique:
received: 20 09 2018
accepted: 28 11 2018
entrez: 24 2 2019
pubmed: 24 2 2019
medline: 24 2 2019
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 30797207
doi: 10.3171/2018.11.PEDS18601
pii: 2018.11.PEDS18601
pmc: PMC9019725
mid: NIHMS1797128
doi:
pii:

Types de publication

Journal Article

Langues

eng

Pagination

1-12

Subventions

Organisme : NINDS NIH HHS
ID : R01 NS064033
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS089659
Pays : United States

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Auteurs

Min-Hee Lee (MH)

Departments of1Pediatrics.
5Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, Michigan.

Nolan B O'Hara (NB)

4Translational Neuroscience Program, Wayne State University School of Medicine; and.
5Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, Michigan.

Yasuo Nakai (Y)

Departments of1Pediatrics.

Aimee F Luat (AF)

Departments of1Pediatrics.
2Neurology, and.

Csaba Juhasz (C)

Departments of1Pediatrics.
2Neurology, and.
3Neurosurgery.
4Translational Neuroscience Program, Wayne State University School of Medicine; and.
5Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, Michigan.

Sandeep Sood (S)

3Neurosurgery.

Eishi Asano (E)

Departments of1Pediatrics.
2Neurology, and.
4Translational Neuroscience Program, Wayne State University School of Medicine; and.

Jeong-Won Jeong (JW)

Departments of1Pediatrics.
2Neurology, and.
4Translational Neuroscience Program, Wayne State University School of Medicine; and.
5Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, Michigan.

Classifications MeSH