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

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OBJECTIVE

This 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).

METHODS

The 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.

RESULTS

The 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%.

CONCLUSIONS

The 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.

ABBREVIATIONS ADFD = average direct-flip distance; AED = antiepileptic drug; AUC = area under the curve; β* = optimized β; BA = Broca’s area; DWI = diffusion-weighted imaging; ECoG = electrocorticography; ESM = electrical stimulation mapping; fMRI = functional MRI; FN = false negative; MAP = maximum a posteriori probability; ROC = receiver operating characteristic; TP = true positive.

Downloadable materials

  • Supplemental Figs. 1 and 2 and Supplemental Table 1 (PDF 2.07 MB)

Article Information

Correspondence Jeong-Won Jeong: Children’s Hospital of Michigan, Wayne State University, Detroit, MI. jjeong@med.wayne.edu.

INCLUDE WHEN CITING Published online February 22, 2019; DOI: 10.3171/2018.11.PEDS18601.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

© AANS, except where prohibited by US copyright law.

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Figures

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    Representative examples of C1–9 pathways obtained from original DWI-MAP (left) and improved DWI-MAP (right, optimized ADFD threshold β* = 16, 9, 8, 14, 12, 16, 13, 13, and 13 mm for C1–9, respectively). White arrows indicate false-positive fibers (i.e., outliers) in the original DWI-MAP, which were appropriately corrected by the improved DWI-MAP. Figure is available in color online only.

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    Spatial overlap between cortical terminals of DWI-MAP pathways and cortical mapping by ESM. Terminals from preoperative C1 = face motor pathway (case 18 with postoperative face motor weakness) and C4 = Broca’s area–Wernicke’s area pathway (case 18 with postoperative language deficit) were compared with their ground truth locations determined by ESM. Figure is available in color online only.

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    Representative examples of C2 = hand motor pathway (case 13 with postoperative hand motor weakness), C4 = Broca’s area–Wernicke’s area pathway (case 9 with no postoperative language deficit), and C9 = occipital lobe–lateral geniculate nucleus (case 8 with postoperative visual field deficit), obtained from a set of preoperative (blue fibers) and postoperative (red fibers) DWI analyses using the original DWI-MAP and the improved DWI-MAP (DWI-MAP+ADFD). For both original and improved DWI-MAP pathways, Δ indicates postoperative fiber volume change normalized by preoperative fiber volume. Figure is available in color online only.

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    Case 6. Automatic detection of three primary motor pathways using the improved DWI-MAP classifier: C1 (face), C2 (hand), C3 (leg). This patient has an ECoG-determined seizure onset zone (SOZ) in the precentral gyrus (red or green spheres, left). The ESM electrode sites for face, finger, and leg motor areas (blue or green spheres) were spatially well matched with cortical terminals of C1 (face), C2 (hand), and C3 (leg) pathways, respectively. Cortical terminals of C1 (face), C2 (hand), and C3 (leg) pathways were included in resected volumes (blue area, right) causing severe motor impairment in this patient. Figure is available in color online only.

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