Lee Min-Hee, O'Hara Nolan B, Nakai Yasuo, Luat Aimee F, Juhasz Csaba, Sood Sandeep, Asano Eishi, Jeong Jeong-Won
Departments of1Pediatrics.
5Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, Michigan.
J Neurosurg Pediatr. 2019 Feb 22;23(5):648-659. doi: 10.3171/2018.11.PEDS18601. Print 2019 May 1.
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).
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.
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%.
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.
本研究旨在提高扩散加权成像最大后验概率(DWI-MAP)分析的临床实用性,据报道该分析有助于预测小儿癫痫手术术后的运动、语言和视野缺损。作者确定了一种新的聚类映射方法的附加价值,即平均直接翻转距离(ADFD)通过参考其到范例流线(即中心点)的最小距离对原始DWI-MAP流线的异常值进行重新分类。
作者研究了40例耐药性局灶性癫痫患儿(平均年龄8.7±4.8岁),这些患儿接受了假定致痫区切除术,且存在五类术后缺损(即涉及面部、手部和/或腿部的偏瘫;需要言语治疗的言语困难;和/或视野缺损)。在切除半球的术前和术后图像中,DWI-MAP共识别出九条流线通路:C1 = 面部运动区,C2 = 手部运动区,C3 = 腿部运动区,C4 = 布洛卡区-韦尼克区,C5 = 运动前区-布洛卡区,C6 = 运动前区-韦尼克区,C7 = 顶叶区-韦尼克区,C8 = 运动前区-顶叶区,C9 = 枕叶-外侧膝状体核。对于识别出的通路中的每条流线,到九个范例的最小ADFD校正了通路归属。采用二元逻辑回归分析来确定两个分数预测因子Δ1-9(C1-9的术后体积变化)和γ1-9(术前计划切除的C1-9体积)对术后运动、语言和视觉缺损的预测准确性。
将ADFD添加到DWI-MAP分析中,可使预测术后运动、语言和视觉缺损的回归模型的敏感性和特异性分别提高:Δ1-3提高28%(从0.62提高到0.79),Δ4-8提高13%(从0.69提高到0.78),Δ9提高13%(从0.77提高到0.87),γ1-3提高7%(从0.81提高到0.87),γ4-8提高1%(从0.86提高到0.87),γ9提高24%(从0.75提高到0.93)。通过ADFD对术前DWI-MAP分析定义的明确通路进行保留(高达C1-4,9的97%)可预防术后运动、语言和视觉缺损,敏感性和特异性范围为88%至100%。
本研究表明,根据术前DWI-MAP分析确定的包含明确皮层的白质切除范围,术后功能结果存在显著差异。保留术前DWI-MAP定义的通路对于预防术后缺损可能至关重要。改进后的DWI-MAP分析可能提供一种辅助性非侵入性工具,能够指导手术切缘,以将儿童术后缺损风险降至最低。