Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy.
Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy.
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作者：
Whiting AC;Morita-Sherman M;Li M;Vegh D;Machado de Campos B;Cendes F;Wang X;Bingaman W;Jehi LE
接受额叶切除术的患者比接受颞叶切除术和其他几种切除干预的患者显示出更低的无瘤率。我们试图利用自动化术前定量分析局灶性和整体皮质体积损失，以开发额叶切除术后癫痫发作结局的预测体积指标。90例接受额叶切除术的患者根据平均随访时间为3.5（标准差[SD] 2.5）年时的癫痫发作率进行分层。在术前T1加权磁共振成像（MRI）研究中，对不同脑区和偏侧组织的皮质体积损失进行自动定量分析。采用单变量统计分析来选择无癫痫发作的潜在预测因素。使用向后变量选择和多元逻辑回归来开发预测癫痫发作自由度的模型。末次随访时，90例患者中有48例（53.3%）无并发症。在无癫痫和非癫痫发作队列中，几个额叶和额外脑区在体积皮质体积损失和左右侧体积不对称方面表现出统计学显著差异。最终的多变量logistic模型仅利用术前定量MRI数据预测癫痫发作结局，c统计量为0.846。使用术前定量MRI数据和先前验证的癫痫发作结局的临床预测因子，我们开发了一个c统计量为0.897的模型。这项研究表明，术前额叶和额外区域的皮质体积损失可以预测额叶切除术后的癫痫发作结果，并且可以使用术前MRI数据开发具有良好预测能力的模型。自动定量MRI分析可以快速可靠地对额叶癫痫患者进行，进一步的研究可能会被开发用于整合术前风险分层。
Patients undergoing frontal lobectomy demonstrate lower seizure-freedom rates than patients undergoing temporal lobectomy and several other resective interventions. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy. Ninety patients who underwent frontal lobectomy were stratified based on seizure freedom at a mean follow-up time of 3.5 (standard deviation [SD] 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted magnetic resonance imaging (MRI) studies. Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom. Forty-eight of 90 (53.3%) patients were seizure-free at the last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right sides in the seizure-free and non–seizure-free cohorts. A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes, we developed a model with a c-statistic of 0.897. This study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy, and further studies may be developed for integration into preoperative risk stratification.
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