使用多模式数据对三叉神经痛的手术结果进行分层。

IF 4.5 Q1 CLINICAL NEUROLOGY
Brain communications Pub Date : 2025-06-17 eCollection Date: 2025-01-01 DOI:10.1093/braincomms/fcaf178
Timur H Latypov, Rose Yakubov, Daniel Jörgens, Pascale Tsai, Patcharaporn Srisaikaew, Peter Shih-Ping Hung, Matthew R Walker, Marina Tawfik, David Mikulis, Frank Rudzicz, Mojgan Hodaie
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引用次数: 0

摘要

慢性疼痛对临床医生来说仍然是一个挑战,只有有限的个性化预测工具可以帮助诊断、病程或预测治疗结果。我们假设,综合分析,包括患者完整的疼痛相关临床数据,病史和脑成像,可以确定与手术结果相关的关键因素,并对三叉神经痛(TN)-慢性面部疼痛综合征的特定结果分类进行分层。使用监督和无监督机器学习方法,我们分析了102例经典TN患者的数据。通过无监督学习处理术前临床数据,以描述TN结局分层的关键临床因素及其与手术反应的相关性。同时,我们将监督学习应用于术前t1加权脑磁共振成像。临床数据分析揭示了疼痛和非疼痛相关的措施-包括疼痛频率,药物缓解程度,疼痛特征,糖尿病和癌症病史的存在-是预测手术结果最重要的因素。分析显示术前临床资料与手术反应时间有很强的相关性(r = 0.5, P < 0.00001)。影像数据分析采用支持向量机分类模型,对长期应答者和无应答者的召回率分别为0.79和0.86,受试者工作特征曲线下面积(AUC)分别为0.86和0.84。预测手术反应类别持续时间的平均多类别准确率为78% (AUC 0.8)。总之,这些结果表明,TN手术结果类别是可区分的,手术结果可以根据手术治疗前可获得的临床和脑成像数据进行分层。我们建议对不同层次的慢性疼痛疾病,每一个结构成像,临床相关性和具体的手术结果新颖的观点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stratification of surgical outcomes in trigeminal neuralgia using multimodal data.

Chronic pain remains a challenge for clinicians, with limited individualized predictive tools that can aid with diagnosis, disease course, or prediction of treatment outcomes. We hypothesized that a comprehensive analysis, encompassing a patient's complete pain-related clinical data, medical history and brain imaging, can identify key contributors linked to surgical outcomes and stratify specific outcome categories for trigeminal neuralgia (TN)-chronic facial pain syndrome. Using supervised and unsupervised machine learning approaches, we analysed data from 102 subjects with classical TN. Pre-surgical clinical data were processed through unsupervised learning to delineate key clinical contributors of TN outcome stratification and their correlation with surgical response. Concurrently, we applied supervised learning to pre-surgical T1-weighted brain magnetic resonance imaging. Clinical data analysis uncovered pain and non-pain-related measures-including pain frequency, degree of medication relief, pain character, presence of diabetes and cancer history-as the most significant in forecasting surgical outcome. Analysis revealed strong correlation of pre-surgical clinical data with surgical response duration (r = 0.5, P < 0.00001). Imaging data analysis used a support vector machine classification model with high recall for subjects who would be either long-term responders or non-responders 0.79 and 0.86 with the area under the receiver operating characteristic curve (AUC) of 0.86 and 0.84, respectively. The average multiclass accuracy in predicting the duration of surgical response categories was 78% (AUC 0.8). Together, these results show that TN surgical outcome categories are distinguishable, and surgical outcome can be stratified based on combined clinical and brain imaging data available prior to surgical treatment. We suggest a novel perspective on different strata of chronic pain disorders, each with structural imaging, clinical correlates and specific surgical outcomes.

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CiteScore
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