Roles of different methods in measurement of distance between intracranial arteriovenous malforma-tion and eloquent cortex in prediction of postoperative neurological deficits

Qingyuan Liu, Jun Wu, Pengjun Jiang, Yong Cao
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Abstract

Objectives To explore the predictive values of different methods measuring the distance between lesion and eloquent cortex (LED) in surgery-related functional deficit (SFD) in intracranial arteriovenous malformations (iAVMs). Methods This study retrospectively reviewed and followed up the patients who underwent microsurgical resection of iAVMs at Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University from January to December 2017. Based on the difference (≥1) between the mRS (modified Rankin scale) at 3 months after surgery and that prior to surgery, 17 patients were categorized into the SFD group and 79 into non-SFD group. The eloquent cortex was firstly recognized according to the Spetzler-Martin(S-M)standard. Based on the presurgical T1-weight, T2-weight, time-of-flight, functional and diffuse tensor imaging MR, the LED and modified LED (MLED) were measured. Multivariate logistic analysis was performed to investigate the independent risk factors related to SFD at 3 months post surgery. The ROC (receiver operating characteristic) curve analysis was performed to explore the predicting values of different measuring methods in SFD. Results Multivariate logistic analysis based on 96 patients demonstrated that the S-M grading (OR=3.10), MLED (OR=0.66) and nidus type (diffuse and compact, OR=0.12) were independent risk factors for SFD (all P<0.05). For all iAVMs, both MLED and LED had good predictive value in SFD, and the AUC (area under a curve) values were 0.843 and 0.830 respectively (both P<0.05), while the S-M grading(AUC: 516, P=0.804) did not have predictive value. For compact iAVMs, LED (AUC=0.784) and MLED (AUC=0.726) could effectively predict the risk of SFD(all P<0.05). For diffuse iAVMs, MLED (AUC=0.833) could predict the risk of SFD (P=0.003), whereas LED (AUC=0.583, P=0.665) had poor predicting value. Conclusions The distance between iAVM and eloquent cortex seems to be an important factor to predict the surgical outcome. For compact iAVM, both LED and MLED have good predicting values, while only MLED shows predicting value for diffuse iAVM. Key words: Intracranial arteriovenous malformations; Neurological deficit; Multi-modeimage; Forecasting
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不同方法测量颅内动静脉畸形与雄辩皮层之间的距离在预测术后神经功能缺损中的作用
目的探讨颅内动静脉畸形(iAVM)手术相关功能缺损(SFD)中不同方法测量病变与舌皮质(LED)距离的预测价值。方法对2017年1月至12月在首都医科大学北京天坛医院神经外科接受iAVM显微外科切除的患者进行回顾性分析和随访。根据术后3个月和术前mRS(改良Rankin量表)之间的差异(≥1),17名患者被分为SFD组,79名患者被划分为非SFD组。根据Spetzler-Martin(S-M)标准首次识别出能说会道皮层。基于术前T1重量、T2重量、飞行时间、功能和扩散张量成像MR,测量LED和改良LED(MLED)。对术后3个月与SFD相关的独立危险因素进行多因素logistic分析。进行ROC(受试者工作特性)曲线分析,探讨不同测量方法对SFD的预测值。结果96例患者的多因素logistic分析表明,S-M分级(OR=3.10)、MLED(OR=0.66)和病灶类型(弥漫型和致密型,OR=0.12)是SFD的独立危险因素(均P<0.05),而S-M分级(AUC:516,P=0.804)没有预测价值。对于紧凑型iAVM,LED(AUC=0.784)和MLED(AUC=7.726)可有效预测SFD的风险(均P<0.05)。对于弥漫型iAVM而言,MLED(AUC=0.833)可预测SFD风险(P=0.003),而LED(AUC=0.583,P=0.665)的预测值较差。结论iAVM与舌皮质的距离是预测手术结果的重要因素。对于紧凑型iAVM,LED和MLED都具有良好的预测值,而只有MLED显示出对扩散型iAVM的预测值。关键词:颅内动静脉畸形;神经系统缺陷;多模式图像;预测
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中华神经外科杂志
中华神经外科杂志 Medicine-Surgery
CiteScore
0.10
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0.00%
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10706
期刊介绍: Chinese Journal of Neurosurgery is one of the series of journals organized by the Chinese Medical Association under the supervision of the China Association for Science and Technology. The journal is aimed at neurosurgeons and related researchers, and reports on the leading scientific research results and clinical experience in the field of neurosurgery, as well as the basic theoretical research closely related to neurosurgery.Chinese Journal of Neurosurgery has been included in many famous domestic search organizations, such as China Knowledge Resources Database, China Biomedical Journal Citation Database, Chinese Biomedical Journal Literature Database, China Science Citation Database, China Biomedical Literature Database, China Science and Technology Paper Citation Statistical Analysis Database, and China Science and Technology Journal Full Text Database, Wanfang Data Database of Medical Journals, etc.
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