首页> 外文期刊>Neurosurgical review. >Levine-Sekhar grading system for prediction of the extent of resection of cranial base meningiomas revisited: study of 124 cases.
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Levine-Sekhar grading system for prediction of the extent of resection of cranial base meningiomas revisited: study of 124 cases.

机译:再次探讨了预测颅底脑膜瘤切除程度的Levine-Sekhar分级系统:研究124例。

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INTRODUCTION: Skull base meningiomas comprise an intricate kingdom in neurological surgery. Due to their proximity to critical neurovascular structures, these tumours impose a cumbersome burden on the surgeon regarding surgical intervention and the clinical outcome. Preoperative prediction of the meningioma resectability will help the surgeon seek a rational result from surgery. This study tries to re-examine and promote the Levine-Sekhar (LS) grading system proposed to predict the resectability of basal meningiomas. PATIENTS AND METHODS: A retrospective study was performed on 124 eligible patients (90 female and 34 male) suffering from cranial base meningioma that had been operated on between April 1996 and February 2003. The patients were classified according to LS and our modified grading systems. The modified grading system deploys six groups of variables: optic apparatus involvement, cavernous sinus neural involvement, facial-auditory involvement, caudal cranial nerve dysfunction, data derived from imaging studies (multiple fossa involvement and/or vessel encasement), and history of previous radiosurgery. Each criterion scores 1 if present and the total score is the sum of scores obtained from the aforementioned criteria. RESULTS: Amongst 124 patients, 66 (52%) underwent gross total removal of the tumour. Regression and correlation analysis were performed for both LS (r(2) = 0.9683) and our modified grading systems (r(2) = 0.990) to evaluate the relationship of tumour grade versus the proportion of total resection. The correlations were significantly different (P < 0.01). CONCLUSION: Although the LS grading system is reported to be a good predictor of the extent of tumour resection, we believe that application of the six aforementioned variables will enhance the accuracy of this system, while preserving simplicity and communicability.
机译:简介:颅底脑膜瘤在神经外科手术中是一个复杂的王国。由于它们靠近关键的神经血管结构,因此这些肿瘤给外科医生带来了手术干预和临床结果方面的繁重负担。术前对脑膜瘤可切除性的预测将有助于外科医生从手术中寻求合理的结果。这项研究试图重新检查和推广Levine-Sekhar(LS)分级系统,该系统旨在预测基础脑膜瘤的可切除性。病人和方法:对1996年4月至2003年2月间手术的124例符合条件的颅底脑膜瘤患者进行了回顾性研究。根据LS和我们的分级系统对患者进行了分类。改进的分级系统采用六组变量:视觉设备受累,海绵窦神经受累,面部听觉受累,尾颅神经功能障碍,影像学研究(多次颅窝受累和/或血管包扎)数据以及以前的放射外科手术史。如果存在,则每个标准得分为1,总分是从上述标准获得的得分之和。结果:在124例患者中,有66例(52%)接受了肿瘤的总清除。对LS(r(2)= 0.9683)和我们的改良分级系统(r(2)= 0.990)均进行了回归和相关性分析,以评估肿瘤分级与总切除率之间的关系。相关性显着不同(P <0.01)。结论:尽管据报道,LS分级系统可以很好地预测肿瘤切除的程度,但我们认为应用上述六个变量将提高该系统的准确性,同时保持简便性和可交流性。

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