Marcus Hani J, Williams Sophie, Hughes-Hallett Archie, Camp Sophie J, Nandi Dipankar, Thorne Lewis
The Hamlyn Centre, Institute of Global Health Innovation, Imperial College, London, UK.
Department of Neurosurgery, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK.
Neurosurg Rev. 2017 Oct;40(4):621-631. doi: 10.1007/s10143-017-0817-0. Epub 2017 Feb 15.
The lack of a simple, objective and reproducible system to describe glioblastoma multiforme (GBM) represents a major limitation in comparative effectiveness research. The objectives of this study were therefore to develop such a grading system and to validate it on patients who underwent surgical resection. A systematic review of the literature was performed to identify features on pre-operative magnetic resonance imaging (MRI) that predict the surgical outcome of patients with GBM. In all, the five most important features of GBM on pre-operative MRI were as follows: periventricular or deep location, corpus callosum or bilateral location, eloquent location, size and associated oedema. These were then used to develop a grading system. To validate this grading system, a retrospective cohort study of all adult patients with supratentorial GBM who underwent surgical resection between the 1 January 2014 and the 31 June 2015 was performed. There was a substantial agreement between the two neurosurgeons grading GBM (Cohen's κ was 0.625; standard error 0.066). High-complexity lesions were significantly less likely to result in complete resection of contrast-enhancing tumour than low-complexity lesions (50.0 versus 3.4%; p = 0.0007). The proposed grading system may allow for the standardised communication of anatomical features of GBM identified on pre-operative MRI.
缺乏一个简单、客观且可重复的系统来描述多形性胶质母细胞瘤(GBM)是比较疗效研究中的一个主要限制。因此,本研究的目的是开发这样一种分级系统,并在接受手术切除的患者身上进行验证。对文献进行了系统回顾,以确定术前磁共振成像(MRI)上预测GBM患者手术结果的特征。总体而言,GBM在术前MRI上的五个最重要特征如下:脑室周围或深部位置、胼胝体或双侧位置、功能区位置、大小和相关水肿。然后用这些特征开发了一个分级系统。为了验证这个分级系统,对2014年1月1日至2015年6月31日期间接受手术切除的所有幕上GBM成年患者进行了一项回顾性队列研究。两位神经外科医生对GBM的分级之间存在高度一致性(科恩kappa系数为0.625;标准误差为0.066)。高复杂性病变导致增强肿瘤完全切除的可能性明显低于低复杂性病变(50.0%对3.4%;p = 0.0007)。所提出的分级系统可能有助于对术前MRI上识别出的GBM解剖特征进行标准化交流。