Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany.
Department of Neuroradiology, Medical Center, University of Freiburg, Germany.
Neuroradiol J. 2021 Oct;34(5):501-508. doi: 10.1177/19714009211012345. Epub 2021 Apr 30.
The aim of this study was the development and external validation of a logistic regression model to differentiate gliosarcoma (GSC) and glioblastoma multiforme (GBM) on standard MR imaging.
A univariate and multivariate analysis was carried out of a logistic regression model to discriminate patients histologically diagnosed with primary GSC and an age and sex-matched group of patients with primary GBM on presurgical MRI with external validation.
In total, 56 patients with GSC and 56 patients with GBM were included. Evidence of haemorrhage suggested the diagnosis of GSC, whereas cystic components and pial as well as ependymal invasion were more commonly observed in GBM patients. The logistic regression model yielded a mean area under the curve (AUC) of 0.919 on the training dataset and of 0.746 on the validation dataset. The accuracy in the validation dataset was 0.67 with a sensitivity of 0.85 and a specificity of 0.5.
Although some imaging criteria suggest the diagnosis of GSC or GBM, differentiation between these two tumour entities on standard MRI alone is not feasible.
本研究旨在开发和验证一种逻辑回归模型，以在标准磁共振成像上区分神经胶母细胞瘤（GBM）和胶质肉瘤（GSC）。
对术前 MRI 进行单变量和多变量逻辑回归分析，以鉴别组织学诊断为原发性 GSC 的患者和年龄、性别匹配的原发性 GBM 患者，并进行外部验证。
共纳入 56 例 GSC 患者和 56 例 GBM 患者。出血征象提示 GSC 诊断，而囊性成分以及软脑膜和室管膜侵犯在 GBM 患者中更为常见。该逻辑回归模型在训练数据集上的平均曲线下面积（AUC）为 0.919，在验证数据集上为 0.746。验证数据集的准确率为 0.67，灵敏度为 0.85，特异性为 0.5。
尽管一些影像学标准提示 GSC 或 GBM 的诊断，但仅凭标准 MRI 无法区分这两种肿瘤实体。
