Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK; Imaging Department, UCLH NHS Trust, London, UK.
National Hospital for Neurology and Neurosurgery, Queen Square, London, UK.
Eur J Radiol. 2019 May;114:120-127. doi: 10.1016/j.ejrad.2019.03.003. Epub 2019 Mar 14.
There is increasing evidence that many IDH wildtype (IDHwt) astrocytomas have a poor prognosis and although MR features have been identified, there remains diagnostic uncertainty in the clinic. We have therefore conducted a comprehensive analysis of conventional MR features of IDHwt astrocytomas and performed a Bayesian logistic regression model to identify critical radiological and basic clinical features that can predict IDH mutation status.
146 patients comprising 52 IDHwt astrocytomas (19 WHO Grade II diffuse astrocytomas (A II) and 33 WHO Grade III anaplastic astrocytomas (A III)), 68 IDHmut astrocytomas (53 A II and 15 A III) and 26 GBM were studied. Age, sex, presenting symptoms and Overall Survival were recorded. Two neuroradiologists assessed 23 VASARI imaging descriptors of MRI features and the relation between IDH mutation status and MR and basic clinical features was modelled by Bayesian logistic regression, and survival by Kaplan-Meier plots.
The features of greatest predictive power for IDH mutation status were, age at presentation (OR = 0.94 +/-0.03), tumour location within the thalamus (OR = 0.15 +/-0.25), involvement of speech receptive areas (OR = 0.21 +/-0.26), deep white matter invasion of the brainstem (OR = 0.10 +/-0.32), and T1/FLAIR signal ratio (OR = 1.63 +/-0.64). A logistic regression model based on these five features demonstrated excellent out-of-sample predictive performance (AUC = 0.92 +/-0.07; balanced accuracy 0.81 +/-0.09). Stepwise addition of further VASARI variables did not improve performance.
Five demographic and VASARI features enable excellent individual prediction ofIDH mutation status, opening the way to identifying patients with IDHwt astrocytomas for earlier tissue diagnosis and more aggressive management.
越来越多的证据表明，许多 IDH 野生型（IDHwt）星形细胞瘤预后较差，尽管已经确定了 MRI 特征，但临床上仍存在诊断不确定性。因此，我们对 IDHwt 星形细胞瘤的常规 MRI 特征进行了全面分析，并进行了贝叶斯逻辑回归模型分析，以确定能够预测 IDH 突变状态的关键影像学和基本临床特征。
共纳入 146 例患者，其中 52 例 IDHwt 星形细胞瘤（19 例 WHO 分级 II 级弥漫性星形细胞瘤（A II）和 33 例 WHO 分级 III 级间变性星形细胞瘤（A III）），68 例 IDHmut 星形细胞瘤（53 例 A II 和 15 例 A III）和 26 例 GBM。记录年龄、性别、首发症状和总生存期。两名神经放射科医生评估了 23 个 VASARI 成像描述符的 MRI 特征，采用贝叶斯逻辑回归模型对 IDH 突变状态与 MR 和基本临床特征之间的关系进行建模，并通过 Kaplan-Meier 图进行生存分析。
对 IDH 突变状态具有最大预测能力的特征是发病时的年龄（OR=0.94±0.03）、丘脑内肿瘤位置（OR=0.15±0.25）、语言接受区受累（OR=0.21±0.26）、脑干深部白质侵犯（OR=0.10±0.32）和 T1/FLAIR 信号比（OR=1.63±0.64）。基于这五个特征的逻辑回归模型显示出出色的样本外预测性能（AUC=0.92±0.07；平衡准确性 0.81±0.09）。逐步增加其他 VASARI 变量并不能提高性能。
五个人口统计学和 VASARI 特征能够极好地预测 IDH 突变状态，为识别 IDHwt 星形细胞瘤患者以进行早期组织诊断和更积极的治疗开辟了道路。
