Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high-grade gliomas
Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high-grade gliomas
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治疗前结构和动脉自旋标记 MRI 可预测高级别胶质瘤中的 p53 突变
DOI：
10.1259/bjr.20200661
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发表时间：
2020-01-01
影响因子：
2.6
通讯作者：
Shen, Jun
中科院分区：
文献类型：
--
作者：
Mao, Jiaji;Deng, Dabiao;Shen, Jun
目的：确定预处理结构和动脉自旋标记（ASL） MRI在预测高级别胶质瘤（HGGs）患者p53突变中的作用。方法：对57例组织学证实的HGGs患者进行术前结构和ASL MRI检查，并获取p53状态信息。对增强肿瘤的脑血流（CBF）图像和HGGs的瘤周水肿进行全病变直方图分析。视觉上可访问的伦勃朗图像特征作为定性分析。分析p53突变前后hgg的ASL直方图参数和视觉可访问伦勃朗图像特征的差异，并进行事后校正进行多重比较。采用LASSO回归选择预测p53突变的最优特征，进行受体操作特征分析，确定预测效果。结果：共纳入33例p53突变HGGs和24例无p53突变HGGs。突变型p53的HGGs与野生型p53的HGGs相比，增强肿瘤的CBFpercentile5和cbff均匀性较低（p < 0.05），增强肿瘤越过中线（ETCM）定性MRI特征的发生率较高（p < 0.05）。LASSO回归显示，增强肿瘤的cb均匀性和ETCM是p53突变的预测特征。CBFuniformity对p53突变的预测效果尚可（曲线下面积= 0.721），结合ETCM的特征，其预测效果显著提高（曲线下面积= 0.814,p = 0.012）。结论：综合治疗前结构MRI和ASL MRI有助于预测hgg中p53突变。
Objectives: To determine the performance of pretreatment structural and arterial spin labelling (ASL) MRI in predicting p53 mutation in patients with high-grade gliomas (HGGs).Methods: Pre-treatment structural and ASL MRI were performed in 57 patients with histologically confirmed HGGs and information of p53 status. Whole-lesion histogram analysis of cerebral blood flow (CBF) images of the enhancing tumour and the peritumoral oedema in the HGGs were performed. Visually AcceSAble Rembrandt Images features were used as qualitative analysis. The differences of ASL histogram parameters and Visually AcceSAble Rembrandt Images features between HGGs with or without p53 mutation were analyzed with post hoc correction for multiple comparisons. LASSO regression was performed to select the optimal features that could predict p53 mutation, followed by receiveroperating characteristic analysis to determine the predictive efficacy.Results: A total of 33 HGGs with p53 mutation and 24 without p53 mutation were included. HGGs with mutant p53 showed lower CBFpercentile5 and CBFuniformity of the enhancing tumour (p < 0.05) and higher prevalence of the qualitative MRI feature of enhancing tumour crossing midline (ETCM) (p < 0.05) as compared with HGGs with wild-type p53. LASSO regression showed that the CBFuniformity of the enhancing tumour and ETCM were predictive features for p53 mutation. CBFuniformity showed an acceptable performance in predicting p53 mutation (area under the curve = 0.721), when combined with the feature of ETCM, its predictive efficacy was significantly improved (area under the curve = 0.814, p = 0.012).Conclusion: An integrated pre-treatment structural and ASL MRI can help to predict p53 mutation in HGGs.