国家神经系统疾病临床医学研究中心数据管理与共享系统

Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

《国家中心数据管理与共享系统操作说明书 —— 研究者部分》

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

第一作者：Li Yiming,Qian Zenghui

通讯作者：Jiang Tao,Liu Xing,Wang Yinyan

Lower-grade gliomas Machine learning Prediction Ra

基本信息

2017-10-29

已发表文章

发表杂志：Neuroimage Clin.

发表类型: Research Article

影响因子(IF): 3.943

PMID: 29527478 Pubmed原文链接 全文下载

主要研究背景及目的

方法

Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n = 180) or validation (n = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets.

结论

These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

主要结果

The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features.