MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.
MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.
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MRI 特征通过机器学习方法预测低级别神经胶质瘤中的 p53 状态。
DOI：
10.1016/j.nicl.2017.10.030
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Li Y;Qian Z;Xu K;Wang K;Fan X;Li S;Jiang T;Liu X;Wang Y
P53突变状态是胶质瘤的关键生物标志物。在这里，我们开发了一个机器学习模型，根据从常规磁共振（MR）图像中提取的放射组学特征来预测低级别胶质瘤中的p53状态。回顾性地获得了272例原发性II/III级胶质瘤患者的术前MR图像。患者以2：1的比例随机分配到训练组（n = 180）或验证组（n = 92）。从每位患者中提取了总共431个放射组学特征。最小绝对收缩和选择算子（LASSO）方法用于特征选择和放射组学签名构建。随后，使用所选特征和支持向量机分类器建立了预测p53状态的机器学习模型。使用训练集和验证集中的受试者工作特征曲线计算所有个体特征和模型的预测性能。使用LASSO算法构建p53相关放射组学特征;该程序包括四个一阶统计或相关小波特征（包括最大值、中值、最小值和均匀性）、一个基于形状和大小的特征（球形不比例）和十个纹理特征或相关小波特征（包括相关性、运行百分比和熵）。基于曲线下面积的预测准确率在训练集中为89.6%，在验证集中为76.3%，优于单个特征。这些结果表明，MR图像纹理特征是预测p53突变状态在低级别胶质瘤。因此，我们的程序可以方便地用于促进术前分子病理学诊断。我们基于LASSO算法建立了低级别胶质瘤中p53相关的放射组学特征。我们开发了一个机器学习模型，使用放射组学签名和支持向量机。基于我们的机器学习模型，可以有效地预测低级别胶质瘤的P53突变状态。
P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. 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. 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. 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. We established a p53-related radiomic signature in lower-grade gliomas based on LASSO algorithm. We developed a machine-learning model using the radiomic signature and a support vector machine. P53 mutation status of lower-grade gliomas was predicted effectively based on our machine-learning model.
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影响因子：
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