Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient

基因型的鉴定对于胶质瘤的治疗至关重要。在此，我们开发了一种利用预训练卷积神经网络（CNN）从磁共振（MR）图像预测肿瘤基因型的方法，并将其准确性与基于常规影像组学特征和患者年龄的诊断准确性进行了比较。164例Ⅱ/Ⅲ级胶质瘤患者的多部位术前MR图像按异柠檬酸脱氢酶（IDH）和端粒酶逆转录酶（TERT）启动子（pTERT）突变分组如下：（1）IDH野生型，（2）IDH和pTERT共同突变型，（3）IDH突变且pTERT野生型。我们将一个CNN（AlexNet）应用于四种类型的MR序列，并获得CNN纹理特征，利用线性支持向量机对这些组进行分类。同时也使用常规影像组学特征和/或患者（此处原文似乎不完整）进行了分类。