Xiao Zheng, Yao Shun, Wang Zong-Ming, Zhu Di-Min, Bie Ya-Nan, Zhang Shi-Zhong, Chen Wen-Li
Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Center for Pituitary Tumor Surgery, Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Front Oncol. 2021 May 31;11:663451. doi: 10.3389/fonc.2021.663451. eCollection 2021.
Synaptophysin (SYP) gene expression levels correlate with the survival rate of glioma patients. This study aimed to explore the feasibility of applying a multiparametric magnetic resonance imaging (MRI) radiomics model composed of a convolutional neural network to predict the SYP gene expression in patients with glioma.
Using the TCGA database, we examined 614 patients diagnosed with glioma. First, the relationship between the SYP gene expression level and outcome of survival rate was investigated using partial correlation analysis. Then, 7266 patches were extracted from each of the 108 low-grade glioma patients who had available multiparametric MRI scans, which included preoperative T1-weighted images (T1WI), T2-weighted images (T2WI), and contrast-enhanced T1WI images in the TCIA database. Finally, a radiomics features-based model was built using a convolutional neural network (ConvNet), which can perform autonomous learning classification using a ROC curve, accuracy, recall rate, sensitivity, and specificity as evaluation indicators.
The expression level of SYP decreased with the increase in the tumor grade. With regard to grade II, grade III, and general patients, those with higher SYP expression levels had better survival rates. However, the SYP expression level did not show any significant association with the outcome in Level IV patients.
Our multiparametric MRI radiomics model constructed using ConvNet showed good performance in predicting the SYP gene expression level and prognosis in low-grade glioma patients.
突触素（SYP）基因表达水平与胶质瘤患者的生存率相关。本研究旨在探讨应用由卷积神经网络组成的多参数磁共振成像（MRI）放射组学模型预测胶质瘤患者SYP基因表达的可行性。
利用TCGA数据库，我们研究了614例诊断为胶质瘤的患者。首先，采用偏相关分析研究SYP基因表达水平与生存率结果之间的关系。然后，从108例有可用多参数MRI扫描的低级别胶质瘤患者中，每人提取7266个图像块，这些扫描包括TCIA数据库中的术前T1加权图像（T1WI）、T2加权图像（T2WI）和对比增强T1WI图像。最后，使用卷积神经网络（ConvNet）建立基于放射组学特征的模型，该模型可以使用ROC曲线、准确率、召回率、灵敏度和特异性作为评估指标进行自主学习分类。
SYP的表达水平随肿瘤分级的增加而降低。对于II级、III级和一般患者，SYP表达水平较高者生存率较好。然而，SYP表达水平在IV级患者中与预后无显著相关性。
我们使用ConvNet构建的多参数MRI放射组学模型在预测低级别胶质瘤患者的SYP基因表达水平和预后方面表现良好。
