Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature
Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature
复制标题
使用 MRI 放射组学特征预测低级别胶质瘤 ATRX 突变的基因型
DOI：
10.1007/s00330-017-5267-0
复制
发表时间：
2018-07-01
影响因子：
5.9
通讯作者：
Jiang, Tao
中科院分区：
文献类型：
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作者：
Li, Yiming;Liu, Xing;Jiang, Tao
目的通过放射学分析预测低级别胶质瘤患者的ATRX突变状态。方法将肿瘤基因组图谱(TCGA)中的低级别胶质瘤患者随机分为训练组(n=63)和验证组(n=32)。以中国基因组图谱(CGGA)数据库为基础，建立独立的外部验证集(n=91)。在特征提取后，构造了ATRX相关签名。随后，将放射组学特征与支持向量机相结合来预测训练、验证和外部验证集中的ATRX突变状态。通过接收器操作特征曲线分析评估预测性能。结果根据LASSO回归模型筛选出9个与ATRX相关的低级别胶质瘤的放射组学特征。所有9个放射学特征都与纹理相关(例如总和、平均值和方差)。以曲线下面积衡量的预测效率在训练集、验证集和外部验证集分别为94.0%、92.5%和72.5%。结论通过放射组学分析，我们实现了对低级别胶质瘤ATRX基因分型的有效预测，我们的模型在两个独立的数据库中是有效的。关键点·利用放射组学分析可以预测低级别胶质瘤的ATRX。·Lasso回归算法和支持向量机在放射组学分析中表现良好。·筛选出9个放射组学特征作为ATRX预测的放射组学特征。·ATRX预测的机器学习模型得到了独立数据库的验证。
ObjectivesTo predict ATRX mutation status in patients with lower-grade gliomas using radiomic analysis.MethodsCancer Genome Atlas (TCGA) patients with lower-grade gliomas were randomly allocated into training (n = 63) and validation (n = 32) sets. An independent external-validation set (n = 91) was built based on the Chinese Genome Atlas (CGGA) database. After feature extraction, an ATRX-related signature was constructed. Subsequently, the radiomic signature was combined with a support vector machine to predict ATRX mutation status in training, validation and external-validation sets. Predictive performance was assessed by receiver operating characteristic curve analysis. Correlations between the selected features were also evaluated.ResultsNine radiomic features were screened as an ATRX-associated radiomic signature of lower-grade gliomas based on the LASSO regression model. All nine radiomic features were texture-associated (e.g. sum average and variance). The predictive efficiencies measured by the area under the curve were 94.0 %, 92.5 % and 72.5 % in the training, validation and external-validation sets, respectively. The overall correlations between the nine radiomic features were low in both TCGA and CGGA databases.ConclusionsUsing radiomic analysis, we achieved efficient prediction of ATRX genotype in lower-grade gliomas, and our model was effective in two independent databases.Key Points• ATRX in lower-grade gliomas could be predicted using radiomic analysis.• The LASSO regression algorithm and SVM performed well in radiomic analysis.• Nine radiomic features were screened as an ATRX-predictive radiomic signature.• The machine-learning model for ATRX-prediction was validated by an independent database.