Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China.
Chinese Academy of Sciences, Institute of Automation, Beijing, China.
Eur Radiol. 2018 Jul;28(7):2960-2968. doi: 10.1007/s00330-017-5267-0. Epub 2018 Feb 5.
To predict ATRX mutation status in patients with lower-grade gliomas using radiomic analysis.
Cancer 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.
Nine 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.
Using radiomic analysis, we achieved efficient prediction of ATRX genotype in lower-grade gliomas, and our model was effective in two independent databases.
• 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.
利用放射组学分析预测低级别胶质瘤患者的 ATRX 突变状态。
癌症基因组图谱（TCGA）的低级别胶质瘤患者被随机分配到训练集（n = 63）和验证集（n = 32）。基于中国基因组图谱（CGGA）数据库建立了独立的外部验证集（n = 91）。在特征提取后，构建了一个与 ATRX 相关的特征。随后，利用放射组学特征和支持向量机来预测训练集、验证集和外部验证集中的 ATRX 突变状态。通过接收者操作特征曲线分析评估预测性能。还评估了所选特征之间的相关性。
基于 LASSO 回归模型，筛选出 9 个放射组学特征作为低级别胶质瘤与 ATRX 相关的放射组学特征。所有 9 个放射组学特征均为纹理相关（如总和平均值和方差）。在训练集、验证集和外部验证集中，曲线下面积测量的预测效率分别为 94.0%、92.5%和 72.5%。在 TCGA 和 CGGA 数据库中，这 9 个放射组学特征之间的总体相关性均较低。
利用放射组学分析，我们实现了对低级别胶质瘤中 ATRX 基因型的有效预测，我们的模型在两个独立的数据库中均有效。
• 可以使用放射组学分析预测低级别胶质瘤中的 ATRX。• LASSO 回归算法和 SVM 在放射组学分析中表现良好。• 筛选出 9 个作为 ATRX 预测放射组学特征的放射组学特征。• 用于 ATRX 预测的机器学习模型通过独立数据库进行验证。
