基于T2WI影像组学模型可预测低级别胶质瘤1p/19q的缺失状态

Application of T2WI radiomics to predict 1p/19q status in low-grade gliomas

目的 基于磁共振T2WI构建影像组学模型,预测低级别胶质瘤1p/19q缺失状态的价值。方法 回顾性分析本院经病理证实的154例低级别胶质瘤患者(1p/19q共缺失100例,1p/19q非共缺失54例),按照分层抽样7∶3分成训练集和验证集。使用3DSlicer软件对肿瘤区域进行手动分割,用pyradiomics进行特征提取。临床资料的分析采用t检验/χ^(2)检验;影像组学特征采用方差法和10折交叉验证的LASSO算法进行筛选,最后建立支持向量机、高斯朴素贝叶斯、K-近邻、逻辑回归模型,采用ROC曲线的曲线下面积值和sklearn分类报告中的参考指标(准确度、敏感度、特异性、F1分数)进行效能评价。结果 4种模型中,支持向量机的曲线下面积值最高,训练集和验证集分别为0.95、0.91;参考指标中表现最佳为K-近邻,其准确度、敏感度、特异性及F1分数分别为0.87、0.97、0.70、0.91;其次为支持向量机,各项指标与模型平均值相当。结论 基于T2WI影像组学模型可以有效地预测低级别胶质瘤1p/19q的缺失状态。

Objective To predict 1p/19q deletion status in low-grade gliomas by construction of the radiomics model based on magnetic resonance T2WI.Methods A total of 154 patients with low-grade gliomas confirmed by pathology in our hospital(including 100 cases of 1p/19q deletion and 54 cases of non-deletion of 1p/19q) were retrospectively analyzed and divided into training set and validation set according to stratified sampling at 7∶3.The tumor region was segmented manually by 3D-Slicer software and the features were extracted by pyradiomics.The clinical data were analyzed by t-test and χ^(2)test,and the radiomics features were screened by variance method and 10-fold cross-validation LASSO algorithm.Finally,the models of support vector machine,gaussian na?ve bayes、K-nearest neighbor and logistic regreesion were established,and the efficacy was evaluated by the area under the ROC curve and the reference indexes(accuracy,sensitivity,specificity,F1 score) in sklearn classification report.Results Among the four models,the AUC value of support vector machine was the highest,the training set and verification set were 0.95 and 0.91 respectively,the best reference index was K-nearest neighbor,its accuracy,sensitivity,specificity and F1 score were 0.87,0.97,0.70,0.91 respectively,followed by support vector machine,each index was equal to the average value of the model.Conclusion Based on the T2WI radiomics model,the state of 1p/19q deletion in low-grade gliomas can be effectively predicted.