目的:基于多模态磁共振成像(MRI)影像组学,开发一种非侵入性方法来预测弥漫性低级别胶质瘤(DLGG)中1p/19q共缺失。
方法:我们收集了10月之间104例经病理证实的DLGG患者的MRI数据,2015年9月,2022年。总共从T2WI中提取了535个影像组学特征,T1WI,FLAIR,CE-T1WI和DWI,包括70个形态特征,90个一阶特征,和375纹理特征。我们构建了逻辑回归(LR),逻辑回归最小绝对收缩和选择算子(LRlasso),支持向量机(SVM)和线性判别分析(LDA)影像组学模型,并在10倍交叉验证后比较了它们的预测性能。由两名放射科医生独立审查MRI图像以预测1p/19q状态。接收器工作特征曲线用于评估放射组学模型和放射科医师的分类性能。
结果:4种影像组学模型(LR,LRlasso,SVM和LDA)在验证数据集中实现了相似的曲线下面积(AUC)(分别为0.833、0.819、0.824和0.819;P>0.1),他们的预测性能均优于放射科住院医师(AUC分别为0.645,P分别为0.011,0.022,0.016,0.030),与放射科主治医师相似(AUC=0.838,P>0.05)。
结论:多参数MRI影像组学模型在无创性预测弥漫性低度胶质瘤患者1p/19q共缺失状态方面表现良好。
OBJECTIVE: To develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics.
METHODS: We collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T2WI, T1WI, FLAIR, CE-T1WI and DWI, including 70 morphological features, 90 first order features, and 375 texture features. We constructed logistic regression (LR), logistic regression least absolute shrinkage and selection operator (LRlasso), support vector machine (SVM) and Linear Discriminant Analysis (LDA) radiomics models and compared their predictive performance after 10-fold cross validation. The MRI images were reviewed by two radiologists independently for predicting the 1p/19q status. Receiver operating characteristic curves were used to evaluate classification performance of the radiomics models and the radiologists.
RESULTS: The 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively; P>0.1), and their predictive performance was all superior to that of resident physicians of radiology (AUC=0.645, P=0.011, 0.022, 0.016, 0.030, respectively) and similar to that of attending physicians of radiology (AUC=0.838, P>0.05).
CONCLUSIONS: Multiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.