磁共振成像2024，Vol.15Issue(8)：103-109,123,8.DOI:10.12015/issn.1674-8034.2024.08.016
基于磁共振影像组学和语义特征对高级别胶质瘤和转移瘤的鉴别研究
Differentiation of high-grade glioma and metastatic tumor based on MRI radiomics and semantic features
摘要
Abstract
Objective:To combine traditional MRI sequences and enhancement scans,extract multimodal high-throughput radiomics features along with semantic features,and use different learning classifiers to construct various models and draw Normogragh for the differentiation of high-grade glioma (HGG) and solitary brain metastasis (SBM). Materials and Methods:This study retrospectively analyzed multiparametric MRI images of 101 patients. Tumor region of interest (ROI) were delineated by two experienced physicians,and 107 sets of radiomic features for each sequence were extracted using the Pyradiomics software package. To eliminate variability in manual delineation,an intraclass correlation coefficient (ICC) consistency test was carried out. The features with the highest relevance were selected using the maximum relevance minimum redundancy algorithm,and then redundant features were further eliminated using the least absolute shrinkage and selection operator method. Classification models were established using four algorithms:support vector machine,logistic regression,random forest,and K-nearest neighbors. Combining seven semantic features evaluated by radiologists,chi-square test and multivariate analysis were used to remove semantically irrelevant features. Then,a comprehensive model incorporating both radiomics and semantic features was formed and illustrated using nomogram. Finally,the diagnostic capability of each model was evaluated to determine the optimal classifier. Results:Among the radiomics models for HGG and SBM patients,the model with the highest area under the curve (AUC) value was logistic regression,with AUC values of 0.90 for both the training set and test set. In models constructed using semantic features,the random forest model exhibited the best performance,with AUC values of 0.82 and 0.87 for the training and test sets,respectively. After combining semantic features with radiomics scores,the model constructed using logistic regression demonstrated optimal performance,with AUC values of 0.91 and 0.92 for the training and test sets,respectively. Conclusions:The non-invasive approach proposed in this study that utilizes radiomics machine learning classifiers and combines image semantic features to draw nomogram for differentiating between HGG and SBM,demonstrates good accuracy and provides significant assistance for clinical decision-making and practice.关键词
高级别胶质瘤/单发性脑转移瘤/磁共振成像/影像组学/机器学习/语义特征/列线图Key words
high-grade glioma/solitary brain metastasis/magnetic resonance imaging/radiomics/machine learning/semantic features/nomogram分类
医药卫生引用本文复制引用
徐子超,张娅,柳青,史朝霞,王静,卫宏洋,彭兴珍,宗会迁..基于磁共振影像组学和语义特征对高级别胶质瘤和转移瘤的鉴别研究[J].磁共振成像,2024,15(8):103-109,123,8.基金项目
河北省卫生健康委科研基金项目(编号:20230518)Scientific Research Fund Project of Hebei Provincial Health Commission(No.20230518). （编号:20230518）
