Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma

作者：Jinfa Ren, Xiaoyang Zhai, Huijia Yin, Feng-Mei Zhou, Ying Hu, Kaiyu Wang, Ruifang Yan, Dongming Han · 发表于：Neurology and Therapy · 年份：2023 · DOI：10.1007/s40120-023-00524-2 · 被引用次数：22 · 研究领域：Glioma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Brain Tumor Detection and Classification

INTRODUCTION: Conventional magnetic resonance imaging (MRI) features have difficulty distinguishing glioma true tumor recurrence (TuR) from treatment-related effects (TrE). We aimed to develop a machine-learning model based on multimodality MRI radiomics to help improve the efficiency of identifying glioma TuR. METHODS: A total of 131 patients were enrolled and randomly divided into the training set (n = 91) and the test set (n = 40). Radiomic features were extracted from the postoperative enhancement (PoE) region and edema (ED) region from four routine MRI sequences. After analyses of Spearman's rank correlation coefficient, and least absolute shrinkage and selection operator, the key radiomic features were selected to construct support vector machine (SVM) and k-nearest neighbor (KNN) models. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to analyze the performance. RESULTS: The PoE model had a significantly higher area under curve (AUC) than the ED model (p < 0.05). Among the models constructed with a single sequence, the model using PoE regional features from CE-T1WI was superior to other models, with an AUC of 0.905 for SVM and 0.899 for KNN. In multimodality models, the PoE model outperformed the ED model with an AUC of 0.931 for SVM and 0.896 for KNN. The multimodality model, which combined routine sequences and the whole regional features, showed a slightly better performance with an AUC of 0.965 for SVM and 0.955 for KNN. D...