Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Frontiers in Oncology

Zenghui Qian¹  Jie Hu¹  Fei Zheng²  Huicong Shen²  Xuzhu Chen²  Lingling Zhang²  Hongyan Chen²  Yuying Zang²  Shuguang Chen³ 

[1] Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;School of Mathematical Sciences, Nankai University, Tianjin, China;

关键词: gliosarcoma;    glioblastoma;    machine learning;    radiomics;    differentiation;   

DOI  :  10.3389/fonc.2021.699789

来源: DOAJ

【 摘 要 】

ObjectiveTo identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM).Materials and MethodsThis retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.9 years; range, 16-77 years) and 100 patients with GBM (58 men, 42 women; mean age, 53.4 ± 14.1 years; range, 12-77 years) and divided them into a training and validation set randomly. Radiomics features were extracted from the tumor mass and peritumoral edema. Three feature selection and classification methods were evaluated in terms of their performance in distinguishing GSM and GBM: the least absolute shrinkage and selection operator (LASSO), Relief, and Random Forest (RF); and adaboost classifier (Ada), support vector machine (SVM), and RF; respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of each method were analyzed.ResultsBased on tumor mass features, the selection method LASSO + classifier SVM was found to feature the highest AUC (0.85) and ACC (0.77) in the validation set, followed by Relief + RF (AUC = 0.84, ACC = 0.72) and LASSO + RF (AUC = 0.82, ACC = 0.75). Based on peritumoral edema features, Relief + SVM was found to have the highest AUC (0.78) and ACC (0.73) in the validation set. Regardless of the method, tumor mass features significantly outperformed peritumoral edema features in the differentiation of GSM from GBM (P < 0.05). Furthermore, the sensitivity, specificity, and accuracy of the best radiomics model were superior to those obtained by the neuroradiologists.ConclusionOur radiomics study identified the selection method LASSO combined with the classifier SVM as the optimal method for differentiating GSM from GBM based on tumor mass features.

【 授权许可】

Unknown