Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

Yang Zhang; Chaoyue Chen; Yangfan Cheng; Yuen Teng; Wen Guo; Hui Xu; Xuejin Ou; Jian Wang; Hui Li; Xuelei Ma; Jianguo Xu

doi:10.3389/fonc.2019.01371

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

Front Oncol. 2019 Dec 17:9:1371. doi: 10.3389/fonc.2019.01371. eCollection 2019.

Authors

Yang Zhang^{1

2}, Chaoyue Chen¹, Yangfan Cheng², Yuen Teng², Wen Guo², Hui Xu³, Xuejin Ou², Jian Wang⁴, Hui Li², Xuelei Ma^{5

6}, Jianguo Xu¹

Affiliations

¹ Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
² West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China.
³ Radiology Department, West China Hospital, Sichuan University, Chengdu, China.
⁴ School of Computer Science, Nanjing University of Science and Technology, Nanjing, China.
⁵ Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
⁶ State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, China.

Abstract

Objectives: To investigate the ability of radiomics features from MRI in differentiating anaplastic oligodendroglioma (AO) from atypical low-grade oligodendroglioma using machine-learning algorithms. Methods: A total number of 101 qualified patients (50 participants with AO and 51 with atypical low-grade oligodendroglioma) were enrolled in this retrospective, single-center study. Forty radiomics features of tumor images derived from six matrices were extracted from contrast-enhanced T1-weighted (T1C) images and fluid-attenuation inversion recovery (FLAIR) images. Three selection methods were performed to select the optimal features for classifiers, including distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT). Then three machine-learning classifiers were adopted to generate discriminative models, including linear discriminant analysis, support vector machine, and random forest (RF). Receiver operating characteristic analysis was conducted to evaluate the discriminative performance of each model. Results: Nine predictive models were established based on radiomics features from T1C images and FLAIR images. All of the classifiers represented feasible ability in differentiation, with AUC more than 0.840 when combined with suitable selection method. For models based on T1C images, the combination of LASSO and RF classifier represented the highest AUC of 0.904 in the validation group. For models based on FLAIR images, the combination of GBDT and RF classifier showed the highest AUC of 0.861 in the validation group. Conclusion: Radiomics-based machine-learning approach could potentially serve as a feasible method in distinguishing AO from atypical low-grade oligodendroglioma.

Keywords: anaplastic oligodendroglioma; grading; machine learning; magnetic resonance imaging; oligodendroglioma; radiomics.