AUTHOR=Sun Kai , Liu Zhenyu , Li Yiming , Wang Lei , Tang Zhenchao , Wang Shuo , Zhou Xuezhi , Shao Lizhi , Sun Caixia , Liu Xing , Jiang Tao , Wang Yinyan , Tian Jie
TITLE=Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images
JOURNAL=Frontiers in Oncology
VOLUME=10
YEAR=2020
URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.01096
DOI=10.3389/fonc.2020.01096
ISSN=2234-943X
ABSTRACT=Purpose: The present study aimed to evaluate the performance of radiomics features in the preoperative prediction of epileptic seizure following surgery in patients with LGG.
Methods: This retrospective study collected 130 patients with LGG. Radiomics features were extracted from the T2-weighted MR images obtained before surgery. Multivariable Cox-regression with two nested leave-one-out cross validation (LOOCV) loops was applied to predict the prognosis, and elastic net was used in each LOOCV loop to select the predictive features. Logistic models were then built with the selected features to predict epileptic seizures at two time points. Student's t-tests were then used to compare the logistic model predicted probabilities of developing epilepsy in the epilepsy and non-epilepsy groups. The t-test was used to identify features that differentiated patients with early-onset epilepsy from their late-onset counterparts.
Results: Seventeen features were selected with the two nested LOOCV loops. The index of concordance (C-index) of the Cox model was 0.683, and the logistic model predicted probabilities of seizure were significantly different between the epilepsy and non-epilepsy groups at each time point. Moreover, one feature was found to be significantly different between the patients with early- or late-onset epilepsy.
Conclusion: A total of 17 radiomics features were correlated with postoperative epileptic seizures in patients with LGG and one feature was a significant predictor of the time of epilepsy onset.