Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas - Jie, Bai; Hongxi, Yang; Ankang, Gao; Yida, Wang; Guohua, Zhao; Xiaoyue, Ma; Chenglong, Wang; Haijie, Wang; Xiaonan, Zhang; Guang, Yang; Yong, Zhang; Jingliang, Cheng (Frontiers in Oncology, 2022)
Purpose: To investigate the association between clinic-radiological features and glioma associated epilepsy (GAE), we developed and validated a radiomics nomogram for predicting GAE in WHO grade II IV gliomas. Methods: This retrospective study consecutively enrolled 380 adult patients with glioma (266 in the training cohort and 114 in the testing cohort). Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. The semantic radiological characteristics were assessed by a radiologist with 15 years of experience in neuro-oncology. A clinic-radiological model, radiomic signature and a combined model were built for predicting GAE. The combined model was visualized as a radiomics nomogram. The AUC was used to evaluate model classification performance and the Mc-Nemar test and Delong test were used to compare the performance the among the models. Statistical analysis was performed using SPSS software, and p < 0.05 was regarded as statistically significant. Results: The combined model reached the highest AUC with the testing cohort (training cohort, 0.911 [95% CI, 0.878-0.942]; testing cohort, 0.866 [95% CI, 0.790-0.929]). The Mc-Nemar test revealed that the differences among the accuracies of the clinic-radiological model, radiomic signature and combined model in predicting GAE in the testing cohorts (p>0.05) were not significantly different. The DeLong tests showed that the difference between the performance of the radiomic signature and the combined model was significant (p<0.05). Conclusion: The radiomics nomogram predicted seizures in patients with glioma noninvasively, simply and practically. Compared with the radiomics models, comprehensive clinic-radiological imaging signs observed by the naked eye have nondiscriminatory performance in predicting of GAE.