Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas.

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 McNemar test and Delong test were used to compare the performance 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 McNemar 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 non-invasively, simply, and practically. Compared with the radiomics models, comprehensive clinic-radiological imaging signs observed by the naked eye have non-discriminatory performance in predicting GAE.

ADC and T2WI in differentiating skull base chordoma and invasive pituitary adenomas / 实用放射学杂志

Objective To investigate the value of ADC and T2WI in differentiating of skull base chordoma and invasive pituitary adenomas(IPA).Methods 15 patients with skull base chordoma and 19 patients with IPA which involve paranasal sinus were reviewed retrospectively.All diagnosis were demonstrated by pathology.Quantitative analysis of minimum ADC, normal ADC and rT2WI values were performed.Differences in minimum ADC, normal ADC and rT2WI values between skull base chordoma and IPA were evaluated using the independent samples t test and receiver operating curves(ROC).Results Statistical analysis revealed a significant difference among normal ADC, minimum ADC and rT2WI values (P<0.01),and the area under the ROC curves decreased in turn.Conclusion Both ADC values and rT2WI SI are effective parameter for differentiating diagnosis of skull base chordoma and IPA.