Clinicopathological, immunohistochemical and therapeutic approaches on survival in patients with epithelioid glioblastoma: Institutional experience in the management of 58 patients.

Epithelioid glioblastoma (Ep-GBM) is a rare variant of glioblastoma characterized by a high recurrence rate and poor prognosis. Currently, there is no established standard treatment for Ep-GBM. Therefore, we identified 58 Ep-GBM cases to investigate these characteristics and identify the possible prognostic factors of survival. There were 30 male and 28 female patients with a median age of 39 years. Headaches and dizziness were the most common clinical symptom. The tumor is most frequently located in the temporal lobe (36.2%). The positivity rate for BRAF-V600E is 56.9% (33/58), for MGMT is 56.9% (33/58), and for INI-1 is 75% (30/40). Tumor recurrence was observed in 39 patients. The median progression-free survival (PFS) of all patients was 12.7 months, while the median overall survival (OS) was 29.1 months. Additionally, the median survival time after recurrence was 14.3 months. Both univariate and multivariate COX regression analyses revealed that individuals who received more than six cycles of adjuvant oral temozolomide experienced a longer median PFS compared to those who received fewer cycles. Characteristics associated with poorer PFS included tumor dissemination prior to initial surgery. Additionally, both analyses identified tumor dissemination, radiotherapy and adjuvant oral temozolomide as predictors of OS. Notably, for patients with recurrent Ep-GBM, reoperation was shown to significantly increase survival time after recurrence. In conclusion, the standard Stupp regimen is also applicable to patients with Ep-GBM, extending adjuvant oral temozolomide could further improve survival for Ep-GBM patients, reoperation may also prolong survival for recurrent Ep-GBM.

Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features.

Objectives: To explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features. Methods: Data of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images. Data were randomly divided into training (70%) and test cohorts (30%) and normalized and standardized using Z-scores. Feature dimensionality reduction was performed using the variance method and maximum relevance and minimum redundancy (mRMR) algorithm. We used the logistic regression algorithm to construct three models for T2w, T2 FLAIR, and T1c images as well as one combined model. The test cohort was used to evaluate the models, and receiver operating characteristic (ROC) curves, areas under the curve (AUCs), sensitivity, specificity, and accuracy were calculated. The nomogram of the combined model was built and evaluated using a calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical application value of the four models. Results: A total of 1,316 features were extracted from T2w, T2 FLAIR, and T1c images, respectively. And then the best non-redundant features were selected from the extracted features using the variance method and mRMR. Finally, five features were extracted each from T2w, T2 FLAIR, and T1c images, and 12 features were extracted for the combined model. Four models were established using the optimal features. In the test cohort, the combined model performed the best out of all models. The AUCs of the T2w, T2 FLAIR, T1c, and combined models were 0.73, 0.78, 0.74, and 0.87, respectively, and accuracies were 0.72, 0.76, 0.72, and 0.84, respectively. The ROC curves and DCA showed that the combined model had the highest efficiency and most favorable clinical benefits. Conclusion: The combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.