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There are no clear indications for the best choice of anti-seizure medications to control brain tumor related epilepsy. In vitro studies have shown an antitumoral effect of Levetiracetam and Lacosamide on glioblastoma IDH-wild type.

Objective: Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively. Methods: The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95). Results: In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs. Conclusions: The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma’s MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences.

Glioblastoma (GBM) is known as an intractable, highly heterogeneous tumor encompassing multiple subclones, each supported by a distinct glioblastoma stem cell (GSC). The contribution of GSC genetic and transcriptional heterogeneity to tumor subclonal properties is debated. In this study, we describe the systematic derivation, propagation, and characterization of multiple distinct GSCs from single, treatment-naive GBMs (GSC families). The tumorigenic potential of each GSC better correlates with its transcriptional profile than its genetic make-up, with classical GSCs being inherently more aggressive and mesenchymal more dependent on exogenous growth factors across multiple GBMs. These GSCs can segregate and recapitulate different histopathological aspects of the same GBM, as shown in a paradigmatic tumor with two histopathologically distinct components, including a conventional GBM and a more aggressive primitive neuronal component. This study provides a resource for investigating how GSCs with distinct genetic and/or phenotypic features contribute to individual GBM heterogeneity and malignant escalation.

High-grade gliomas are aggressive tumors that require multimodal management and gross total resection is considered to be the first crucial step of treatment. Because of their infiltrative nature, intraoperative differentiation of neoplastic tissue from normal parenchyma can be challenging. For these reasons, in the recent years, neurosurgeons have increasingly performed this surgery under the guidance of tissue fluorescence. Sodium fluoresceine and 5-aminolevulinic acid represent the 2 main compounds that allow real-time identification of residual malignant tissue and have been associated with improved gross total resection and radiological outcomes. Though presenting different profiles of sensitivity and specificity and further investigations concerning cost-effectiveness are need, Sodium fluoresceine, 5-aminolevulinic acid and new phluorophores, such as Indocyanine green, represent some of the most important tools in the neurosurgeon’s hands to achieve gross total resection.