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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. more...

Background and Objectives Preoperative embolization of metastatic spinal tumors (MSTs) has proven advantageous in limiting intraoperative blood loss (IBL) during resection. N-butyl cyanoacrylate (nBCA) is a liquid embolic agent known for its rapid hemostatic effects. However, nBCA is associated with a higher risk of distal nontarget embolization. This study highlights the refinement of the embolization technique and assesses its efficacy in performing an initial distal segmental artery plug with concentrated nBCA followed by proximal diluted nBCA for MSTs.Methods A retrospective review of patients with MST (2018–2023) was performed. Patients who underwent preoperative nBCA endovascular embolization prior to tumor resection and spinal instrumentation were included. Baseline standard spinal angiography was performed.Results Sixteen patients (13 men, 3 women; 56.0 ± 12.4 years) met inclusion criteria. And 43.75% (7 of 16) had thoracic levels, 37.5% (6 of 16) lumbar, and 18.75% (3 of 16) sacral. The most common primary tumor was renal cell carcinoma (43.75%, 7 of 16). A total of 43 pedicles were embolized (median 3), resulting in complete/near complete obliteration of the tumor blush. Most pedicles (83.7%, 36 of 43) received a single dilute concentration of nBCA; however, 16.3% (7 of 43) received two separate concentrations of nBCA, a denser concentration distally into the segmental artery and a diluted concentration proximally into the tumor bed. Mean IBL was 1150 ± 1201 mL in 3 distal plug patients distal plug patients versus 1625 ± 681 mL in 12 other patients. There were no complications related to embolization.Conclusion Performing a distal, concentrated nBCA plug during preoperative nBCA embolization of MSTs may increase tumor penetration and reduce IBL. more...

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. more...