Chronic subdural hematoma (cSDH) is one of the most common neurosurgical entities, especially in the elderly population. Diagnosis is usually established via a head computed tomography, while an increasing number of studies are investigating biomarkers to predict the natural history of cSDH, including progression and recurrence. Surgical evacuation remains the mainstay of treatment in the overwhelming majority of cases. Nevertheless, many controversies are associated with the nuances of surgical treatment. We performed a systematic review of the literature between 2010 and 2022, aiming to identify and address the issues in cSDH surgical management where consensus is lacking. The results show ambiguous data in regard to indication, the timing and type of surgery, the duration of drainage, concomitant membranectomy and the need for embolization of the middle meningeal artery. Other aspects of surgical treatment—such as the use of drainage and its location and number of burr holes—seem to have been adequately clarified: the drainage of hematoma is strongly recommended and the outcome is considered as independent of drainage location or the number of burr holes.

Glioblastoma (GB) is the most common and most aggressive primary brain tumor in adults, with an overall survival almost 14.6 months. Optimal resection followed by combined temozolomide chemotherapy and radiotherapy, also known as Stupp protocol, remains the standard of treatment; nevertheless, resistance to temozolomide, which can be obtained throughout many molecular pathways, is still an unsurpassed obstacle. Several factors influence the efficacy of temozolomide, including the involvement of other DNA repair systems, aberrant signaling pathways, autophagy, epigenetic modifications, microRNAs, and extracellular vesicle production. The blood–brain barrier, which serves as both a physical and biochemical obstacle, the tumor microenvironment’s pro-cancerogenic and immunosuppressive nature, and tumor-specific characteristics such as volume and antigen expression, are the subject of ongoing investigation. In this review, preclinical and clinical data about temozolomide resistance acquisition and possible ways to overcome chemoresistance, or to treat gliomas without restoration of chemosensitinity, are evaluated and presented. The objective is to offer a thorough examination of the clinically significant molecular mechanisms and their intricate interrelationships, with the aim of enhancing understanding to combat resistance to TMZ more effectively.

Background: Chronic subdural hematoma (cSDH) is a common neurosurgical condition, particularly among elderly patients. Middle meningeal artery (MMA) embolization has emerged as a minimally invasive adjunctive treatment aimed at reducing recurrence. However, its comparative efficacy and safety remain under investigation. Methods: In this systematic review and meta-analysis, randomized-controlled clinical trial (RCT) data evaluating MMA embolization combined with best medical therapy (BMT) versus BMT alone in adult patients with symptomatic cSDH were pooled. The primary efficacy outcome was recurrence or progression of hematoma at follow-up. Secondary efficacy outcomes included good functional outcome [modified Rankin Scale (mRS) score ≤ 2], independent ambulation (mRS score ≤ 3), and hematoma thickness at follow-up. The primary safety outcome was all-cause mortality. Procedure-related complications were assessed as a secondary safety outcome. Results: Six RCTs were included, comprising 760 patients treated with MMA embolization and 788 patients treated with BMT alone. MMA embolization significantly reduced recurrence compared to BMT alone (RR: 0.50; 95% CI: 0.37–0.69; six studies; I² = 0%; number-needed-to-treat = 13). No significant differences were observed in good functional outcome (RR: 1.01; 95% CI: 0.97–1.05; three studies; I² = 0%), independent ambulation (RR: 1.01; 95% CI: 0.99–1.04; three studies; I² = 0%), or hematoma thickness at follow-up (SMD: −0.1; 95% CI: −0.3 to 0; four studies; I² = 42%). All-cause mortality was similar between the two groups (RR: 1.01; 95% CI: 0.42–2.40; five studies; I² = 44%). The pooled rate of procedure-related adverse events in the MMA embolization-group was 1% (95% CI: 0–3%; two studies; I² = 35%). Conclusions: MMA embolization significantly reduced cSDH recurrence when used as an adjunct to BMT. However, it did not demonstrate a significant impact on functional outcomes or mortality in this meta-analysis. Further research is needed to identify patient subgroups that benefit most from MMA embolization and to evaluate its impact on cognitive function and quality of life using longer follow-up periods.

Brain gliomas are highly infiltrative and heterogenous tumors, whose early and accurate detection as well as therapeutic management are challenging. Artificial intelligence (AI) has the potential to redefine the landscape in neuro-oncology and can enhance glioma detection, imaging segmentation, and non-invasive molecular characterization better than conventional diagnostic modalities through deep learning-driven radiomics and radiogenomics. AI algorithms have been shown to predict genotypic and phenotypic glioma traits with remarkable accuracy and facilitate patient-tailored therapeutic decision-making. Such algorithms can be incorporated into surgical planning to optimize resection extent while preserving eloquent cortical structures through preoperative imaging fusion and intraoperative augmented reality-assisted navigation. Beyond resection, AI may assist in radiotherapy dose distribution optimization, thus ensuring maximal tumor control while minimizing surrounding tissue collateral damage. AI-guided molecular profiling and treatment response prediction models can facilitate individualized chemotherapy regimen tailoring, especially for glioblastomas with MGMT promoter methylation. Applications in immunotherapy are emerging, and research is focusing on AI to identify tumor microenvironment signatures predictive of immune checkpoint inhibition responsiveness. AI-integrated prognostic models incorporating radiomic, histopathologic, and clinical variables can additionally improve survival stratification and recurrence risk prediction remarkably, to refine follow-up strategies in high-risk patients. However, data heterogeneity, algorithmic transparency concerns, and regulatory challenges hamstring AI implementation in neuro-oncology despite its transformative potential. It is therefore imperative for clinical translation to develop interpretable AI frameworks, integrate multimodal datasets, and robustly validate externally. Future research should prioritize the creation of generalizable AI models, combine larger and more diverse datasets, and integrate multimodal imaging and molecular data to overcome these obstacles and revolutionize AI-assisted patient-specific glioma management.

Brain metastases (BMs) are the most common intracranial tumors in adults. Their heterogeneity, potential multifocality, and complex biomolecular behavior pose significant diagnostic and therapeutic challenges. Artificial intelligence (AI) has the potential to revolutionize BM diagnosis by facilitating early lesion detection, precise imaging segmentation, and non-invasive molecular characterization. Machine learning (ML) and deep learning (DL) models have shown promising results in differentiating BMs from other intracranial tumors with similar imaging characteristics—such as gliomas and primary central nervous system lymphomas (PCNSLs)—and predicting tumor features (e.g., genetic mutations) that can guide individualized and targeted therapies. Intraoperatively, AI-driven systems can enable optimal tumor resection by integrating functional brain maps into preoperative imaging, thus facilitating the identification and safeguarding of eloquent brain regions through augmented reality (AR)-assisted neuronavigation. Even postoperatively, AI can be instrumental for radiotherapy planning personalization through the optimization of dose distribution, maximizing disease control while minimizing adjacent healthy tissue damage. Applications in systemic chemo- and immunotherapy include predictive insights into treatment responses; AI can analyze genomic and radiomic features to facilitate the selection of the most suitable, patient-specific treatment regimen, especially for those whose disease demonstrates specific genetic profiles such as epidermal growth factor receptor mutations (e.g., EGFR, HER2). Moreover, AI-based prognostic models can significantly ameliorate survival and recurrence risk prediction, further contributing to follow-up strategy personalization. Despite these advancements and the promising landscape, multiple challenges—including data availability and variability, decision-making interpretability, and ethical, legal, and regulatory concerns—limit the broader implementation of AI into the everyday clinical management of BMs. Future endeavors should thus prioritize the development of generalized AI models, the combination of large and diverse datasets, and the integration of clinical and molecular data into imaging, in an effort to maximally enhance the clinical application of AI in BM care and optimize patient outcomes.

This study aimed to investigate the association between serum iron (SI) and postoperative delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH). We retrospectively analyzed 985 consecutive adult patients diagnosed with aSAH. Demographic, clinical, and laboratory data were recorded. Univariate and multivariate analyses were employed to assess the association between SI and DCI. Propensity-score matching (PSM) analysis was implemented to reduce confounding. Postoperative DCI developed in 14.38% of patients. Lower SI upon admission was detected in aSAH patients with severe clinical conditions and severe aSAH. SI was negatively correlated with WFNS grade (r = −0.3744, p < 0.001) and modified Fisher (mFisher) grade (r = −0.2520, p < 0.001). Multivariable analysis revealed lower SI was independently associated with DCI [odds ratios (OR) 0.281, 95% confidence interval (CI) 0.177–0.448, p < 0.001], while WFNS grade and mFisher grade were not. The receiver-operating characteristics (ROC) curve analysis of SI for DCI gave an area under the curve (AUC) of 0.7 and an optimal cut-off of 7.5 μmol/L (95% CI 0.665 to 0.733, p < 0.0001). PSM demonstrated the DCI group had a significantly lower SI than the non-DCI group (10.91 ± 6.86 vs. 20.34 ± 8.01 μmol/L, p < 0.001). Lower SI remained a significant independent predictor for DCI and an independent poor prognostic factor of aSAH in multivariate analysis (OR 0.363, 95% CI 0.209–0.630, p < 0.001). The predictive performance of SI for poor outcome had a corresponding AUC of 0.718 after PSM. Lower SI upon admission is significantly associated with WFNS grade, mFisher grade, and predicts postoperative DCI and poor outcome at 90 days following aSAH.