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.

Gliomas represent the most common primary Central Nervous System (CNS) tumors, characterized by increased heterogeneity, dysregulated intracellular signaling, extremely invasive properties, and a dismal prognosis. They are generally resistant to existing therapies and only a few molecular targeting options are currently available. In search of signal transduction pathways with a potential impact in glioma growth and immunotherapy, the Slit guidance ligands (Slits) and their Roundabout (Robo) family of receptors have been revealed as key regulators of tumor cells and their microenvironment. Recent evidence indicates the implication of the Slit/Robo signaling pathway in inflammation, cell migration, angiogenesis, and immune cell infiltration of gliomas, suppressing or promoting the expression of pivotal proteins, such as cell adhesion molecules, matrix metalloproteinases, interleukins, angiogenic growth factors, and immune checkpoints. Herein, we discuss recent data on the significant implication of the Slit/Robo signaling pathway in glioma pathology along with the respective targeting options, including immunotherapy, monoclonal antibody therapy, and protein expression modifiers.

Background/Objectives: Apathy is among the most common neuropsychiatric features of late-life neurocognitive disorders and predicts functional decline and greater caregiver burden. As no treatment is formally established, identifying effective interventions is a priority. We systematically reviewed non-pharmacological randomized controlled trials (RCTs) targeting apathy in older adults with neurocognitive disorders. Methods: We searched PubMed/MEDLINE, PsycInfo, the Cochrane Library, and Google Scholar (final search 23 March 2026). Eligible studies were non-pharmacological RCTs reporting an apathy outcome. Evidence levels were graded with OCEBM and quality with PEDro; two reviewers mapped PEDro items onto Cochrane risk-of-bias domains. Reporting followed PRISMA 2020. Results: Sixty-two RCTs were included. Physical exercise and music-based interventions showed the most consistent benefit, whereas technology-based and brain stimulation approaches remained experimental. Only 30 trials (48%) showed a significant between-group effect on apathy—most were null, within-group, or had apathy as a secondary outcome. Marked heterogeneity precluded meta-analysis. Most trials were of moderate to high quality, though near-universal performance bias arose from the inability to blind participants and providers. Conclusions: Managing apathy in these populations remains challenging, and the certainty of the evidence is limited. Purpose-built, apathy-focused trials reporting effect sizes and durability are needed before disease-specific recommendations can be made.

BACKGROUND AND OBJECTIVES: Lesions of the cerebellar peduncles (CPs), particularly the mesial CP, pose a surgical challenge because of their deep location and the risk of disrupting critical neural pathways. Given their low incidence, the safest and most anatomically sound surgical approach for this region remains to be determined. This study analyzes the surgical anatomy and technique of the telovelar approach with particular focus on the white matter tract anatomy of the middle (MCP), inferior (ICP), and superior CPs. By integrating cadaveric studies, tractographic anatomy and surgical cases, we provide an in-depth anatomic framework that highlights white matter preservation and its implications for patient outcomes. METHODS: We analyzed the white matter fiber architecture of the CPs using 3 formalin-fixed adult human brains treated with Klinger's technique, a single formalin-fixed, color-latex-injected cadaveric head, and diffusion tensor imaging-based tractography from a healthy male volunteer. Stepwise cadaveric dissections and tractographic reconstructions were performed to delineate fiber tract trajectories relevant to the telovelar approach. In addition, 3 illustrative clinical cases were included to correlate anatomic findings with surgical considerations. RESULTS: Cadaveric white matter, cranial dissections, tractography, and intraoperative images were synthesized to provide a structured stepwise guide for the telovelar approach. The resultant distinct anatomic trajectories of MCP, ICP, and superior CP fibers, underscore the differential impact of surgical approaches on white matter integrity. Compared with the retrosigmoid approach, which risks disrupting lateral MCP fibers, the telovelar route preserves these pathways while primarily interacting with ICP fibers, potentially mitigating postoperative motor deficits. CONCLUSION: By integrating white matter fiber dissection and tractography, this study refines the anatomic basis of the telovelar approach for mesial cerebellar peduncle lesions and its impact on critical cerebellar pathways. Understanding these three-dimensional white matter relationships enhances intraoperative decision-making, optimizes surgical strategies and advances the neurosurgical field toward functionally informed, minimally disruptive interventions.