Recurrent brain tumors—including high-grade gliomas, brain metastases, and aggressive meningiomas—continue to carry a poor prognosis, with high mortality despite therapeutic advances. The aim of this narrative review is to summarize and critically discuss the current evidence on the role of intra-arterial radioligand therapy (RLT) in the treatment of recurrent brain tumors. RLT, a targeted form of radionuclide therapy, has gained increasing attention for its potential theranostic applications in neuro-oncology. A literature search was conducted using PubMed and Scopus, including clinical studies evaluating intra-arterial radioligand delivery in central nervous system tumors. Recent research has explored intra-arterial administration of radioligands targeting somatostatin receptors and prostate-specific membrane antigen (PSMA). Somatostatin receptors are overexpressed in meningiomas, while PSMA is highly expressed in the neovasculature of glioblastomas and brain metastases; both targets can be addressed using lutetium-177 (¹⁷⁷Lu)- or actinium-225 (²²⁵Ac)-labeled radiopharmaceuticals, traditionally delivered intravenously. Available evidence indicates that the intra-arterial route achieves markedly higher radionuclide uptake on ⁶⁸Ga-PSMA-11 and ⁶⁸Ga-DOTATOC PET, as well as increased absorbed doses in dosimetric models. Dosimetric analyses consistently show greater tracer accumulation compared with intravenous administration, without evidence of significant peri-procedural toxicity. Uptake in healthy brain tissue is minimal, and no relevant differences have been reported in liver or salivary gland accumulation between intra-arterial and intravenous RLT. Although based on heterogeneous and limited data, intra-arterial RLT appears to be a promising therapeutic strategy for recurrent brain tumors. Future research should focus on improving radioligand delivery beyond the blood–brain barrier and enhancing effective tumor targeting.

Acute ischemic stroke caused by intracranial large vessel occlusion is associated with significant morbidity and mortality. Emerging evidence suggests that environmental exposures, including toxic metals, may be associated with cerebrovascular disease. However, the presence of toxic metals within intracranial thrombi and their relationship with clinical features of acute ischemic stroke remain largely unexplored. We conducted an observational cohort study including 59 patients with acute ischemic stroke due to large vessel occlusion treated with mechanical thrombectomy between November 2023 and April 2024. Retrieved thrombi were analyzed for aluminum, cadmium, nickel, and lead concentrations using Inductively Coupled Plasma-Mass Spectrometry. Histological and immunohistochemical analyses were performed to characterize thrombus composition. Associations between thrombus metal concentrations and clinical, radiological, and procedural variables, including NIHSS scores and first-pass recanalization, were assessed. Significant correlations were observed between metals: aluminum with cadmium (r = 0.733, P < 0.001) and nickel (r = 0.558, P = 0.001). Cadmium was positively associated with first-pass recanalization (FPR) success (r = 0.648, P < 0.001), while lead correlated with National Institutes of Health Stroke Scale (NIHSS) at onset (r = 0.502, P < 0.001). In 29% of thrombi containing all 4 metals, aluminum was inversely related to NIHSS at 12 hours (r = -0.775, P < 0.001). This study identifies significant associations between thrombus toxic metal concentrations and clinical outcomes, suggesting that thrombus metal composition may be relevant to acute ischemic stroke characteristics.

Large Vessel Occlusions (LVOs) in the M1 segment are among the most frequent and severe causes of Acute Ischemic Stroke (AIS). In this study, we investigate the use of Artificial Intelligence (AI) to detect these occlusions through Deep Neural Networks (DNN). Although AI-assisted segmentation shows considerable promise in medical imaging, the trade-offs between functional and non-functional performance metrics, particularly in clinical and emergency settings, remain insufficiently explored. To address this gap, we adopt an interdisciplinary approach that integrates medical and engineering perspectives to detect and characterize LVOs in the M1 segment of the middle cerebral artery (MCA). We train a state-of-the-art nnU-Net model on a recent NVIDIA GH200 system using late-phase CT angiography (CTA) images from a retrospective cohort of 198 patients. Our results indicate that segmentation performance is influenced by anatomical factors such as collateral circulation and thrombus location, and predictive quality is not strictly dependent on the specific DNN architecture. The model achieves a sensitivity of 0.93 in identifying the thrombus–vessel interface. Furthermore, analysis of non-functional metrics demonstrates that inference costs can be limited to approximately €0.08 per 1000 patients. These findings support the deployment of GPU-based systems in on-premise hospital environments, providing neuroradiologists with an effective assistive and decision-support tool for the detection of LVOs and the planning of surgical interventions in AIS.