A comprehensive review on the current applications and future perspectives of radioembolization in endovascular neurosurgery
Bibliographic record
Abstract
In recent years, there has been advancement in the management of primary and secondary brain tumors within a multidisciplinary framework. However, substantial challenges persist in optimizing treatment strategies, enhancing survival rates, and improving patient prognosis and quality of life. These challenges stem from the complex interplay of biological processes that drive tumor pathogenesis and encompass factors such as tumor heterogeneity and patient variability together with surgical access and resectability in certain brain areas. Current therapeutic modalities – surgical resection, radiotherapy, and systemic pharmacotherapy – all have their own inherent limitations. However, promising alternative treatments are seen in emerging techniques such as endovascular radiosurgery, specifically ones that use intra-arterial delivery of radioactive Yttrium-90 ( 90 Y) microspheres. This approach is currently used in hepatocellular carcinoma (HCC) treatment, which allows for the precise and targeted delivery of radiation to the tumor while minimizing systemic toxicity. Advances in imaging modalities such as MRI and single-photon emission computerized tomography (SPECT)/CT facilitate accurate dosimetry planning and ensure optimal therapeutic outcomes. This review provides the first comprehensive synthesis of the rationale, technical considerations, and translational potential of 90Y-based endovascular radiosurgery in neuro-oncology. While intra-arterial therapies and radiotherapy are established in other fields, their convergence for intracranial applications—particularly in bypassing the blood-brain barrier (BBB) and achieving localized brachytherapy—remains in its early preclinical and clinical stages. We highlight specific central nervous system (CNS) use cases, preclinical findings, and procedural adaptations needed for this modality’s advancement in brain tumor care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".