Emergent radiotherapy for brain and leptomeningeal metastases: a narrative review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: As novel systemic therapies allow patients to live longer with cancer, the risk of developing central nervous system (CNS) metastases increases and providers will more frequently encounter emergent presentation of brain metastases (BM) and leptomeningeal metastases (LM). Management of these metastases requires appropriate work-up and well-coordinated multidisciplinary care. We set out to perform a review of emergent radiotherapy (RT) for CNS metastases, specifically focusing on BM and LM. METHODS: We review the appropriate pathways for workup and initial management of BM and LM, while reviewing the literature supporting emergent treatment of these entities with surgery, systemic anti-cancer therapy, and RT. To inform this narrative review, literature searches in PubMed and Google Scholar were conducted, with preference given to articles employing modern RT techniques, when applicable. Due to the paucity of high-quality evidence for management of BM and LM in the emergent setting, discussion was supplemented by the authors' expert commentary. KEY CONTENT AND FINDINGS: This work highlights the importance of surgical evaluation, particularly for patients presenting with significant mass effect, hemorrhagic metastases, or increased intracranial pressure. We review the rare situations where emergent initiation of systemic anti-cancer therapy is indicated. When defining the role of RT, we review factors guiding selection of appropriate modality, treatment volume, and dose-fractionation. Generally, 2D- or 3D-conformal treatment techniques prescribed as 30 Gy in 10 fractions or 20 Gy in 5 fractions, should be employed in the emergent setting. CONCLUSIONS: Patients with BM and LM present from a diverse array of clinical situations, requiring well-coordinated multidisciplinary management, and there is a paucity of high-quality evidence guiding such management decisions. This narrative review aims to more thoroughly prepare providers for the challenging situation of emergent management of BM and LM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".