The modern management of untreated large (>2 cm) brain metastases: a narrative review
Bibliographic record
Abstract
OBJECTIVE: Our objective was to identify contemporary management options for large brain metastases reported in literature, specifically evaluating local control and risk of toxicity. BACKGROUND: Large brain metastases are typically defined as lesions >2 cm in diameter, and historically conferred poor outcomes due to the high rates of radiation necrosis and less local control in comparison to smaller brain metastases. METHODS: A literature search examining modern management of large brain metastases was performed using ovid-MEDLINE. A total of 18 articles met criteria for review, evaluating single fraction radiosurgery [stereotactic radiosurgery (SRS)] and multi-fraction stereotactic radiation therapy (MFSRT) in both the definitive and post-operative cavity setting, as well as targeted therapies. CONCLUSIONS: Multi-fractionated radiosurgery represents a modern and attractive treatment approach in the definitive management of patients with large brain metastases, with equivalent local control and reduced rates of radionecrosis less than 13% in comparison to single fraction SRS. In cases where surgery is indicated, fractionated cavity radiation should be considered for large tumor bed volumes. More research is needed for the optimal dose and fractionation regimen for optimal tumor control with reduced risk of radiation toxicity, but common regimens include 3-5 fractions while meeting appropriate biologically effective dose (BED) goals. Future areas of interest include targeted therapies in the initial management of brain metastases as well as pre-operative radiation therapy to reduce risk of leptomeningeal disease (LMD).
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".