The Role of Stereotactic Radiosurgery in the Management of Brain Metastases From a Health-Economic Perspective: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Stereotactic radiosurgery (SRS) is an effective option in the management of brain metastases, offering improved overall survival to whole-brain radiation therapy (WBRT). However, given the need for active surveillance and the possibility of repeated interventions for local/distant brain recurrences, the balance between clinical benefit and economic impact must be evaluated. OBJECTIVE: To conduct a systematic review of health-economic analyses of SRS for brain metastases, compared with other existing intervention options, to determine the cost-effectiveness of this treatment across different clinical scenarios. METHODS: The MEDLINE, EMBASE, Cochrane, CRD, and EconLit databases were searched for health-economic analyses, according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, using terms relevant to brain metastases and radiation-based therapies. Simple cost analysis studies were excluded. Quality analysis was based on BMJ Consolidated Health Economics Reporting Standards (CHEERS) checklist. RESULTS: Eleven eligible studies were identified. For lesions with limited mass effect, SRS was more cost-effective than surgical resection (6 studies). In patients with Karnofsky performance scale (KPS) >70 and good predicted survival, SRS was cost-effective compared to WBRT (7 studies); WBRT became cost-effective with poor performance status or low anticipated life span. Following SRS, routine magnetic resonance imaging surveillance saved $1326/patient compared to symptomatic imaging due to reduced surgical salvage and hospital stay (1 study). CONCLUSION: Based on our findings, SRS is cost-effective in the management of brain metastases, particularly in high-functioning patients with longer expected survival. However, before an optimal care pathway can be proposed, emerging factors such as tumor molecular subtype, diagnosis-specific graded prognostic assessment, neuroprognostic score, tailored surveillance imaging, and patient utilities need to be studied in greater detail.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".