CyberKnife Radiosurgery in Recurrent Brain Metastases: Do the Benefits Outweigh the Risks?
Bibliographic record
Abstract
Introduction Local treatment concepts are in high demand in the salvage treatment of recurrent brain metastases. Still, their risks and benefits are scarcely characterized. In this study, we analyzed the outcome and risk-/benefit-ratio of salvage CyberKnife (Accuray Incorporated, Sunnyvale, California, US) radiosurgery in the treatment of recurrent brain metastases after whole brain radiotherapy (WBRT). Materials and methods Seventy-six patients with 166 recurrent brain metastases and a multimodal pretreatment were retrospectively investigated. All patients underwent salvage CyberKnife radiosurgery (single fraction, reference dose: 17-22 Gy). Study endpoints were post-recurrence survival (PRS) after salvage treatment as well as local and distant tumor control rates. Central nervous system (CNS) toxicity was assessed according to the toxicity criteria of the Radiation Therapy Oncology Group and the European Organization for Research and Treatment of Cancer (RTOG/EORTC)). Results The population was homogenous regarding its demographic parameters. All patients had a history of WBRT prior to salvage CyberKnife radiosurgery. PRS was 13.3 months (10.4 - 16.2 months), one-year local and distant tumor control rates were 87% (95% CI: 75-99) and 38% (95% CI: 23-52), respectively. Eighteen patients suffered from RTOG/EORTC grade I/II toxicity. No toxicity-related risk factors were identified. Discussion This study found indicative survival and tumor control rates as well as a favorable risk/benefit ratio regarding radiotoxicity in salvage CyberKnife radiosurgery. These results point to a proactive therapeutic strategy based on appropriate patient selection instead of therapeutic nihilism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".