MLTI-06. Surgical resection plus stereotactic radiosurgery versus SRS alone for large brain metastases: a comparative study
Bibliographic record
Abstract
Abstract Purpose Large brain metastases (BRM) are challenging to manage. Therapeutic options include Stereotactic Radiosurgery (SRS) or surgery (S) with adjuvant SRS. We sought to compare overall survival (OS), radionecrosis (RN), local failure (LF), pachymeningeal (PMD) and leptomeningeal (LMD) disease in patients treated with SRS vs. S+SRS. Methods We reviewed a prospective registry database from 2009 to 2020 and identified all patients with BRM (≥4cc in volume) treated with SRS or S+SRS. WBRT or SRS re-targeting the index lesion were censoring events. Survival percentages were calculated using the Kaplan-Meier method. Differences between groups were tested using the Cox proportional hazards model. Results 383 patients were identified, 128 and 255 were treated with S+SRS and SRS, respectively. Median ages in the S+SRS and SRS groups were 62.2 (23.6–98.5) and 60.2 (20.2–97.4) (P 0.33). OS at 12 and 24 months was 69% and 41% vs 55% and 20% for the S+SRS and SRS groups, respectively hazard ratio (HR) 1.64 (1.23–2.18) (P<0.001). LF requiring salvage surgery at 12 and 24 months were 3% and 5% vs 8% and 10% for S+SRS and SRS groups, respectively (P 0.067). RN at 12 and 24 months were 9% and 17% vs 15% and 21% for S+SRS and SRS groups, respectively 1.32 HR (0.77–2.29) (P =0.32). PMD disease at 12 and 24 months were 16% and 21% vs 3% and 7% for S+SRS and SRS groups, respectively HR 0.26(0.12–0.56) (P < 0.001). LMD at 12 and 24 months were 4% and 6% vs 2% and 4% for S+SRS and SRS groups, respectively HR 0.73(0.25–2.17) (P 0.57). Conclusion Surgical resection plus SRS correlated with improved OS and a trend towards a decreased incidence of LF compared to SRS alone. However, patients treated with S experienced an increased incidence of PMD.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.002 |
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".