Anatomical and surgical characteristics correlate with pachymeningeal failure in patients with brain metastases after neurosurgical resection and adjuvant stereotactic radiosurgery
Bibliographic record
Abstract
Abstract Purpose Neurosurgery (NS) is an essential modality for large brain metastases (BM). Postoperative stereotactic radiosurgery (SRS) is the standard of care adjuvant treatment. Pachymeningeal failure (PMF) is a newly described entity, distinct from classical leptomeningeal failure (LMF), that is uniquely observed in postoperative patients treated with adjuvant SRS. We sought to identify risk factors for PMF in patients treated with NS + SRS. Methods From a prospective registry (2009 to 2021), we identified all patients treated with NS + SRS. Clinical, imaging, pathological, and treatment factors were analyzed. PMF incidence was evaluated using a competing risks model. Results 144 Patients were identified. The median age was 62 (23–90). PMF occurred in 21.5% (31/144). Female gender [Hazard Ratio (HR) 2.65, p = 0.013], higher Graded Prognostic Assessment (GPA) index (HR 2.4, p < 0.001), absence of prior radiation therapy (HR N/A, p = 0.018), controlled extracranial disease (CED) (HR 3.46, p = 0.0038), and pia/dura contact (PDC) (HR 3.30, p = 0.0053) were associated with increased risk for PMF on univariate analysis. In patients with PDC, wider target volumes correlated with reduced risk of PMF. Multivariate analysis indicated PDC (HR 3.51, p = 0.0053), piecemeal resection (HR 2.38, p = 0.027), and CED (HR 3.97, p = 0.0016) independently correlated with PMF risk. PMF correlated with reduced OS (HR 2.90, p < 0.001) at a lower rate compared to LMF (HR 10.15, p < 0.001). Conclusion PMF correlates with tumor PDC and piecemeal resection in patients treated with NS + SRS. For unclear reasons, it is also associated with CED. In tumors with PDC, wider dural radiotherapy coverage was associated with a lower risk of PMF.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".