Predictors of leptomeningeal disease following hypofractionated stereotactic radiotherapy for intact and resected brain metastases
Bibliographic record
Abstract
BACKGROUND: The objective was to evaluate the risk and predictors of developing leptomeningeal disease (LMD) in patients with brain metastases treated with 5-fraction hypofractionated stereotactic radiotherapy (HSRT). METHODS: Patients treated with HSRT for intact brain metastases and/or surgical cavities were reviewed from a prospectively maintained database. Radiographic patterns of LMD were classified as focal classical, diffuse classical, focal nodular, and diffuse nodular. RESULTS: HSRT was delivered, most commonly 30 Gy in 5 fractions, to 320 intracranial lesions (57% intact and 43% surgical cavities) in 235 patients. The median follow-up was 13.4 months (range, 0.8 to 60 mo). LMD developed in 19% of patients with a 1-year LMD rate of 12%. From the diagnosis of LMD, the median overall survival (OS) was 3.8 months (range, 2-20.8 mo). The most common LMD pattern was diffuse nodular (44%). No difference in OS was observed between LMD patterns (P = 0.203). Multivariable analysis identified surgical cavities at significantly higher risk of LMD compared with intact lesions (odds ratio [OR] = 2.30, 95% CI: 1.24, 4.29, P = 0.008). For cavities, radiosensitive tumors (OR = 2.35, 95% CI: 1.04, 5.35, P = 0.041) predicted for LMD, while, for intact metastases, patients receiving treatment with targeted agents or immunotherapy (TA/I) were at lower risk (OR = 0.178, 95% CI: 0.04, 0.79, P = 0.023). CONCLUSIONS: Patients who had a brain metastasis resected were at an increased risk of LMD. OS was poor despite treatment of LMD, and no differences in OS based on the pattern of LMD was observed. Treatment with TA/I was observed to be protective against LMD and requires further study.
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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 | 0.000 |
| 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".