Insights on Survival and Recurrence After Surgery in Malignant Meningiomas: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Malignant meningiomas (WHO Grade III) are rare, aggressive tumors with poor prognosis and high recurrence rates. Gross total resection (GTR) is the preferred treatment; however, recurrence remains a challenge, especially after subtotal resection (STR). The role of adjuvant radiotherapy (RT) and chemotherapy in improving patient outcomes remains controversial. This systematic review and meta-analysis assessed the impact of surgical extent, adjuvant therapies, and prognostic factors on survival and recurrence of malignant meningiomas. METHODS: A systematic review and meta-analysis were conducted using the PubMed, Cochrane Library, and Scopus databases. Eligible studies included retrospective and prospective cohorts, case-control studies, and clinical trials reporting the surgical extent (GTR vs. STR), adjuvant therapy, survival, and recurrence. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Cochrane Risk of Bias Tool. Meta-analysis was performed using random- and fixed-effects models and heterogeneity was assessed using the I² statistic. RESULTS: Sixteen studies (2,208 patients) met the inclusion criteria. The 5-year overall survival (OS) ranged from 40% to 90%, with GTR significantly improving survival (HR = 0.54, 95% CI: 0.50-0.58, p < 0.00001) [1]. Recurrence rates were lower in GTR cases (50-90% in STR). Adjuvant RT improved progression-free survival (HR = 0.36, 95% CI: 0.18-0.70) in STR patients, but its benefit post-GTR was unclear. Chemotherapy had no significant effect on patient survival [2]. Key prognostic factors included tumor location, patient age, Ki-67 index, and histology [3, 4]. CONCLUSION: GTR is the strongest predictor of long-term survival, whereas STR requires adjuvant RT for disease control. The role of chemotherapy remains uncertain, necessitating further research into targeted therapies. Standardized treatment protocols and long-term surveillance are essential to improve patient outcomes [5].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".