Necrosis and Brain Invasion Predict Radio-Resistance and Tumor Recurrence in Atypical Meningioma: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Meningiomas are the most common tumors occurring in the central nervous system, with variable recurrence rates depending on World Health Organization grading. Atypical (Grade II) meningioma has a higher rate of recurrence than benign (Grade I) meningioma. The efficacy of adjuvant radiotherapy (RT) to improve tumor control has been questioned. OBJECTIVE: To investigate clinical and histopathological predictors of tumor recurrence and radio-resistance in atypical meningiomas. METHODS: This cohort study retrospectively reviewed all patients in St. Michael's Hospital CNS tumor patient database who underwent surgical resection of a Grade II meningioma from 1995 to 2015. Cases with neurofibromatosis type II, multiple satellite tumors, spinal cord meningioma, radiation-induced meningioma, and perioperative death were excluded. Patient demographics, neuropathological diagnosis, tumor location, extent of resection, radiation therapy, and time to recurrence or progression were recorded. Cox univariate regression and Kaplan-Meier survival analysis were employed to identify risk factors for recurrence and radio-resistance. RESULTS: Among 181 patients, the combination of necrosis and brain invasion was associated with an increased recurrence risk (hazard ratio [HR] = 4.560, P = .001) and the lowest progression-free survival (PFS) relative to other pathological predictors. This trend was maintained after gross total resection (GTR, P = .001). RT was associated with decreased PFS (P = .001), even in patients who received GTR (P = .001). CONCLUSION: The combination of necrosis and brain invasion is a strong predictor of tumor recurrence and radio-resistance in meningioma, regardless of EOR or adjuvant RT. Our findings question the sensibility of brain invasion as an absolute criterion for Grade II status.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".