NCOG-49. COMBINED NECROSIS AND BRAIN INVASION PREDICT RADIO-RESISTANCE AND TUMOR RECURRENCE IN ATYPICAL MENINGIOMA
Bibliographic record
Abstract
Abstract BACKGROUND Meningiomas are the most common tumors in the central nervous system, with variable recurrence rates depending on WHO grading. Atypical (WHO grade II) meningioma has a higher recurrence rate than benign meningioma (WHO grade I). The efficacy of adjuvant radiotherapy (RT) to improve tumor control has been questioned. METHODS This cohort study retrospectively reviewed all patients at St. Michaels Hospital Tumor Patient Database with a diagnosis of atypical meningioma (AM) who underwent surgical resection between 1995 and 2015. Patient and tumor characteristics such as location, neuropathological diagnosis, resection extent, RT and time to reoccurrence or progression were recorded. Resection extent was defined by Simpson Grading Scale as either Gross Total (GTR) or Subtotal (STR) resection. Cox univariate regression and Kaplan-Meier survival analysis were employed to identify risk factors for recurrence and radio-resistance. RESULTS In our cohort of 181 patients, the combination of necrosis and brain invasion was associated with an increased reoccurrence risk (HR= 4.560, P=0.001) and the lowest progression-free survival relative to other histopathological predictors. This trend was maintained after GTR (P=0.001), whereas necrosis alone and combined necrosis and brain invasion were both associated with low progression-free survival in STR (P=0.001). Radiotherapy was associated with decreased progression-free survival (P=0.001), especially in patients who received GTR (P=0.001). This trend was maintained in patients with necrosis alone (P=0.002) but especially in patients with combined necrosis and brain invasion (P=0.001). CONCLUSION Combined necrosis and brain invasion is a strong predictor of tumor recurrence and radio-resistance in AM, regardless of extent of resection or RT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".