Can Histopathological Features Predict Outcomes in Grade 2 Meningiomas?
Bibliographic record
Abstract
Background: The WHO Classification of CNS tumors grades meningiomas based on the presence of atypical (grade 2) or anaplastic (grade 3) histological features, or their absence (grade 1). Increasing grade is associated with worse recurrence and survival rates. Grade 2 tumors form a heterogeneous class both in their histological features and prognoses, and it remains unclear whether specific histological features can be used to predict outcomes within this class. Objective: The goal of this study was to determine the prognostic value of atypical histological features currently used for classification of grade 2 meningiomas. Methods: We used multivariate logistic regression and Cox regression to model the effects of each of seven histological features (proliferative index, brain invasion, small cell change, hypercellularity, sheeting, spontaneous necrosis and prominent nucleoli) on recurrence and survival in a large cohort of patients with grade 2 meningiomas, while controlling for the extent of resection, NF2 status, and exposure to adjuvant radiotherapy. Results: We included 189 patients with mean age 57.4 ± 14.6 years, 64% of whom were female. The median follow-up time was 64 (IQR: 20–96) months. There was no correlation between the presence of any atypical histological feature and recurrence or survival at 1 or 5 years, except for the relationship between proliferative index and survival at 1 year (OR = 0.044, 95% CI [0.0016, 0.48], p = 0.021). However, proliferative index was not significantly correlated with recurrence at 1 year (OR = 3.32, 95% CI [0.58–6.3], p = 0.26) or 5 years (OR = 1.98, 95% CI [0.69, 6.22], p = 0.21), or survival at 5 years (OR = 0.43, 95% CI [0.12, 1.66], p = 0.2). Discussion: In a large retrospective cohort of patients with grade 2 meningiomas, we failed to find any relationship between specific histological features and recurrence or survival outcomes. Although histology has allowed us to broadly stratify meningiomas with the WHO grading system, this suggests it should not be used for prognostication of grade 2 tumors and highlights the importance of pursuing other approaches to prognostication. Publication History Article published online: 05 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".