Outcomes and predictors of response to fractionated radiotherapy as primary treatment for intracranial meningiomas
Bibliographic record
Abstract
Background: Surgery is the primary treatment for most meningiomas. However, primary fractionated radiotherapy (fRT) remains an option for patients with larger meningiomas in challenging anatomic locations or patients at prohibitively high surgical risk. Outcome prediction for these patients is uncertain and cannot be guided by histopathology without available tumor tissue from surgery. Therefore, we aimed to assess the clinical factors that contribute to treatment failure in a large cohort of meningiomas consecutively treated with fRT as primary therapy, with the goal of identifying predictors of response. Methods: Patients treated with primary fRT for intracranial meningiomas from 1998 to 2017 were reviewed. Those who received primary surgical resection, radiosurgery, previous fRT, or had <6 months of clinical follow-up were excluded. We applied logistic regression and Cox regression modeling to ascertain key predictors of treatment failure, progression-free survival (PFS), and adverse events (AE) following fRT. Results: was independently predictive of progression and larger GTV was associated with higher risk of significant (grades 3/4) AE following fRT. Cavernous sinus and optic nerve sheath meningiomas had overall excellent outcomes post-fRT. Conclusions: We present a large cohort of meningiomas treated with primary fRT and find GTV and anatomic location to be key predictors of outcome, adding to the complex treatment considerations for this heterogeneous disease.
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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 | 0.000 |
| 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) | 0.000 | 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".