RADT-27. FRACTIONATED RADIOTHERAPY AS FIRST-LINE TREATMENT FOR INTRACRANIAL MENINGIOMAS
Bibliographic record
Abstract
Abstract BACKGROUND Meningiomas are the most common primary brain tumour, and patients requiring intervention typically undergo surgical resection as first-line therapy. However, fractionated radiotherapy (fRT) as primary treatment remains an option for patients with larger tumours and surgical contraindications, although outcome prediction in this group remains uncertain. We aimed to assess the clinical factors that contribute to treatment failure in a cohort of meningiomas treated with fRT as first-line therapy. METHODS Patients treated with primary fRT for intracranial meningiomas at our institution from November 1, 1998 to December 30, 2017 were reviewed. Those who received upfront surgical resection, radiosurgery, or had less than 6 months of clinical follow up were excluded. We applied logistic regression and Cox regression analysis to ascertain key predictors of treatment failure, progression-free survival (PFS), and adverse events following radiotherapy. RESULTS Our cohort included 147 tumours, of which 25 progressed after fRT (median PFS of 3.45 years). Tumours that progressed following RT had a larger median gross tumour volume (GTV) than those that did not (32.3cm3 vs 20.2cm3,p = 0.03768). Pre-RT GTV greater than 11.27cm3 was independently predictive of treatment failure. Larger pre-RT GTV was also associated with significant (grade 3/4) adverse events following fRT. Finally, cerebellopontine angle meningiomas were more prevalent in the group that progressed (20% vs 3.3%, p = 0.0046), whereas cavernous sinus and optic nerve sheath meningiomas had longer PFS (p = 0.0067). CONCLUSION We present the largest known cohort of meningiomas treated with fRT as first line therapy and find pre-RT tumour GTV and location to be the critical predictors of outcome, adding to the discussion of treatment considerations in this heterogeneous disease.
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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 | 0.001 |
| 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) | 0.002 | 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".