RE-IRRADIATION FOR RECURRENT HIGH-GRADE GLIOMA: AN ANALYSIS OF PROGNOSTIC FACTORS FOR SURVIVAL AND PREDICTORS OF RADIATION NECROSIS
Bibliographic record
Abstract
Abstract Recurrent high-grade glioma (rHGG) is a heterogeneous population, and the ideal patient selection for reirradiation (re-RT) has yet to be established. This study aims to identify prognostic factors for rHGG patients treated with re-RT. METHODS: We retrospectively reviewed consecutive adults with rHGG who underwent re-RT from 2009-2020 from our institutional database. The primary objective was overall survival (OS). The secondary outcomes included prognostic factors for early death (<6 months after re-RT) and predictors of radiation necrosis (RN). RESULTS: For the 79 patients identified, the median OS after re-RT was 9.9 months (95% CI 8.3-11.6). On multivariate analyses (MVA), re-resection at progression (HR=0.56, p=0.027), interval from primary treatment to first progression ≥16.3 months (HR=0.61, p=0.034), interval from primary treatment to re-RT ≥23.9 months (HR=0.35, p<0.001), and re-RT PTV volume <112 cc (HR=0.27, p<0.001) were prognostic for improved OS. Patients who had unmethylated-MGMT tumours (OR=12.4, p=0.034), ≥3 prior systemic treatment lines (OR=29.1, p=0.22), interval to re-RT <23.9 months (OR=9.0, p=0.039), and re-RT PTV volume ≥112 cc (OR=17.8, p=0.003) were more likely to survive <6 months. The cumulative incidence of RN was 11.4% (95% CI 4.3-18.5) at 12 months. Concurrent bevacizumab use (HR<0.001, p<0.001) and cumulative equivalent dose in 2 Gy fractions (cEQD2, a/b=2) <99 Gy2 (HR<0.001, p<0.001) were independent protective factors against RN. CONCLUSIONS: We observe favorable OS rates following re-RT and identified prognostic factors, including methylation status, that can assist in patient selection and clinical trial design. Concurrent use of bevacizumab and cEQD2 <99 Gy2 mitigates the risk of RN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".