Bevacizumab for Cerebral Radionecrosis: A Single-Center Experience
Bibliographic record
Abstract
ABSTRACT: Background: Cerebral radionecrosis, a subacute or late effect of radiotherapy, can be debilitating and difficult to treat. Steroids can reduce symptoms, but have significant long-term side effects. Bevacizumab has been shown to reduce edema and other radiologic features associated with radionecrosis and improve patient symptoms. We report our experience using bevacizumab for cerebral radionecrosis. Methods: We retrospectively reviewed the charts of all patients treated at our institution with bevacizumab for non-glioma-associated cerebral radionecrosis. We recorded change in symptoms, change in steroids, change in performance status, time to tumor progression, and time to death. We delineated the volume of necrosis pre- and post-bevacizumab on T1-post-gadolinium and fluid-attenuated inversion recovery (FLAIR) MRI scans. Results: We identified 15 patients, 8 with brain metastases, 6 with meningioma, and 1 with nasopharyngeal carcinoma. Most received four doses of bevacizumab, 7.5 mg/kg q 3 weeks × 4 doses. Neuroimaging demonstrated a reduced T1 gadolinium-enhancing volume and edema in 14/15 patients (the average reduction in T1-post-gadolinium volume was 3.0 cm3, and average reduction in FLAIR volume was 27.9 cm3). There was no appreciable change in patient performance status. Steroid doses decreased in five of nine patients. There was a high rate (26%) of adverse events, including pulmonary embolism, stroke, and wound dehiscence. The median progression-free survival was 6.5 months. Conclusion: Although bevacizumab is commonly prescribed for cerebral radionecrosis, in our retrospective cohort, the clinical benefits were modest and there was significant toxicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".