BEVACIZUMAB FOR SYMPTOMATIC RADIATION NECROSIS IN BRAIN METASTASES: OUTCOMES FROM A SINGLE-CENTER RETROSPECTIVE ANALYSIS
Bibliographic record
Abstract
Abstract Radiation necrosis is a late complication after radiotherapy and can be treated with bevacizumab, however predictors of response are not well established. We performed a single-institution retrospective chart review of patients with previously irradiated brain metastases who developed radiation necrosis and were treated with bevacizumab. 19 patients (86% female, median age of 62 years) were included. 50% had metastatic lung cancer, 37% had breast cancer and 11 % had melanoma. 18 had perfusion imaging, 9 had chemical exchange saturation transfer (CEST) imaging to increase diagnostic confidence. 6 cases were tissue-confirmed, 3 before treatment, and 3 upon recurrence after bevacizumab. All patients received a dose of 7.5 mg/kg per infusion every 3 weeks and 63% received 4 infusions. All were symptomatic before treatment. Clinical response was seen in 89% while radiographic response was achieved in 95% of patients. Dexamethasone was successfully discontinued in 71% of steroid-dependent patients. The median duration of response (interval between first infusion and recurrence) was 7.2 months (range: 2.3 to 43.2). Of the nine diagnosed using perfusion and CEST imaging, 89% had clinical response, 100% had a reduction in T2/FLAIR volume and 89% in T1 post-gadolinium enhancement, 83% discontinued dexamethasone. Grade 1/2 adverse events occurred in 8 patients (hypertension, fatigue, rash, joint pain, intracranial hemorrhage). One patient developed a grade 3 bowel perforation requiring surgery. Bevacizumab resulted in significant and prolonged clinical and radiographic improvement in a series of carefully selected patients. CEST imaging helped select responders to bevacizumab in our cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".