Bevacizumab for the Treatment of Radiation-Induced Cerebral Necrosis: A Systematic Review of the Literature
Bibliographic record
Abstract
Radiation necrosis (RN) of brain tissue is a serious late complication of brain irradiation and recently bevacizumab has been suggested as treatment option of RN. There is a lack of data in the literature regarding the effectiveness of bevacizumab for the treatment of RN. The purpose of this review was to perform a comprehensive analysis of all reported cases using bevacizumab for the treatment of brain RN. In September 2016, we performed a comprehensive literature search of the following electronic databases: PubMed, Web of Science, Scopus and Cochrane Library. The research for the review was conducted using a combination of the keywords "radiation necrosis", "radiotherapy" and "bevacizumab" alongside the fields comprising article title, abstract and keywords. Randomized trials, non-randomized trials, prospective studies, retrospective studies and single case reports were included in the review. Our research generated 21 studies and 125 cases where bevacizumab had been used for the treatment of RN. The median follow-up was 8 months and the most frequent bevacizumab dose used was 7.5 mg/kg for 2 weeks with a median of four cycles. Low-dose bevacizumab resulted in effectiveness with improvement in both clinical and radiographic response. The median decrease in T1 contrast enhancement and in T2/FLAIR signal abnormality was 64% and 60%, respectively. A reduction in steroidal therapy was observed in majority of patients treated. Based on the data of our review, bevacizumab appears to be a promising agent for the treatment of brain RN. Future prospective studies are required to evaluate the role of bevacizumab in RN and to define the optimal scheduling, dosage and duration of therapy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".