State of the Art in Combination Immuno/Radiotherapy for Brain Metastases: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Common origins for brain metastases (BMs) are melanoma, lung, breast, and renal cell cancers. BMs account for a large share of morbidity and mortality caused by these cancers. The advent of new immunotherapeutic treatments has made a revolution in the treatment of cancer patients and particularly, as a new concept, if it is combined with radiotherapy, may lead to considerably longer survival. This systematic review and meta-analysis aimed to evaluate the survival rate and toxicities of such a combination in brain metastases. METHODS: To perform a systematic review of the literature until January 2021 using electronic databases such as PubMed, Cochrane Library, and Embase; the Newcastle-Ottawa Scale was used to evaluate the quality of cohort studies. For data extraction, two reviewers extracted the data blindly and independently. Hazard ratio with 95% confidence interval (CI), fixed-effect model, and inverse-variance method was calculated. The meta-analysis has been evaluated with the statistical software Stata/MP v.16 (The fastest version of Stata). RESULTS: In the first step, 494 studies were selected to review the abstracts, in the second step, the full texts of 86 studies were reviewed. Finally, 28 studies were selected consisting of 1465 patients. The addition of IT to RT in the treatment of brain metastasis from melanoma and non-small-cell lung carcinoma was associated with a 39% reduction in mortality rate and has prolonged overall survival, with an acceptable toxicity profile. The addition of IT to RT compared with RT alone has a hazard ratio of 0.39(95% CI 0.34-0.44). CONCLUSIONS: A combination of immuno/radiotherapy (IR) for the treatment of patients with BMs from melanoma and non-small-cell lung carcinoma has prolonged overall survival and reduced mortality rate, with acceptable toxicity. In terms of timing, RT seems to have the best effect on the result when performed before or simultaneously with immunotherapy.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 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".