[Efficacy and Safety of Radiotherapy Combined with Immunotherapy for Brain Metastases from Lung Cancer: A Meta-analysis].
Bibliographic record
Abstract
BACKGROUND: Immunotherapy (IT) is recommended for the treatment of advanced non-small cell lung cancer (NSCLC), while brain radiotherapy (RT) is the mainstream treatment for patients with brain metastases (BM). This study aimed to investigate the efficacy and safety of combined use of RT and IT. METHODS: The date was limited to May 1, 2022, and literature searches were carried out in CNKI, Wanfang, PubMed, EMBASE and Cochrane databases. Heterogeneity was judged using the I2 test and P value. Publication bias was assessed using a funnel plot. The quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS). Statistical analysis was performed using Stata 16.0 software. RESULTS: A total of 17 articles involving 2,636 patients were included. In the comparison of RT+IT group and RT group, no significant difference was found in overall survival (OS) (HR=0.85, 95%CI: 0.52-1.38, I2=73.9%, Pheterogeneity=0.001) and intracranial distance control (DBC) (HR=1.04, 95%CI: 0.55-1.05, I2=80.5%, Pheterogeneity<0.001), but the intracranial control (LC) in the RT+IT group was better than the RT group (HR=0.46, 95%CI: 0.22-0.94, I2=22.2%, Pheterogeneity=0.276), and the risk of radiation necrosis/treatment-related imaging changes (RN/TRIC) was higher than RT (HR=1.72, 95%CI: 1.12-2.65, I2=40.2%, Pheterogeneity=0.153). In the comparison between the RT+IT concurrent group and the sequential group, no significant difference was found in OS (HR=0.62, 95%CI: 0.27-1.43, I2=74.7%, Pheterogeneity=0.003) and RN/TRIC (HR=1.72, 95%CI: 0.85-3.47, I2=0%, Pheterogeneity=0.388) was different between the two groups. However, DBC in the concurrent treatment group was better than that in the sequential treatment group (HR=0.77, 95%CI: 0.62-0.96, I2=80.5%, Pheterogeneity<0.001). CONCLUSIONS: RT combined with IT does not improve the OS of NSCLC patients with BM, but also increases the risk of RN/TRIC. In addition, compared with sequential RT and IT, concurrent RT and IT improved the efficacy of DBC.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.069 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".