Immune Checkpoint Inhibitors and Survival Outcomes in Brain Metastasis: A Time Series-Based Meta-Analysis
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have shown potential to improve the prognosis of patients with brain metastasis (BM) caused by advanced cancers. However, controversies still exist in regard to its survival benefits. In the present work, a time series-based meta-analysis based on the phase I/II/III trials and observational studies were performed to investigate the differences in mortality of ICI-treated BM patients. A number of public library databases, including MEDLINE, EMBASE, OVID, and COCHRANE, were systemically searched by March 2019. The quality of included studies was evaluated by the Newcastle-Ottawa Scale (NOS) scoring. Outcome measures here established were mortality and progression-free survival (PFS) at different follow-up endpoints. Survival rates and curve data were pooled for further analysis. To detect the data heterogeneity, subgroup analyses were conducted according to tumor and ICI types. Eighteen studies, 6 trials, and 12 controlled cohorts were assessed, involving a total of 1330 ICI-treated BM patients. The 6-month survival rate and PFS were 0.67 (95%CI: 0.59-0.74) and 0.36 (95%CI: 0.24-0.49), respectively. According to the tumor type (melanoma, NSCLC, and RCC), subgroup analyses indicated that melanoma presented the lowest survival rates among the three groups here selected. In regard to the type of ICIs, the anti-CTLA-4 combined with the anti-PD-1/PD-L1 showed the best survival outcome among these groups. The 12-month survival rate and PFS showed a consistent pattern of findings. In the long-term, the 24-month survival rate and PFS were 0.20 (95%CI: 0.12-0.31) and 0.18 (0.05-0.46) in BM patients. Hence, ICI therapy may be associated with an improved prognosis of BM patients. Nevertheless, current research presented a limited study design. Multicenter randomized trials may later assist in validating ICI-based therapies for a better outcome of BM patients.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.060 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".