HER2-targeted therapy prolongs survival in patients with HER2-positive breast cancer and intracranial metastatic disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Intracranial metastatic disease (IMD) is a serious and known complication of human epidermal growth factor receptor 2 (HER2)-positive breast cancer. The role of targeted therapy for patients with HER2-positive breast cancer and IMD remains unclear. In this study, we sought to evaluate the effect of HER2-targeted therapy on IMD from HER2-positive breast cancer. METHODS: We searched MEDLINE, EMBASE, CENTRAL, and gray literature sources for interventional and observational studies reporting survival, response, and safety outcomes for patients with IMD receiving HER2-targeted therapy. We pooled outcomes through meta-analysis and examined confounder effects through forest plot stratification and meta-regression. Evidence quality was evaluated using GRADE (PROSPERO CRD42020161209). RESULTS: A total of 97 studies (37 interventional and 60 observational) were included. HER2-targeted therapy was associated with prolonged overall survival (hazard ratio [HR] 0.47; 95% confidence interval [CI], 0.39-0.56) without significantly prolonged progression-free survival (HR 0.52; 95% CI, 0.27-1.02) versus non-targeted therapy; the intracranial objective response rate was 19% (95% CI, 12-27%), intracranial disease control rate 62% (95% CI, 55-69%), intracranial complete response rate 0% (95% CI, 0-0.01%), and grade 3+ adverse event rate 26% (95% CI, 11-45%). Risk of bias was high in 40% (39/97) of studies. CONCLUSION: These findings support a potential role for systemic HER2-targeted therapy in the treatment of patients with IMD from HER2-positive metastatic breast cancer.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".