The incidence of brain metastases among patients with metastatic breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients with metastatic breast cancer (MBC) are living longer, but the development of brain metastases often limits their survival. We conducted a systematic review and meta-analysis to determine the incidence of brain metastases in this patient population. METHODS: Articles published from January 2000 to January 2020 were compiled from four databases using search terms related to breast cancer, brain metastasis, and incidence. The overall and per patient-year incidence of brain metastases were extracted from studies including patients with human epidermal growth factor receptor-2 positive (HER2+), triple negative, and hormone receptor (HR)+/hormone receptor negative (HER2-) MBC; pooled overall estimates for incidence were calculated using random effects models. RESULTS: 937 articles were compiled, and 25 were included in the meta-analysis. Incidence of brain metastases in patients with HER2+ MBC, triple negative MBC, and HR+/HER2- MBC was reported in 17, 6, and 4 studies, respectively. The pooled cumulative incidence of brain metastases was 31% for the HER2+ subgroup (median follow-up: 30.7 months, IQR: 24.0-34.0), 32% for the triple negative subgroup (median follow-up: 32.8 months, IQR: 18.5-40.6), and 15% among patients with HR+/HER2- MBC (median follow-up: 33.0 months, IQR: 31.9-36.2). The corresponding incidences per patient-year were 0.13 (95% CI: 0.10-0.16) for the HER2+ subgroup, 0.13 (95%CI: 0.09-0.20) for the triple negative subgroup, and only 0.05 (95%CI: 0.03-0.08) for patients with HR+/HER2- MBC. CONCLUSION: There is a high incidence of brain metastases among patients with HER2+ and triple negative MBC. The utility of a brain metastases screening program warrants investigation in these populations.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".