The Incidence of Brain Metastases in Breast Cancer according to molecular subtype and stage: a 10-year single institution analysis
Bibliographic record
Abstract
Abstract Background Breast cancer (BC) is the second most common etiology of brain metastases (BrM). We aimed to examine the incidence of BrM among all BC patients presenting to a large tertiary cancer centre over one decade. Methods We included all BC patients presenting consecutively between 2009 and 2019 and cross referenced that cohort to a radiotherapy database, identifying patients treated for BrM at any time following their initial presentation. Cumulative incidences (CI) of BrM diagnoses were calculated using death as a competing risk and compared using the Fine-Gray method. Overall survival was estimated using the Kaplan Meier method. Results We identified 12,995 unique patients. The CI of BrM in patients who initially presented with Stage 0-4 disease was 2.1%, 3.7%, 9.4%, 10.6%, and 28.7%, respectively at 10 years. For 8,951 patients with available molecular subtype data, 6,470 (72%), 961 (11%), 1,023 (11%), and 497 (6%) had hormone-receptor (HR)-positive/ERBB2-, HR-negative/ERBB2-, HR-positive/ERBB2+, and HR-negative/ERBB2+ disease, respectively; the CI of BrM in each was 7.6%, 25.3%, 24.1%, and 26.6%, at 10 years following BC diagnosis, respectively. Median overall survival (OS) following BC diagnosis and BrM diagnosis was 28 years 95% CI [25, 32] and 10 months 95% CI [9, 12], respectively. Conclusions From a large, registry-based study, we observed that patients with ERBB2+ and triple negative BC have the highest incidence of BrM. Clinicians should have a low threshold for brain imaging in patients with a history of BC who experience symptoms suggestive of BrM, especially in ERBB2+ and triple negative BC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".