Central Nervous System Metastasis in Patients with HER2-Positive Metastatic Breast Cancer: Patient Characteristics, Treatment, and Survival from SystHERs
Bibliographic record
Abstract
PURPOSE: Patients with HER2-positive metastatic breast cancer (MBC) with central nervous system (CNS) metastasis have a poor prognosis. We report treatments and outcomes in patients with HER2-positive MBC and CNS metastasis from the Systemic Therapies for HER2-positive Metastatic Breast Cancer Study (SystHERs). EXPERIMENTAL DESIGN: SystHERs (NCT01615068) was a prospective, U.S.-based, observational registry of patients with newly diagnosed HER2-positive MBC. Study endpoints included treatment patterns, clinical outcomes, and patient-reported outcomes (PRO). RESULTS: Among 977 eligible patients enrolled (2012-2016), CNS metastasis was observed in 87 (8.9%) at initial MBC diagnosis and 212 (21.7%) after diagnosis, and was not observed in 678 (69.4%) patients. White and younger patients, and those with recurrent MBC and hormone receptor-negative disease, had higher risk of CNS metastasis. Patients with CNS metastasis at diagnosis received first-line lapatinib more commonly (23.0% vs. 2.5%), and trastuzumab less commonly (70.1% vs. 92.8%), than patients without CNS metastasis at diagnosis. Risk of death was higher with CNS metastasis observed at or after diagnosis [median overall survival (OS) 30.2 and 38.3 months from MBC diagnosis, respectively] versus no CNS metastasis [median OS not estimable: HR 2.86; 95% confidence interval (CI), 2.05-4.00 and HR 1.94; 95% CI, 1.52-2.49]. Patients with versus without CNS metastasis at diagnosis had lower quality of life at enrollment. CONCLUSIONS: Despite advances in HER2-targeted treatments, patients with CNS metastasis continue to have a poor prognosis and impaired quality of life. Observation of CNS metastasis appears to influence HER2-targeted treatment choice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".