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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".