Prognostic Significance of Human Epidermal Growth Factor Receptor Positivity for the Development of Brain Metastasis After Newly Diagnosed Breast Cancer
Bibliographic record
Abstract
PURPOSE: As survival in breast cancer patients is improving, brain metastases are becoming increasingly prevalent. The risk of brain metastases in newly diagnosed human epidermal growth factor receptor 2 (HER-2) -overexpressing breast cancer patients is not yet fully defined. We aimed to analyze the risk of brain metastasis in newly diagnosed HER-2-positive breast cancer patients in comparison with HER-2-negative patients. PATIENTS AND METHODS: To determine the incidence of brain metastases in HER-2-overexpressing patients, we analyzed a cohort of newly diagnosed 301 HER-2-positive and 363 HER-2-negative patients identified between January 1998 and December 2003. The association between histologic features and the occurrence of brain metastases was evaluated with univariate and multivariate Cox regression analysis. RESULTS: Median follow-up was 3.9 years. Brain metastases were identified in 9% (27 patients) with HER-2-overexpressing breast cancer compared with only 1.9% (7 patients) in the HER-2 negative patients (hazard ratio 4.23 [1.84-9.74], P = .0007). HER-2 overexpression, tumor size larger than 2 cm, at least one positive node, and grade 2/3 disease were predictors of brain metastases in univariate analysis. In multivariate analysis, HER-2 overexpression, tumor size larger than 2 cm, and hormone-receptor negativity were independent prognostic factors for the development of brain metastases, whereas hormone-receptor expression was protective. CONCLUSION: Our study shows that newly diagnosed HER-2-overexpressing breast cancer patients are at increased risk for brain metastases. Because most brain metastases occur after the development of systemic disease, these findings prompt consideration of brain prophylaxis strategies with HER-2-inhibiting small molecules able to cross the blood-brain barrier and/or radiologic screening to detect asymptomatic brain metastases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".