Analysis of Factors Associated With Pathological Complete Response in Patients With HER2-Positive Breast Cancer Receiving Neoadjuvant Chemotherapy
Bibliographic record
Abstract
PURPOSE: This study aimed to examine the impact of the level of HER2 overexpression on pathologic and clinical outcomes in HER2-positive breast cancer (BC) patients treated with neoadjuvant therapy (NAT). METHODS: Women with Stage II or III HER2-positive BC who received anthracycline-taxane-trastuzumab NAT regimens followed by curative-intent surgery were included. Patients were classified according to tumor HER2 expression into HER2-high (immunohistochemistry (IHC) 3+ or fluorescence in situ hybridization (FISH) HER2/CEP17 ratio ≥5 or HER2 copy number ≥10) and HER2-intermediate (IHC 2+ with HER2/CEP17 ratio ≥2 to <5 or copy number ≥4 to <10). Univariate and multivariate logistic regression analyses were performed using HER2 expression as a categorical variable. The primary outcome was pathological complete response (pCR). Estimated 3-year disease-free survival (DFS) and Overall Survival (OS) were secondary outcomes. RESULTS: Among 161 patients with HER2-positive BC, 139 (86%) and 22 (14%) were classified as HER2-high and HER2-intermediate, respectively; 105 (65.2%) had hormone receptor (HR)-positive tumors; 72 (45%) achieved a pCR. In the overall population, pCR rates of 18% and 49% were achieved in HER2-intermediate and HER2-high cases, respectively (odds ratio [OR] = 0.23 95% CI 0.07-0.72; P = .007). No pCRs were observed among HR-positive, HER2-intermediate cases. Estimated 3-year DFS was 97.1% versus 89.3% for patients achieving a pCR versus those with residual disease, respectively (P = .0011). CONCLUSION: We found that patients with HER2-high disease were more likely to achieve pCR after NAT compared to patients with HER2-intermediate BC, a subgroup of patients that may benefit from more personalized NAT strategies.
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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 | 0.001 |
| 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) | 0.001 | 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".