Treatment Strategies for Residual Disease following Neoadjuvant Chemotherapy in Patients with Early-Stage Breast Cancer
Bibliographic record
Abstract
Breast cancer continues to be the most diagnosed cancer among women worldwide. Neoadjuvant chemotherapy is the standard of care for breast cancer patients with locally advanced disease and patients with poor pathological features, such as triple-negative (TN) or human epidermal growth factor receptor-2 (HER2)-positive subtypes. Neoadjuvant therapy offers several advantages, including better surgical outcomes, early systemic treatment for micro-metastases, and accurate tumor biology and chemosensitivity assessment. Multiple studies have shown that achieving pathological complete response (pCR) following neoadjuvant chemotherapy is associated with better prognosis and better treatment outcomes; almost half of such patients may fail to achieve pCR. Tumor proliferative index, hormone receptor (HR) status, and HER2 expression are the major predictors of pCR. Strategies to improve pCR have been dependent on augmenting neoadjuvant chemotherapy with the addition of taxanes and dual anti-HER2 targeted therapy in patients with HER2-positive tumor, and more recently, immunotherapy for patients with TN disease. The clinical management of patients with residual disease following neoadjuvant chemotherapy varies and depends mostly on the level of HR expression and HER2 status. Recent data have suggested that switching trastuzumab to trastuzumab-emtansine (T-DM1) in patients with HER2-positive disease and the addition of capecitabine for patients with HER2-negative and HR-negative subtype is associated with a better outcome; both strategies are incorporated into current clinical practice guidelines. This paper reviews available and ongoing studies addressing strategies to better manage patients who continue to have residual disease following neoadjuvant chemotherapy.
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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) | 0.001 | 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 | 0.001 |
| 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".