Predictors of Pathologic Complete Response and Its Prognostic Value in Early Breast Cancer: A Real-World Cohort Study
Bibliographic record
Abstract
Background: Pathologic complete response (pCR) after neoadjuvant systemic therapy (NAST) is a key prognostic marker in early breast cancer (EBC), particularly in triple-negative (TNBC) and HER2-positive subtypes. However, real-world data on predictors of pCR and their impact on survival remain limited. Methods: We retrospectively analyzed 200 patients with stage II–III EBC treated with NAST at a single institution (2015–2023). Clinicopathologic variables and treatment characteristics were evaluated for association with pCR (ypT0/is ypN0), and histological regression was additionally assessed using the Miller–Payne scoring system. Multivariable logistic regression identified independent predictors. Disease-free survival (DFS) and overall survival (OS) were estimated using Kaplan–Meier methods. Results: Overall, 36.0% achieved pCR, with the highest rates in HER2-positive (65%) and TNBC (56%) subtypes. Independent predictors of pCR included HER2 positivity (OR 4.21, 95% CI 1.83–9.67, p < 0.001), high Ki-67 > 47.5% (OR 3.62, 95% CI 1.68–7.80, p = 0.001), ER < 10% (OR 2.77, 95% CI 1.18–6.50, p = 0.019), and radiologic complete response (OR 10.03, 95% CI 2.91–34.60, p < 0.001). At a median follow-up of 75 months, compared with non-pCR, patients achieving pCR had a significantly lower risk of recurrence (HR 0.16, 95% CI 0.04–0.70, p = 0.014) with 5-year DFS rates of 91.5% vs. 72.8%. For OS, pCR patients showed a lower risk of death (HR 0.33, 95% CI 0.07–1.49, p = 0.150), corresponding to 5-year OS of 92.2% vs. 87.0%, although this difference was not statistically significant. Conclusions: HER2 positivity, high Ki-67, low ER expression, and radiologic complete response are independent predictors of pCR. Achieving pCR strongly correlates with improved DFS but not OS, likely due to limited sample size and event number. These findings reinforce pCR as a surrogate endpoint in TNBC and HER2-positive disease and highlight the need for post-neoadjuvant escalation in non-pCR patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".