Assessment of the Predictive Role of Ki-67 in Breast Cancer Patients’ Responses to Neoadjuvant Chemotherapy
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Neoadjuvant chemotherapy (NAC) in breast cancer (BC) is being considered for a broader range of cases, including locally advanced tumors and situations where downstaging could reduce extensive surgery. Several trials have explored predictive markers of pathological complete response (pCR). The role of Ki-67 as a predictor of pCR has been demonstrated in studies. However, the cut-off remains vague, given the lack of standardization of measurement methods. The aim of our study was to evaluate the predictive value of Ki-67 in response to NAC and to identify the cut-off values that exhibit the strongest correlation with best response. MATERIALS AND METHODS: This retrospective study included 187 patients who had undergone surgery following NAC for BC at the CHU Souss Massa of Agadir between January 2020 and January 2023. Logistic regression was used to assess the correlation between Ki-67 and patients' characteristics. Optimal Ki-67 cutoff was identified by receiver operating characteristic curve. Kaplan-Meier curves were used to assess disease-free survival (DFS), and survival comparisons were assessed with the log-rank test. RESULTS: <0.001). DFS was 98.8% at 12 months. No statistical difference was found in DFS according to Ki-67 values and pCR status. CONCLUSION: Our results indicate that Ki-67 is a predictive marker for response in the neoadjuvant setting in BC patients. Our study showed that a Ki-67 cut-off >35% predicts a better pCR rate in response to NAC. However, this cutoff value remains controversial due to the absence of a standard method of measurement, with inter- and intra-observer variability. It would be necessary to validate this cutoff in other studies.
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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) | 0.000 | 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) | 0.000 | 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 it