Development of a Ki-67-based clinical trial assay for neoadjuvant endocrine therapy response monitoring in breast cancer
Bibliographic record
Abstract
PURPOSE: The recent publication of the ACOSOG Z1031 trial results demonstrated that Ki-67 proliferation marker-based neoadjuvant endocrine therapy response monitoring could be used for tailoring the use of adjuvant chemotherapy in ER+HER2-negative breast cancer patients. In this paper, we describe the development of the Ki-67 clinical trial assay used for this study. METHODS: Ki-67 assay assessment focused on reproducing a 2.7% Ki-67 cut-point (CP) required for calculating the Preoperative Endocrine Prognostic Index and a 10% CP for poor endocrine therapy response identification within the first month of neoadjuvant endocrine treatment. Image analysis was assessed to increase the efficiency of the scoring process. Clinical outcome concordance for two independent Ki-67 scores was the primary performance metric. RESULTS: Discordant scores led to a triage approach where cases with complex histological features that software algorithms could not resolve were flagged for visual point counting (17%). The final Ki-67 scoring approach was run on T1/2 N0 cases from the P024 and POL trials (N = 58). The percent positive agreement for the 2.7% CP was 87.5% (95% CI 61.7-98.5%); percent negative agreement 88.9% (95% CI: 65.3-98.6%). Minor discordance did not affect the ability to predict similar relapse-free outcomes (Log-Rank P = 0.044 and P = 0.055). The data for the 10% early triage CP in the POL trial were similar (N = 66), the percentage positive agreement was 100%, and percent negative agreement 93.55% (95% CI: 78.58-99.21%). The independent survival predictions were concordant (Log-rank P = 0.0001 and P = 0.01). CONCLUSIONS: We have developed an efficient and reproducible Ki-67 scoring system that was approved by the Clinical Trials Evaluation Program for NCI-supported neoadjuvant endocrine therapy trials. Using the methodology described here, investigators are able to identify a subgroup of patients with ER+HER2-negative breast cancer that can be safely managed without the need of adjuvant 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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".