Ki67 assessment protocol as an integral biomarker for avoiding radiotherapy in the LUMINA breast cancer trial
Bibliographic record
Abstract
AIMS: The LUMINA trial demonstrated a very low local recurrence rate in women ≥55 years with low-risk luminal A breast cancer (defined as grade I-II, T1N0, hormone receptor positive, HER2 negative and Ki67 index ≤13.25%) treated with breast-conserving surgery and endocrine therapy (but no other systemic therapy), supporting the safe omission of radiation in these women. Here we describe the protocol for Ki67 assessment, the companion diagnostic used to guide omission of adjuvant radiotherapy. METHODS: Ki67 immunohistochemistry was performed on full-face sections at one of three regional labs. Pathologists trained in the International Ki67 in Breast Cancer Working Group (IKWG) method demarcated tumour areas on scanned slides and scored 100 nuclei from each of at least five randomly selected 1-mm fields. For cases with high Ki67 heterogeneity, further virtual cores were selected and scored in order to confidently assign a case as luminal A (≤13.25%) or B (>13.25%). Interlaboratory variability was assessed through an annual quality assurance programme during the study period. RESULTS: From the quality assurance programme, the mean Ki67 index across all cases/labs was 13%. The observed intraclass correlation coefficient (ICC) and kappa statistics were ≥0.9 and ≥0.7, respectively, indicating a substantial level of agreement. Median scoring time was 4 min per case. The IKWG-recommended scoring method, performed directly from slides, requiring up to four scored fields, is concordant with the LUMINA scoring method (ICC ≥ 0.9). CONCLUSION: Ki67 is a practical, reproducible, and inexpensive biomarker that can identify low-risk luminal A breast cancers as potential candidates for radiation de-escalation. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01791829.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".