Reliability of Ki67 visual scoring app compared to eyeball estimate and digital image analysis and its prognostic significance in hormone receptor‐positive breast cancer
Bibliographic record
Abstract
We analysed the reproducibility of Ki67 labelling index (LI) between two scorers using the International Ki67 Working Group (IKWG) global methods on an Android application (APP), correlated the APP and eyeball estimate (EBE) with digital image analysis (DIA) scores and determined the prognostic significance of Ki67LI. Global weighted (GW) and global unweighted (GUW) Ki67 app scores of hormone receptor-positive and HER2 (human epidermal growth factor receptor 2)-negative breast cancer patients were obtained. Reproducibility of Ki67LI between 2 scorers and correlation of APP and EBE scores with DIA scores were performed. The prognostic significance of APP scores and its correlation with other clinico-pathologic variables were evaluated. The intra-class correlation coefficient (ICC) between 2 scorers showed excellent reliability with both GW and GUW methods. ICC between DIA and APP scores was significantly greater than DIA versus EBE. The three categories of APP scores based on median value and cut points of 10%, 18% and 38% were significantly associated with poor DFS. On multivariate analysis, significant association between Ki67LI, tumour size, nodal involvement and DFS was noted. Our study shows that the visual Ki67 scoring app is effective in bringing consistency to KI67LI and APP scores showed significant correlation with DFS.
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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 | 0.005 | 0.011 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".