Improving the accuracy of reporting Ki-67 IHC by using an AI tool
Bibliographic record
Abstract
Ki-67 proliferative index (PI) scoring is measured by estimating the proportion of the number of active cell nuclei in hotspot regions within immunohistochemical (IHC) stained slides. It provides valuable information about the rate of proliferation in a tumour. Manual scoring of Ki-67 PI is laborious, time-consuming and often the victim of interobserver variability between pathologists. This motivated us to develop an AI-based method to automate Ki-67 PI scoring with the aim to improve the concordance of pathologists' inter-observability through aided diagnosis. We sourced 88 sequential cases of sarcomas for our study. We applied watershed algorithm to perform nuclear segmentation on 440 regions of interest (ROI). A study was conducted where three pathologists scored the Ki-67 PI on the ROIs with and without AI-assistance. Our study demonstrated great concordance between the pathologists scoring with AI-assistance. After AI assistance, inter-pathologist discordance was significantly reduced by 82.1.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".