Auto-Analysis for Ki-67 Indices of Breast Cancer Using Specified Computer Software and a Virtual Microscopy
Bibliographic record
Abstract
Ki-67 index is one of important markers that is correlated with chemotherapy response and prognosis of breast cancer patients. However, Ki-67 index is not easily provided and are limited by intra-observer error and potentially subjective decision making. We performed this study to develop an objective auto-analysis system to count Ki-67 indices. A total of185 invasive breast cancer cases were used. Immunohistochemical staining was performed using auto-stainer and MIB-1 antibody. The results were stored digitally by virtual microscopy and auto-analyzed by Genie/Aperio software (Vista, CA, USA). As for Ki-67 indices, a good correlation was observed between direct ocular observations and auto-analysis techniques (r = 0.94, p < 0.001). The index examined by auto-analysis was significantly correlated with nuclear atypia, mitotic counts, and nuclear grade of pT1 breast cancers. Auto-analysis of 5 high power fields was better correlated with nuclear grade than that of whole fields. Further, the Ki-67 index was better correlated with mitotic counts than with nuclear atypia.Auto-analysis can provide results concordant with those obtained by direct ocular observation in a short time. Auto-analysis is more likely to result in an objective observation and provide a means by which to standardize methods for immunohistochemical Ki-67 indices of breast cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".