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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".