Current breast cancer proliferative markers correlate variably based on decoupled duration of cell cycle phases
Bibliographic record
Abstract
Mitotic count, PhH3, and MIB-1 are used as measures of the proportion of proliferating malignant cells in surgical pathology. They highlight different stages of the cell cycle, but little is known about how this affects their counts. This study assesses the strength of their correlations and attempts to determine the relationship between them. Proliferation counts for forty-nine consecutive cases of invasive breast carcinomas were analyzed, with the same tumor area on each stain counted using digital image analysis. The integrated optical density (IOD) of nuclei was measured as an approximation of nuclear DNA content. PhH3 strongly correlated with mitotic count (r = 0.94). Weaker correlations were found between MIB-1 versus PhH3 (r = 0.79) and mitotic count (r = 0.83). Nuclear IOD showed stronger correlation with MIB-1 (r = 0.37) than to mitotic count (r = 0.23) and PhH3 (r = 0.34). With evidence from a literature review, it is suggested that the weaker correlations with MIB-1 are not explained by count imprecision or error, but relies on temporal decorrelation between cell cycle phases. Consequences on correlation between these proliferative markers are illustrated by mathematical models.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".