Nuclear morphometric features in benign breast tissue and risk of subsequent breast cancer
Bibliographic record
Abstract
Certain nuclear morphometric features measured in breast tumor tissue have been shown to predict the prognosis of breast cancer patients. However, the application of these features to predicting risk of breast cancer development has received little attention. We conducted a case-control study to evaluate nuclear morphometric features in benign breast tissue in association with subsequent breast cancer risk. The study was nested within a cohort of 4,888 women with a histopathologic diagnosis of benign breast disease (BBD) and involved 61 cases and 71 controls, amongst whom there were 53 matched case-control sets. Conditional logistic regression models were fitted to assess various measurements of nuclear size and nuclear shape factors in relation to subsequent breast cancer risk. In multivariate analysis, subsequent breast cancer risk was positively associated with a nuclear shape factor that takes the shortest nuclear axis and the longest nuclear axis into consideration simultaneously (highest quartile versus lowest 3 quartiles: odds ratio = 3.07, 95% confidence limits = 1.61, 5.84). In contrast, there was no alteration in subsequent breast cancer risk in association with nuclear size features and other shape factors. In conclusion, our study results suggest that the shape factor that takes both the shortest nuclear axis and the longest nuclear axis into consideration might be of value to predict subsequent development of breast cancer among women with BBD.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".