Malignancy‐Associated Changes in Breast Tissue Detected by Image Cytometry
Bibliographic record
Abstract
In several tissues, nuclear differences have been described in normal-appearing cells from patients with invasive carcinomas compared to cases without invasive carcinoma, a phenomenon known as malignancy-associated changes (MACs). The aim of this study was to determine the presence of malignancy-associated changes in breast tissue. Image cytometry was performed on Feulgen stained tissue sections of patients with usual ductal hyperplasia with (n = 30) or without (n = 41) adjacent invasive breast carcinoma. Nuclear features of normal-appearing cells as well as of usual ductal hyperplastic cells were separately compared between the two groups. Many features of normal-appearing epithelial cells were significantly different between cases with and without invasive cancer. Significant differences were also found by measuring ductal hyperplastic nuclei instead of normal-appearing nuclei. Cases with or without cancer could be distinguished with a classification accuracy of 80% by discriminant analysis using 2 nuclear features derived from ductal hyperplastic cells. In conclusion, image cytometry on breast tissue sections shows that malignancy-associated changes can be found in normal as well as in usual ductal hyperplastic breast cells. This could be clinically relevant for the detection of occult breast cancer, for the prediction of risk in these lesions, and to monitor the effect of chemopreventive agents.
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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 | 0.001 |
| 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".