Quantification of the morphologic features of fibroepithelial tumors of the breast.
Bibliographic record
Abstract
CONTEXT: Phyllodes tumors of the breast are uncommon, comprising 0.3% to 0.9% of female primary breast tumors. Owing in part to their rarity, definitive, objective, reproducible morphologic criteria that reliably distinguish benign from low-grade malignant or malignant phyllodes tumors have yet to be established. OBJECTIVE: To use image analysis to quantitate and compare morphologic features of different groups of fibroepithelial tumors (FETs) of the breast. DESIGN: Hematoxylin-eosin-stained sections of 41 FETs previously identified as fibroadenoma, benign phyllodes, low-grade malignant phyllodes, or high-grade malignant phyllodes were blinded and studied using a Leica DMRA2 microscope and OpenLab Image Analysis software. Features measured included mitotic rate per 10 high-power fields, stromal cellularity, nuclear size, stromal overgrowth, and the largest and smallest stromal-epithelial surface area ratios. Epithelial appearance was measured on a semiquantitative basis. Features of each case including tumor size, margin status, and the presence of necrosis or heterologous elements were also considered; these data were retrieved from surgical pathology reports. RESULTS: Quantitative measures of stromal cellularity, stromal-epithelial ratio, mitotic rate, stromal overgrowth, and mean nuclear diameter were developed and found to stratify a population of FETs by the current classification system of fibroadenoma, benign, and low-grade or high-grade malignant phyllodes tumor. CONCLUSIONS: Quantitative morphologic features of FETs can be used to stratify these tumors by subtype. Use of these quantitative criteria could reduce interrater variability in histologically identifying FETs by subclass.
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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 | 0.003 | 0.001 |
| 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".