Quantification of the Morphologic Features of Fibroepithelial Tumors of the Breast
Bibliographic record
Abstract
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| 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".