Cellular fibroepithelial lesions diagnosed on core needle biopsy: Is there any role of clinical‐sonography features helping to differentiate fibroadenomas and phyllodes tumor?
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to determine the role of clinico-sonographic features of breast cellular fibroepithelial lesions (CFELs) diagnosed on core needle biopsy (CNB) in the differentiation between fibroadenoma (FA) and phyllodes. MATERIALS AND METHODS: Results of consecutive women with a CNB showing CFEL from 2005 to 2010 were retrospectively reviewed. Clinical and sonographic findings were compared with surgical outcomes. Chi-square and Fisher's exact tests were used followed by a regression model for statistical analysis. RESULTS: A total of 131 women with 134 CFEL were included in the study; 89 (66%) were FAs and 45 (34%) were phyllodes (32 benign; 13 malignant). Significant predictors of increased risk of phyllodes tumor were patient age equal to or greater than 50 years (P = .021) and lesion size less than 2 cm at sonography (P = .043). No other imaging or clinical features were able to differentiate FA from phyllodes tumors. CONCLUSION: CFEL with a larger size in older women is associated with the surgical pathological result of phyllodes tumor and management should be tailored accordingly. Younger patients with small size nodules might be approached less aggressively, depending on a personalized discussion with the surgeons, taking into account the results obtained in this study.
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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 | 0.007 |
| 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 | 0.001 |
| 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".