Can excisional biopsy be avoided for papillary breast lesions with typical benign sonographic features and concordant core needle biopsy (CNB)?
Bibliographic record
Abstract
26 Background: The role of surgical excision in the management of papillary lesions remains controversial. Some studies have documented upgrade rates to malignancy or atypia as high as 17%. However, due to the inclusion of atypia or malignancy at percutaneous and other differences in methodology, these rates may have been over-estimated. This study was designed to determine sonographic features that can help to decide if a lesion warrants excision or standard follow-up. Methods: With institutional review board approval, we retrospectively reviewed 113 benign papillomas without atypia that were diagnosed at US-guided CNB at Foothills Medical Centre from 2005 to 2010. Surgical pathology for 62 of these lesions were assessed. Two radiologists who were blinded to the diagnosis reviewed the sonographic images to determine the likelihood of benignity. Statistical analysis was performed using chi-square and z-score tests. Results: In our study, 5.3% of benign papillomas were upgraded. The sonographic features associated with a higher likelihood of upgrade were intracystic type [LR+ 5.67, 95% CI (1.47,12)], non-periareolar location [LR+ 2.30, 95% CI (1.04,5.06)], complex echogenic pattern [LR+ 3.10, 95% CI (1.00,9.63)], and radiologist’s impression of unlikely benign (final BiRADS) [LR+ 1.48, 95% CI (1.01,1.90)]. Although no feature was statistically significant for greater likelihood of benignity, the sonographic features that may better predict benign pathology at excision include lesions <1cm in size [LR- 0.52], homogeneously solid [LR- 0.63], smooth margin [LR- 0.69] and periareolar location [LR- 0.47]. Conclusions: The management of benign papillomas diagnosed with CNB remains controversial with a lack of agreement among published articles. Our study identifies characteristic sonographic features that are associated with higher likelihood of malignancy, warranting surgical excision. Conversely, it may not be unreasonable to place patients with lesions that are stratified as low risk into standard radiographic follow-up. As the confidence intervals span 1.00 for latter, further study is necessary to confidently recommend follow-up in place of excision.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".