Papillary Lesions of the Breast
Bibliographic record
Abstract
BACKGROUND: Classifying papillary lesions of the breast on core biopsy (CB) is challenging. Although traditionally all such lesions were surgically excised, at present, conservative management of benign lesions is being advocated; therefore, accurately classifying papillary lesions on CB is all the more imperative. The extent to which subspecialty training in breast pathology might mitigate such difficulties in diagnosis has not yet been reported. We investigated change in diagnoses from CB to surgical excision according to subspecialist training in breast pathology and interobserver agreement between specialized breast pathologists (BPs) and nonbreast pathologists (NBPs) in classifying these lesions. DESIGN: CBs of 281 papillary lesions from 266 patients diagnosed between 2000 and 2010 were classified by both a BP and NBP into benign, atypical, ductal carcinoma in situ/encapsulated papillary carcinoma, or invasive carcinoma categories. Rates of change in diagnostic category in the surgical excision specimen were calculated on the basis of: (i) the original diagnosis, (ii) diagnosis made by the BP, and (iii) diagnosis made by the NBP. Comparisons were made using the χ test. Kappa values were calculated for interobserver agreement. RESULTS: Of 162 lesions with subsequent excision, 90 were originally diagnosed as benign, 38 as atypical, 25 as ductal carcinoma in situ/encapsulated papillary carcinoma, and 9 as invasive on CB. The upgrade rate for benign papillomas to an atypical or malignant lesion on surgical excision was 22.2% according to the original diagnosis. This rate fell to 16.3% when the BP diagnoses were considered, compared with 26.3% for the NBP diagnoses. There was no significant difference between BPs and NBPs in the rate of upgrade from a benign to an atypical/malignant diagnosis, although downgrades from atypical/malignant to benign papillomas were more commonly seen among NBPs (P=0.002). Overall, the BP diagnosis on CB was less likely to differ from the excision diagnosis (P=0.0001). Benign papillomas upgraded on excision were more likely to occur with larger radiologic mass size (P=0.033) compared with those that were not upgraded. Of 8 benign papillomas upgraded to a malignant lesion on excision, 7 were discordant on radiology. Interobserver agreement between BP and NBP diagnoses was in the "fair agreement" range (κ=0.38), with perfect agreement in 66.4% of cases. CONCLUSIONS: Correlation between CB and excision diagnoses for breast papillary lesions is significantly greater for BPs than for NBPs. This is largely because of a tendency to overcall atypia or malignancy on CB by NBPs. However, upgrades from benign to atypical or malignant did not significantly differ according to subspecialization. With accurate pathologic assessment and radiologic-pathologic correlation, the upgrade rate of benign papillomas to malignancy can be minimized significantly.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".