The diagnostic utility of CK5/6 and p63 in fine‐needle aspiration of the breast lesions diagnosed as proliferative fibrocystic lesion
Bibliographic record
Abstract
Fine-needle aspiration (FNA) biopsy (FNAB) in the preoperative assessment of breast lesions has shown diagnostic limitations with false-positive and false-negative diagnoses. We investigated the diagnostic value of cytokeratin 5/6 (CK5/6) and p63 in a series of breast FNABs, diagnosed as proliferative breast lesions with or without atypia, to see whether these ancillary studies enhance the ability to make an accurate diagnosis by FNAB. Sixty-four breast FNABs were retrieved between January 2000 and December 2005 and included in the study as follows: 29/64 (45%) cases as proliferative with atypia and 35/64 (55%) without atypia. We also included 10 cases of fibroadenoma. All cases had histological follow-up available for correlation. Immunostaining for CK5/6 and p63 was performed on the cell block material in all cases. The percentage of staining cells in the specimen was graded as 0 (0-10%), 1 (11-25%), 2 (26-50%), and 3 (>50%). There were 9/29 (31%) cases in the atypical group that were found to be malignant on resection, compared with 6/35 (17%) in the cases without atypia. In histologically proven malignant cases, CK5/6 was negative in 11/15 (73%) or showed 1+ stain in 2/15 (13%) cases. In benign breast lesions, CK5/6 stained more than 25% of cell proliferation in 44/49 (90%). p63 showed characteristic staining for single naked bipolar nuclei in the background of the specimen (not appreciated by CK5/6) in all fibroadenoma cases. In conclusion, CK5/6 may enhance the ability to differentiate between benign and malignant epithelial proliferations in breast FNABs. In fibroepithelial lesions, p63 may be more useful than CK5/6.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| 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 | 0.001 |
| 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".