Accuracy of percutaneous core needle biopsy in diagnosing papillary breast lesions and potential impact of sonographic features on their management
Bibliographic record
Abstract
OBJECTIVE: To assess retrospectively the accuracy of core needle biopsy in diagnosing papillary breast lesions and evaluate the prediction of malignant papillary lesions based on sonographic features. METHODS: Review of 130 papillary lesions diagnosed on core needle biopsy (2002-2008) in 110 patients. The biopsy results were compared with final surgical pathology or evolution on imaging follow-up. Lesion size, patient age, type of biopsy needle and guidance, and length of imaging follow-up were documented. Sonographic features were retrospectively reviewed according to the BI-RADS lexicon. Morphology, not part of BI-RADS, was assessed as intraductal, intracystic, or solid. RESULTS: Of the 130 papillary lesions, 6 were sampled with an 11-G vacuum-assisted needle under stereotactic guidance and the remaining 124 were sampled under US guidance with a 14-G (n = 115), 18-G (n = 8), or 10-G (n = 1) needle. Initial core needle biopsy diagnosis was benign (n = 103), showed atypia (n = 20), or malignancy (n = 7). Thirty-seven (36%) benign lesions were surgically excised and 66 (64%) were followed up. On final outcome, 10 benign lesions were upgraded to malignancy (9.7%) and 3 to atypia (3.6%). There was no significant difference in the benign, malignant, and upgraded groups with respect to size, age, or BI-RADS sonographic characteristics. None of the oval-shaped lesions nor the intraductal ones were upgraded. CONCLUSIONS: Although some sonographic features could favor a benign diagnosis, when a core biopsy yields the diagnosis of a papillary lesion, surgical excision is recommended to definitely exclude malignancy.
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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 | 0.003 | 0.020 |
| 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 | 0.000 |
| 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".