Sonography of Postexcision Specimens of Nonpalpable Breast Lesions: Value, Limitations, and Description of a Method
Bibliographic record
Abstract
OBJECTIVE: The objective of our study was to retrospectively review our experience regarding the value of sonography in identifying a nonpalpable mass within a surgically excised specimen and in assessing the surgical margins in cases of malignancy. MATERIALS AND METHODS: One hundred four lumpectomies were performed in 99 consecutive patients with 131 nonpalpable breast lesions after sonographically guided needle localization. All 104 surgical specimens were scanned on sonography, and 86 specimen radiographs were obtained. Visualization of the lesion on sonography was compared with specimen radiographs and histologic findings. Sonographic margin status was classified as negative (shortest distance between tumor and specimen margin, > 0.2 cm) or positive (shortest distance between tumor and specimen margin, 0.2 cm) and was compared with pathology results. RESULTS: Specimen sonography showed 95.4% (125/131) of the excised abnormalities; nonfatty background and a lesion size of greater than 0.5 cm contributed significantly to the success of specimen sonography. Four of six lesions missed on sonography were identified on specimen radiography. Among 81 malignant specimens, sonography identified 38 specimens with positive margins and 43 with negative margins. Pathologic examination revealed eight false-positive and 10 false-negative results (21% false-positive rate and 23.2% false-negative rate). CONCLUSION: Specimen sonography is an effective procedure for identifying the presence of the lesion within the specimen; however, it is of limited value in cases of small hypoechoic lesions against a fatty background. Assessment of margins is limited by both false-positive and false-negative results.
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 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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".