In Search of Calcifications : Histologic Analysis and Diagnostic Yield of Stereotactic Core Needle Breast Biopsies
Bibliographic record
Abstract
OBJECTIVES: Stereotactic core needle biopsy (SCNB) is used in the diagnostic assessment of suspicious mammographic calcifications to rule out breast ductal carcinoma in situ (DCIS). With advances in imaging technology and increased biopsy tissue volume, the detection rate of calcifications and DCIS in SCNB is unclear. METHODS: This retrospective study included 916 consecutive SCNBs for calcifications performed on 893 patients in a 2-year period. RESULTS: We found the cancer detection rate was 27.1% (DCIS, 23.7%; invasive, 3.4%). The detection rate for calcifications was 74.8% with the standard 3 levels. Additional leveling of calcification-negative cases further increased the detection of both calcifications (to 99.4% of cases) and DCIS (to 32.9% of cases). Lobular neoplasia (LN) was diagnosed in 41 cases. Twenty-five (61.0%) cases of LN were incidental without associated calcification. Of 32 invasive carcinomas detected on SCNB, 87.5% were T1a or less, and calcifications were associated with atypical ductal hyperplasia/DCIS or LCIS. The common benign lesions associated with calcifications were fibrocystic change (32.5%), fibroadenomatous change (30.2%), and columnar cell change and hyperplasia (8.2%). CONCLUSIONS: We determined the up-to-date detection rates of calcification and DCIS in SCNB, as well as the common benign and malignant breast lesions associated with calcifications. Additional levels significantly increase the detection rate when standard levels show only stromal or scant/absent calcifications. Lobular neoplasia is often an incidental finding in SCNB for calcifications. When calcifications are present with LN, they are commonly florid, pleomorphic LCIS, or with concurrent invasive carcinoma.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".