The Positive Outcome of MRI-Guided Vacuum Assisted Core Needle Breast Biopsies is not Influenced by a Prior Negative Targeted Second-Look Ultrasound
Bibliographic record
Abstract
PURPOSE: The study sought to investigate the outcome of breast magnetic resonance-guided biopsies as a function of the indication for magnetic resonance imaging (MRI), the MRI features of the lesions, and the performance or not of a targeted second-look ultrasound (SLUS) prior breast MRI-guided biopsy. METHODS: We identified 158 women with MRI-detected breast lesions scheduled for MRI-guided biopsy (2007-2013). Patient demographics, performance of targeted SLUS, imaging characteristics, and subsequent pathology results were reviewed. RESULTS: Three biopsies were deferred, and 155 lesions were biopsied under MRI guidance (155 women; median age 55.14 years; range 27-80 years). Ninety-eight women underwent a SLUS prior to the MRI-guided biopsy (63%). Of the 155 biopsied lesions, 23 (15%) were malignant, 106 (68%) were benign, and 26 (17%) were high risk. Four of 15 surgically excised high-risk lesions were upgraded to malignancy (27%). Most of the biopsied lesions corresponded to non-mass-like enhancement (81%, 126 of 155) and most of the biopsies (52%, 81 of 155) were performed in a screening context. No demographic or MRI features were associated with malignancy. No differences were noted between the 2 subgroups (prior SLUS vs no prior SLUS) except for the presence of a synchronous carcinoma associated with a likelihood of targeted SLUS before MRI-guided biopsy (P = .001). CONCLUSION: A negative SLUS does not influence the pathology outcome of a suspicious lesion biopsied under MR guidance.
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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 | 0.001 | 0.012 |
| 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 | 0.000 |
| 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".