Challenging Contrast-Enhanced Mammography–Guided Biopsies: Practical Approach Using Real-Time Multimodality Imaging and a Proposed Procedural Algorithm
Bibliographic record
Abstract
Contrast-enhanced mammography (CEM) is an emerging functional breast imaging technique that entails the acquisition of dual-energy digital mammographic images after IV administration of iodine-based contrast material. CEM-guided biopsy technology was introduced in 2019 and approved by the U.S. FDA in 2020. This technology's availability enables direct sampling of suspicious enhancement seen only on or predominantly on recombined CEM images and addresses a major obstacle to the clinical implementation of CEM technology. The literature describing clinical indications and procedural techniques of CEM-guided biopsy is scarce. This article describes our initial experience in performing challenging CEM-guided biopsies and proposes a step-by-step procedural algorithm designed to proactively address anticipated technical difficulties and thereby increase the likelihood of achieving successful targeting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".