Unveiling Tissue Marker Migration in MRI-Guided Vacuum-Assisted Biopsies: Frequency, Factors, and Implications
Bibliographic record
Abstract
Abstract Purpose To evaluate the frequency and factors associated with clip migration in MRI-guided breast biopsies. Methods and materials We retrospectively evaluated all MRI-guided biopsies performed between January 2013 and December 2020 in our institution for clip migration. Only patients with follow-up breast MRI showing the clip were included in the study. Migration was defined as > 1 cm movement from the target lesion. Migration frequency and directions were recorded. Different factors associated with clip migration were analyzed using statistical tests as appropriate. Results A total of 291 biopsies in 268 women were included in the study with 31 migration events recorded (11%, 95%CI 7–15). All migrations occurred along the biopsy tract, 30 of them distal from the tip of the needle (97%). More than 50% regional fat (around the target lesion) was the strongest factor associated with migration, seen in 21/140 (15%), compared to 10/150 (7%) with less than 50% local fat (P = 0.023). Global fatty breast was more loosely associated with migration, showing borderline significance (P = 0.06). Other factors did not correlate with clip migration, including lesion size, depth or location, pathology result, breast thickness or biopsy approach. Conclusion Although clip migration following breast MRI-guided biopsy is an uncommon event, it occurs more often when the target lesion is surrounded by fat, with the clip usually displaced away from the biopsy needle. This information could be valuable for pre-surgical localization and surgical planning.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.000 |
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".