Optimization of an implantable magnetic marker for surgical localization of breast cancer
Bibliographic record
Abstract
Abstract For small, early-stage or otherwise non-palpable breast tumors, surgeons rely on localization technologies to accurately find and remove the tumor tissue during breast conserving surgery. However, current widely accepted localization technologies either use painful and logistically challenging guidewires, or complex radioactive iodine sources. We have developed an implantable magnetic marker, intended to mark the location of a breast tumor, that can be detected during surgery using a clinical handheld magnetic susceptometry system. Here, we report on the development and optimization of this magnetic marker, focusing on the material, shape and various material assemblies. It was found that the effects of magnetic shape anisotropy may decrease localization precision. This can be circumvented by combining multiple isotropic magnetic elements separated from one another. A final optimized prototype was constructed and compared to a commercially available magnetic marker. Finally, the technology was tested in an ex vivo surgical setting on tissue to assess radiological visibility and surgical feasibility. The marker was successfully detected and removed in all ex vivo sessions, and the technology was found feasible.
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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 | 0.001 |
| 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) | 0.000 | 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 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".