Study into the displacement of tumor localization needle during navigated breast cancer surgery
Bibliographic record
Abstract
PURPOSE: Early stage breast cancer is typically treated with lumpectomy. During lumpectomy, electromagnetic tracking can be used to monitor tumor position using a localization needle with an electromagnetic sensor fixed on the needle shaft. This needle is stabilized in the tumor with tissue locking wire hooks, which are deployed once the needle is inserted. The localization needle may displace from its initial position of insertion due to mechanical forces, providing false spatial information about the tumor position and increasing the probability of an incomplete resection. This study investigates whether gravitational and mechanical forces affected the magnitude of needle displacement. METHODS: Ten ultrasound scans were evaluated to measure needle displacement in vivo. Needle position was approximated by the distance between the needle tip and the tumor boundary on a 2D ultrasound image, and needle displacement was defined by the change in position. The angle between the localization needle and the coronal plane was computed in an open-source platform. RESULTS: A significant relationship (p = 0.04) was found between the needle to coronal plane angle and increased needle displacement. Needles inserted vertically, pointing towards the operating room ceiling, tended to exhibit greater needle displacement. Average needle displacement was 1.7 ±1.2 mm. CONCLUSION: Angle between the needle and the horizontal plane has been shown to affect needle displacement, and should be taken into consideration when inserting the localization needle. Future works can be directed towards improving the clinical workflow and mechanical design of the localization needle to reduce slippage during surgery.
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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 | 0.000 | 0.003 |
| 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".