Localization procedure for breast lesions at time of biopsy – Which patients would benefit?
Bibliographic record
Abstract
BACKGROUND: The diagnosis and treatment of non-palpable breast lesions is a multistep pathway that can involve imaging, tissue biopsy, clip placement, localization, and surgical resection. To minimize the procedural burden on patients, placement of localization seeds at time of initial biopsy has been considered. However, benefit to this patient population remains unclear. This study, therefore, aimed to determine the number of patients within our own institution that may benefit from upfront localization and characterize an appropriate target population. METHODS: A single institution retrospective cross-sectional study was conducted with assessment of all patients who underwent core needle biopsy (CNB) and/or breast cancer surgery at a regional high-volume breast centre between January 1 and December 31, 2018. Patients who underwent CNB with a subsequent radiological localization procedure and breast cancer surgeries that utilized localization were evaluated in order to model seed use. RESULTS: In total, 314 CNB and 634 breast cancer surgeries were performed. Within the CNB cohort, 60 (19.1 %) required localization. Of the breast cancer surgeries performed, 420 (66.2 %) were breast-conserving surgery and 303 (47.8 %) required localization. CONCLUSION: With some localization technologies, the localization procedure can be coupled with biopsy and eliminate the need for a clip as the length of implantation is unrestricted. That said, our institutional data suggests that only a small portion of patients undergoing breast biopsy would benefit from upfront localization and a minority of breast cancer surgeries require localization. Further characterization of this specific patient population is needed to streamline management pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".