Technetium-99-Guided Axillary Lymph Node Identification: A Case Report of a Novel Technique for Targeted Lymph Node Excision Biopsy for Node Positive Breast Cancer After Neoadjuvant Chemotherapy
Bibliographic record
Abstract
Targeted axillary lymph node identification for breast cancer involves localization and removal of previously marked metastatic lymph nodes after the completion of neoadjuvant chemotherapy (NACT), when clinical and radiological complete responses of the axillary nodes are achieved. Traditionally, axillary lymph node dissection is performed for patients with node positive disease, but the high rates of pathological complete responses now seen after NACT have ushered in lower morbidity techniques such as sentinel lymph node excision biopsies, targeted axillary lymph node dissection and targeted axillary lymph node identification (clip node identification) in node positive disease which has converted to clinical/radiologically node negative. The latter two techniques often require the use of expensive seeds and advanced localization techniques. Here we describe the case of a 59-year-old woman who was diagnosed with node positive invasive breast cancer who was sequenced with NACT. We developed a novel technique, where technetium-99m was injected directly into a previously clipped metastatic axillary lymph node which was then localized with the Neoprobe gamma detection system intra-operatively and removed. This is a relatively low-cost technique that can be easily introduced in limited resourced health systems where radio-guided sentinel lymph node biopsies are already being performed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".