Non-visualization of axillary pathological lymph nodes in breast cancer patients on SPECT/CT and during operation
Bibliographic record
Abstract
Background: Recent studies have shown that an increased number of axillary lymph node metastases is associated with non-visualized lymph nodes. The purpose of the study was to retrospectively analyze the incidence and characteristics of non-visualized sentinel lymph nodes (SLNs) in nodal metastases in breast cancer patients. Methods: Consecutive women with breast cancer referred for lymphoscintigraphy from January 2021 to November 2022 were reviewed retrospectively. Findings from resected SLNs and non-SLNs and relevant histopathology were collected and analyzed. Results: 500 patients diagnosed with breast cancer were reviewed, excluding 93 patients due to neoadjuvant therapy, DCIS, recurrence, or incomplete clinical documentation. Of the 407 remaining patients, 108 patients were positive for axillary lymph node metastases (24 %) and were the focus of the study. Of this patient cohort, 38 patients (35 %) had non-detected SLNs by intraoperative gamma probe and 43 (40 %) had non-visualized SLNs by lymphoscintigraphy. There was statistically significant difference in primary tumor size (39.8 mm versus 28.9 mm), number of resected (6.9 ± 4.4 versus 4.6 ± 2.4) and positive (3.4 ± 2.2 versus 1.6 ± 1.3) lymph nodes, size (13.8 ± 6.1 mm versus 8.1 ± 4.5 mm), tumor grade and tumor stage between the SLN non-visualized and visualized groups. The multivariate logistic regression analysis showed that only lymph node size and number of lymph nodes resected were independent factors associated with SLN non-visualization. Conclusions: We reported a high non-visualization rate of SLN in breast cancer patients with pathology-proven positive axillary nodes. The causes of the SLN non-visualization are not well understood and warrants further exploration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".