Preoperative Lymphoscintigraphy for Breast Cancer Does Not Improve the Ability to Identify Axillary Sentinel Lymph Nodes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the role of preoperative lymphoscintigraphy in sentinel lymph node (SLN) biopsy for breast cancer. SUMMARY BACKGROUND DATA: Numerous studies have demonstrated that SLN biopsy can be used to stage axillary lymph nodes for breast cancer. SLN biopsy is performed using injection of radioactive colloid, blue dye, or both. When radioactive colloid is used, a preoperative lymphoscintigram (nuclear medicine scan) is often obtained to ease SLN identification. Whether a preoperative lymphoscintigram adds diagnostic accuracy to offset the additional time and cost required is not clear. METHODS: After informed consent was obtained, 805 patients were enrolled in the University of Louisville Breast Cancer Sentinel Lymph Node Study, a multiinstitutional study involving 99 surgeons. Patients with clinical stage T1-2, N0 breast cancer were eligible for the study. All patients underwent SLN biopsy, followed by level I/II axillary dissection. Preoperative lymphoscintigraphy was performed at the discretion of the individual surgeon. Biopsy of nonaxillary SLNs was not required in the protocol. Chi-square analysis and analysis of variance were used for statistical comparison. RESULTS: Radioactive colloid injection was performed in 588 patients. In 560, peritumoral injection of isosulfan blue dye was also performed. A preoperative lymphoscintigram was obtained in 348 of the 588 patients (59%). The SLN was identified in 221 of 240 patients (92.1%) who did not undergo a preoperative lymphoscintigram, with a false-negative rate of 1.6%. In the 348 patients who underwent a preoperative lymphoscintigram, the SLN was identified in 310 (89.1%), with a false-negative rate of 8.7%. A mean of 2.2 and 2. 0 SLNs per patient were removed in the groups without and with a preoperative lymphoscintigram, respectively. There was no statistically significant difference in the SLN identification rate, false-negative rate, or number of SLNs removed when a preoperative lymphoscintigram was obtained. CONCLUSIONS: Preoperative lymphoscintigraphy does not improve the ability to identify axillary SLN during surgery, nor does it decrease the false-negative rate. Routine preoperative lymphoscintigraphy is not necessary for the identification of axillary SLNs in breast cancer.
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 | 0.006 |
| 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) | 0.003 | 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".