Sentinel lymph node biopsy guided by indocyanine green dye in endometrial cancer patients: literature review and clinical experience
Bibliographic record
Abstract
The purpose of the study was to assess the feasibility and safety of using the technique of intraoperative sentinel lymph node (slN) detection guided by indocyanine green dye in endometrial cancer patients. Material and methods. The study included 10 patients with stage i–ii/g1–2 endometrial cancer. the mean age of the patients was 66.2 years. laser-assisted indocyanine green (icg) fluorescent dye was used for slN mapping. icgwas injected into the uterine cervix immediately before surgery. the concentration used for icgwas 1.25 mg/ml (a 25 mg vial with icgpowder was diluted into 20 cc of aqueous sterile water. icgwas injected intracervically at 3 and 9 o’clock positions: 0.5 ml at 5 mm depth and 0.5 ml at 10 mm depth. the pinpoint endoscopic fluorescence imaging system (Novadaq, canada) was used for slN mapping. Results. SLNs were identified and removed in all patients. SLNs were located along the external iliac vessels in 6 patients and in the obturator fossa and in the hypogastric vessels in 4 patients. an additional para-aortic lymph node was visualized in one case. Both urgent and planned histological examinations showed no evidence of metastases in all patients. pelvic lymph node dissection was performed in 4 patients. combined pelvic and para-aortic lymph node dissection was performed in one case. Conclusion. Lymph node involvement is an important prognostic factor in deciding whether neoadjuvant chemotherapy is necessary for endometrial cancer patients. the technique of slN detection using indocyanine green dye appears promising in ensuring adequate intraoperative staging and optimization of the extent and duration of surgery. the use of this algorithm for the treatment of endometrial cancer will significantly reduce the risk of perioperative complications associated with lymphadenectomy, not negatively influencing the disease outcome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".