Sentinel Node Biopsy in Laryngeal Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Sentinel lymph node (SLN) biopsy offers a minimally invasive approach to staging lymph node involvement in laryngeal squamous cell carcinoma (SCC). Despite its adoption in other cancers, its accuracy in laryngeal SCC remains under investigation. This systematic review and meta-analysis evaluates the diagnostic performance of SLN mapping in laryngeal cancer. Methods: A systematic search of MEDLINE, Scopus, and Google Scholar was conducted using the keywords “(larynx OR laryngeal) AND sentinel”, with no date or language restrictions. Studies reporting SLN detection rates and/or sensitivity in laryngeal SCC were included. A random-effects model was applied for data pooling, and subgroup analyses were performed based on tumor location (supraglottic versus transglottic) and mapping material (radiotracer versus blue dye). Publication bias was assessed using funnel plots and statistical methods. Results: Nineteen studies, encompassing 366 patients, were analyzed. The overall pooled SLN detection rate was 90.8% (95% CI: 86–94.1), and sensitivity was 88% (95% CI: 81–94). Supraglottic tumors demonstrated superior outcomes (detection rate: 93.7%, sensitivity: 96%) compared to transglottic tumors (detection rate: 84.7%, sensitivity: 71%). Radiotracers significantly outperformed blue dye, with detection rates of 90.8% versus 81.5% and sensitivities of 88% versus 77%. Conclusions: SLN mapping is a reliable technique for staging laryngeal SCC, particularly for supraglottic tumors, where high detection rates and sensitivity were observed. Radiotracers offer superior performance compared to blue dye, underscoring their clinical value. These findings support the feasibility and accuracy of SLN biopsy in laryngeal cancer, while emphasizing the importance of tumor location and mapping material.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".