Lymphoepithelial Carcinoma of the Salivary Gland: A Systematic Review and Single Institution Experience
Bibliographic record
Abstract
OBJECTIVE: Lymphoepithelial carcinoma of the salivary gland (LECSG) is a rare malignancy. The purpose of this study is to evaluate the clinical features of LECSG with a focus on the role of Epstein-Barr virus (EBV). DATA SOURCES: PubMed, Embase, Web of Science, institutional pathology Natural Language search tool. REVIEW METHODS: Retrospective review of patients diagnosed with LECSG at a single institution and a systematic literature review. Full text articles in English were included. RESULTS: 285 cases of LECSG identified via literature review and an additional 10 within our institution. Most tumors were in the parotid gland (70.5%). Lymph node metastasis was observed in 28%. Most cases were in Asian or Inuit patients (70.5%). EBV association was positive in 52%, and 87.6% of these were in endemic populations. Treatment primarily involved surgical resection (65.8%), often combined with adjuvant therapy (50.2%). No significant differences in disease-free survival (DFS) or disease-specific survival (DSS) were observed by EBV status. Advanced tumor stage was associated with worse DFS (P < .0001) and worse DSS (P < .0001). Lymph node metastasis was also associated with reduced DSS (P = .04). Independent predictors of poorer DFS and DSS included late tumor and endemic region of origin. CONCLUSION: This comprehensive analysis of 295 salivary gland LEC cases provides valuable insights into the clinical behavior, pathological features, and treatment outcomes of this rare malignancy. The study highlights the potential role of EBV in tumor pathogenesis, particularly in endemic populations, and emphasizes the need for further research to establish optimal management strategies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".