International Consensus Recommendations of Diagnostic Criteria and Terminologies for Extranodal Extension in Head and Neck Squamous Cell Carcinoma: An HN CLEAR Initiative (Update 1)
Bibliographic record
Abstract
PURPOSE: Extranodal extension (ENE) increases the risk of recurrence and death in head and neck squamous cell carcinoma (HNSCC) patients and is an indication for treatment escalation. Histopathology forms the mainstay of diagnosing ENE. There is substantial variation in the diagnosis of ENE and related terminology. Harmonising the diagnostic criteria for ENE was identified as a priority by the Head and Neck Consensus Language for Ease of Reproducibility (HN CLEAR) Steering Committee and its global stakeholders. METHODS: An international working group including 16 head and neck pathologists from eight countries across five continents evaluated whole slide images of haematoxylin and eosin-stained sections depicting potential diagnostic problems through nine virtual meetings to develop consensus guidelines. RESULTS: ENE should be diagnosed only when viable carcinoma extends through the primary lymph node (LN) capsule and directly interacts with the extranodal host environment with or without desmoplastic stromal response. Identifying the original LN capsule and reconstruction of its contour can assist in the detection and assessment of ENE. The term matting is recommended for confluence of two or more nodes due to histologically identifiable tumour extending from one LN to another. Matting constitutes major form of ENE. On the other hand, the terms fusion/adhesion/confluence/conglomeration and other synonyms of adhesion should be limited to confluence due to fibrosis or inflammation without histologically identifiable tumour between involved lymph nodes. Tumour extension along narrow needle tracks or spillage of cyst contents following an FNA do not constitute ENE. CONCLUSIONS: The consensus recommendations encompassing the definition of ENE, macroscopic and histologic examination of lymph nodes (LN) and practical guidelines for handling challenging cases are provided.
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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 | 0.053 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".