The histological spectrum and immunoprofile of head and neck NUT carcinoma: A multicentre series of 30 cases
Bibliographic record
Abstract
BACKGROUND AND AIM: Head and neck nuclear protein of testis carcinoma (HN-NUT) is a rare form of carcinoma diagnosed by NUT immunohistochemistry positivity and/or NUTM1 translocation. Although the prototype of HN-NUT is a primitive undifferentiated round cell tumour (URC) with immunopositivity for squamous markers, it is our observation that it may assume variant histology or immunoprofile. METHODS: We conducted a detailed clinicopathological review of a large retrospective cohort of 30 HN-NUT, aiming to expand its histological and immunohistochemical spectrum. RESULTS: The median age of patients with HN-NUT was 39 years (range = 17-86). It affected the sinonasal tract (43%), major salivary glands (20%), thyroid (13%), oral cavity (7%), larynx (7%), neck (7%) and nasopharynx (3%). Although most cases of HN-NUT (63%) contained a component of primitive URC tumour, 53% showed other histological features and 37% lacked a URC component altogether. Variant histological features included basaloid (33%), differentiated squamous/squamoid (37%), clear cell changes (13%), glandular differentiation (7%) and papillary architecture (10%), which could co-exist. While most HN-NUT were positive for keratins, p63 and p40, occasional cases (5-9%) were entirely negative. Immunopositivity for neuroendocrine markers and thyroid transcription factor-1 was observed in 33 and 36% of cases, respectively. The outcome of HN-NUT was dismal, with a 3-year disease specific survival of 38%. CONCLUSIONS: HN-NUT can affect individuals across a wide age range and arise from various head and neck sites. It exhibits a diverse spectrum of histological features and may be positive for neuroendocrine markers, potentially leading to underdiagnosis. A low threshold to perform NUT-specific tests is necessary to accurately diagnose HN-NUT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".