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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".