Clinico-radiological findings of neuroendocrine tumour metastases to the orbit
Bibliographic record
Abstract
PURPOSE: We present the clinico-radiological findings of neuroendocrine tumour metastases to the orbit. METHODS: This was a multicentre, retrospective study of patients with neuroendocrine tumour metastases to the orbit. Data was collected from medical records across five different sites within Australia and the United Kingdom. RESULTS: Nine patients (eleven lesions) were identified. The most common presenting complaint was diplopia (5/9, 56%). Disease occurred bilaterally in two patients. Seven patients (78%) had extraocular muscle involvement. The lateral recti (4/9, 44%) and superior recti (2/9, 22%) were the most commonly affected. Ocular presentation preceded primary tumour diagnosis in three patients (33%). On orbital imaging, metastases were most commonly reported as well circumscribed, ovoid or round, heterogeneous, contrast-enhancing masses. Features of intralesional haemorrhage and bony invasion are uncommonly reported. CONCLUSIONS: Neuroendocrine tumour metastasis to the orbit is uncommon. Metastases have a propensity for the extraocular muscles, commonly presenting as heterogeneous, well circumscribed, contrast-enhancing lesions on neuroimaging. New ocular symptoms, a history of neuroendocrine tumours, and these radiological findings, should lead to high clinical suspicion of metastatic disease. Atypical findings warrant biopsy to exclude other causes of orbital lesions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".