Multimodality imaging findings of cardiac neuroendocrine tumour metastasis
Bibliographic record
Abstract
A 72-year-old man, diagnosed with well-differentiated neuroendocrine tumour (NET) of the ileum, was referred to our hospital for staging and treatment. At initial staging using 68Ga-DOTATATE positron emission tomography (PET)/computed tomography (CT) scan, multifocal DOTATATE-avid lesions were found in the skeleton, liver, and the anterior LV wall (A and B, arrow). Biopsy proved NET hepatic metastasis. Further evaluation with transthoracic echocardiography revealed a 22.5 × 21.1 mm lesion in the mid anterior LV wall with vascularity (F, arrow) without thickening or restriction of the tricuspid and pulmonary valves to suggest carcinoid heart syndrome. Subsequent cardiac magnetic resonance (CMR) imaging revealed normal biventricular size and global systolic function. An intra-myocardial solid lesion, measuring 21 × 20 × 14 mm, hyperintense on T2-W axial (C, arrow), isointense on short axis SSFP (E, arrowhead), and hypervascular on first-pass perfusion images (D, arrow), was demonstrated in the mid LV anterior wall. Given the concordance of findings from PET and CMR, a diagnosis of cardiac NET metastasis was confirmed. Neuroendocrine tumours with heart metastasis are very rare occurring in only 1.75% cases although are clinically relevant. The left ventricle is the most commonly involved cardiac chamber. While echocardiography has a low sensitivity for detecting cardiac NET metastasis, both CMR and 68Ga-DOTATATE PET have excellent accuracy for its diagnosis and monitoring. Interestingly, NET cardiac metastasis and carcinoid heart disease are two distinct entities; they coexist in 8% of reported cases. Images on this report would help identifying cardiac NET metastasis. Consent: The authors confirm that written consent for the submission and publication of this case, including images, has been obtained from the patient and his parents in line with the Committee on Publication Ethics (COPE) guidance. Funding: None declared. The data underlying this article will be shared on reasonable request to the corresponding author.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".