Intraoperative hepatic ultrasonography in patients with gastrointestinal and pancreatic neuroendocrine neoplasms without preoperative evidence of liver metastases
Bibliographic record
Abstract
In patients with gastroenteric and pancreatic neuroendocrine neoplasms (GEP-NENs), the risk of liver metastases is high, but the accuracy of standard imaging for detecting small hepatic nodules is limited. This raises concerns about the adequacy of staging in cM0 patients. This study aims to determine the percentage of cM0 GEP-NEN patients with occult liver metastases that can be identified using intraoperative ultrasound (IOUS) during surgery for the primary tumor. A prospective study was conducted at three high-volume centers between October 2020 and December 2023. Patients who underwent surgery for GEP-NENs staged as cM0 based on CT and PET/CT (MRI in selected cases) were included. IOUS was systematically performed, in combination with contrast-enhanced IOUS (CE-IOUS) and biopsy when necessary. The ground truth was the result of a contrast-enhanced CT or MRI performed 6 months after surgery. A total of 51 cM0 GEP-NEN patients were enrolled. IOUS detected suspicious liver nodules in seven patients (14%). Three were classified as metastatic based on the IOUS pattern, CE-IOUS pattern, and biopsy, respectively, while four were classified as non-metastatic based on biopsy (n = 2) or CE-IOUS pattern (n = 2). At six-month follow-up imaging, two patients (4%) were confirmed as metastatic (the suspicious metastasis at CE-IOUS was not confirmed). GEP-NENs G3 and those with necrosis had a higher risk of metastases (one-third of patients, p < 0.05 compared to G1-2 and non-necrotic neoplasms). IOUS, combined with CE-IOUS and biopsy, achieved 100% sensitivity, 98% accuracy, and 100% negative predictive value. IOUS, along with CE-IOUS and biopsy, provides accurate staging in GEP-NENs, identifying occult metastases in approximately 4% of cM0 cases. Due to the low incidence of occult metastases, routine use of IOUS cannot be recommended. It should be considered selectively in aggressive tumors, which have shown a significantly higher risk of liver involvement.
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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 | 0.002 |
| 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) | 0.001 | 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".