Surgical planning of small intestine neuroendocrine tumors: the concept of mesenteric tumor deposits
Bibliographic record
Abstract
Abstract The mesenteric extension of small neuroendocrine tumors is the surgical limiting factor because of the risk of postoperative short bowel syndrome due to superior mesenteric artery involvement. Recent pathological studies have shown that this vascular involvement is due to mesenteric tumor deposits, differentiated from lymph node metastases. The aim of this study was to evaluate the performances of computed tomography (CT) for the surgical planning of small intestine neuroendocrine tumors. This was a retrospective observational study, and all patients undergoing surgery for small intestine neuroendocrine tumor between January 2014 and March 2019 were included. Preoperative CTs were reviewed, blinded from surgical and pathological data, by two radiologists. Diagnostic accuracy and interobserver reliability analysis were performed. We included 45 patients (mean age: 61 years (28–84 years); 23 men). The CT sensitivity to identify the mesenteric mass was 97% (37/38) with a ĸ of 0.73. The positive predictive value of CT to anticipate a right colic resection was 86% (18/21). The negative predictive value of CT was high (97% (34/35) to 100% (35/35)) for duodenal resection (ĸ = 0.78). Regarding retropancreatic lymph node invasion, the CT sensitivity was poor (24%, 4/17), with a high ĸ (0.88). The level of involvement by the mesenteric mass was correlated with the length and the percentage of the remaining small bowel. CT is essential for the surgical planning of small intestine neuroendocrine tumors, being accurate in defining the mesenteric tumor deposits, allowing one to anticipate, with a good reproducibility, the length and percentage of the remaining small bowel and the necessity for a right colectomy.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".