Role of Imaging in the Preoperative Staging of Small Bowel Neuroendocrine Tumors
Bibliographic record
Abstract
BACKGROUND: Imaging studies are important in the preoperative staging of patients with small bowel neuroendocrine tumors (NET) and when selecting patients for cytoreduction procedures for metastatic disease. The purpose of this study was to assess the accuracy of preoperative imaging compared with operative findings in the staging of small bowel NET. STUDY DESIGN: Sixty-four patients with small bowel NET undergoing laparotomy and who had preoperative imaging with combinations of CT, MR, and radionuclide scintigraphy were reviewed. Results of imaging studies were compared with operative findings to assess the ability of these investigations to detect mesenteric, peritoneal, and hepatic metastases. RESULTS: Mesenteric nodal metastases were seen on imaging in 47 (73%) patients and were present at laparotomy in 56 (88%) patients. Peritoneal metastases were seen on preoperative imaging in 4 (6%) patients and found at laparotomy in 16 (25%) patients. Hepatic metastases were seen on imaging in 42 patients (66%) and found at laparotomy in 49 (77%). Sensitivity and specificity for detection of hepatic metastases were 77% and 100% for CT, 82% and 100% for MR, 63% and 100% for (123)I-meta-iodobenzylguanadine scintigraphy, and 63% and 100% for (111)In-octreotide. Imaging studies failed to detect hepatic metastases in 7 patients and underestimated the extent of hepatic metastatic disease in 17 patients. CONCLUSIONS: Imaging of small bowel NET, even with combinations of CT, MR, and radionuclide studies, underestimates the extent of peritoneal, mesenteric, and hepatic metastatic disease. Accurate staging of small bowel NET might be best performed at the time of laparotomy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".