Annals of surgical oncology
October 16, 2026 · via Europe PMC
The Use and Utility of Preoperative Somatostatin Receptor-Based Imaging in Gastroenteropancreatic Neuroendocrine Tumors.
Chang J, Toliver B, Borbon LC, +6 more, Howe JR
AI summary — for doctors, generated from the abstract only
Preoperative functional imaging became more common over 24 years for small bowel neuroendocrine tumors but not pancreatic ones, and somatostatin receptor imaging was very helpful for finding distant disease.
What was studied
Retrospective single-institution study (2000 to 2023) of surgically treated small bowel and pancreatic neuroendocrine tumor patients, tracking use of preoperative functional imaging and the location of metastases identified.
Key findings
- The study included 392 small bowel and 208 pancreatic NET patients.
- Preoperative functional imaging was done in 248 (63.3%) small bowel patients (158 SSTR PET and 90 Octreoscan) and 113 (54.3%) pancreatic patients (47 SSTR PET and 66 Octreoscan).
- Small bowel use rose about 2.5% per year, with no significant rise in pancreatic NETs.
Clinical relevance
SSTR-based imaging supports staging, operative planning, and management decisions, consistent with guideline recommendations for routine use.
Who should care
Limitations
The abstract is cut off in the results, so the performance comparison between imaging types is not described. It is a single-institution retrospective review, so practice patterns may not generalize.
Functional imaging is increasingly used and useful in NET surgery, though adoption in pancreatic NETs lagged.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
BACKGROUND
Contemporary gastroenteropancreatic neuroendocrine tumor (GEPNET) management guidelines recommend routine use of functional imaging preoperatively, but there are few data highlighting trends in functional imaging adoption. This report reviews 24 years of neuroendocrine tumor (NET) experience to describe changes in rates of preoperative functional imaging use and highlights performance differences between functional imaging types in small bowel NETs (SBNET) and pancreatic NETs (PNETs).
METHODS
A retrospective single-institution study from 2000 to 2023 identified patients with SBNETs and PNETs treated with surgical resection, cytoreduction, or both who underwent preoperative functional imaging. The primary measurements were frequency of functional imaging use and location of metastases identified on preoperative imaging.
RESULTS
The study included 392 SBNET and 208 PNET patients. Preoperative functional imaging was performed for 248 (63.3%) SBNET patients (158 somatostatin receptor [SSTR] positron emission tomography [PET] and 90 Octreoscan) and 113 (54.3%) PNET patients (47 SSTR PET and 66 Octreoscan). There was a significant positive trend over time in functional imaging use in SBNET patients, with an approximate 2.5% yearly increase (p < 0.01) but no significant change in PNET patients during the course of the study (p = 0.82). At presentation, 82.3% (204/248) of the SBNET patients and 54.9% (62/113) of the PNET patients had metastatic disease. Compared with Octreoscan, SSTR PET more frequently identified bone and retroperitoneal nodal metastases.
CONCLUSION
Rates of functional imaging use in SBNET patients but not in PNET patients increased significantly during the last 24 years. Patients with SBNETs and PNETs frequently presented with metastatic disease, and SSTR-based functional imaging was extremely helpful in identifying distant disease, intra-operative planning, and further management.
DOI
10.1245/s10434-026-20735-9
PMID
42886322
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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