Neuroendocrine tumor chromogranin A response following synthetic somatostatin analog (lanreotide): Early observations from an isolated duodenal neoplasm .
Bibliographic record
Abstract
Neuroendocrine tumors (NETs) of duodenal origin are an unusual subset among all NETs, comprising only about 3% of this neoplasm class. In general, NETs are characterized by overexpression of somatostatin receptors and carry an excellent prognosis with early diagnosis and intervention. Chromogranin A (CgA), a protein originating in secretory vesicles of neurons and endocrine cells, has gained wide usage in NET diagnosis and surveillance. Lanreotide is a synthetic octapeptide somatostatin analog with potent anti-proliferative action which has been approved by the FDA (U.S.) and EMA (E.U.) for NET treatment. It is known for its inhibitory effects on growth hormone, serotonin, CgA, and other markers. Here we describe a 56yr-old female with functional NET of duodenal origin, where serum CgA was successfully reduced from 3636 to <100 ng/mL after multidose lanreotide within five months. Of note, no metastatic spread was identified on positron emission tomography/computed tomography with 64Cu-labeled somatostatin analog tracer. Surgical resection of distal antrum, pylorus, and proximal duodenum was completed without complication. Histology revealed well-differentiated tumor cells with characteristic neuroendocrine features and clear surgical margins; low proliferation index (2%) was noted on Ki-67 staining. While select laboratory and imaging modalities are available for diagnosis and monitoring of duodenal NET, this is the first reported therapeutic use of lanreotide in this NET setting. The observed serum chromogranin A attenuation, even before surgery, supports its effectiveness in management of primary nonmetastatic duodenal NET after resection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".