A328 A RETROSPECTIVE ANALYSIS ON SERUM CHROMOGRANIN-A LEVELS IN THE DIAGNOSIS OF PANCREATIC NEUROENDOCRINE TUMORS AT A CANADIAN INSTITUTE
Bibliographic record
Abstract
Pancreatic Neuroendocrine Tumors (PNET) are rare neoplasms, diagnosed via CT, MRI, Octreotide scan or EUS, which has the highest sensitivity & tissue obtaining ability. Management is based on resection, dependent on size & spread. Serum Chromogranin-A (CgA) is still widely used in diagnosis despite its lack of evidence. Hence, the North American Neuroendocrine Tumor Society (NANETS) and the Canadian expert group considers serum CgA diagnostic utility a controversial area and advise caution in its interpretation, requiring further studies in its validation Despite the widespread use of serum CgA in PNET diagnosis, we believe serum CgA has poor diagnostic utility Aims include: 1. Evaluate the diagnostic sensitivity of serum CgA in our cohort 2. Delineate different modalities utilized in tissue diagnosis Retrospective chart review of patients with a histological diagnosis of PNET from a pathology database covering Vancouver BC, Canada, from January 1st, 2011 till July 31st, 2016 Exclusion criteria: 1. Patients with a nonpancreatic primary neuroendocrine tumor 2. Patients with no CgA levels prior to diagnosis We correlated serum CgA levels, patient characteristics, disease manifestations and characteristics such as size and metastases, dianostic modalities used and treatments undertaken 143 patients with histological diagnosis of PNET. Mean age 60 & 58% females. EUS used in diagnosis of 90 (63%), surgical resection in 34 (24%) & remaining had percutaneous or intra-operative biopsy. Serum CgA prior to tissue diagnosis was performed in 60% (87) and had a sensitivity of only 48% (42/87), median of 109 U/L (normal <40 U/L) & 212 ug/L (normal <94 ug/L) from two different lab assays. Comparing CgA positive versus negative patients, no significant difference was found in location of PNET (most commonly in the tail in 33% & 37% of positive & negative patients respectively, 95% CI -17.3–24.8%, p=0.69). A significant difference was found in the size of the lesion in patients with a positive CgA as compared with the negative group (mean 3.31cm vs. 2.32cm; 95% CI 0.11–1.86, p=0.02). A significant proportion of CgA positive patients had metastatic disease as compared to CgA negative patients (38% vs. 15%; 95% CI 2.97–41.53, p=0.015) PNET are rare neoplasms, usually diagnosed via EUS. We corroborated this with approximately two thirds of our cohort undergoing EUS sampling for diagnosis. Serum CgA, although thought to have some diagnostic utility, has a very low sensitivity with less than half of our cohort of patients having positive CgA levels. Hence, CgA levels should not be used as a diagnostic modality in PNETs, and if negative, should be interpreted cautiously. CgA levels may however, have a utility in helping identify metastatic disease and thus altering surgical management None
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".