Abstract 5104: Serum metabolomic profiles acquired by gas chromatography-mass spectrometry (GC-MS) distinguish patients with pancreatic adenocarcinoma from those with benign pancreatic disease
Bibliographic record
Abstract
Abstract Introduction: Pancreatic adenocarcinoma is often difficult to accurately distinguish from benign pancreatic lesions such as pancreatitis. Accurate preoperative identification of patients with benign disease may reduce the number of highly invasive and costly pancreatic resections conducted in this group. This study aims to identify differences in the metabolomic profile of serum between patients with pancreatic adenocarcinoma versus benign pancreatic disease. Methods: Fasting serum samples were collected, as part of an institutional biorepository program (IRB#E20846), from patients with pancreatic adenocarcinoma or benign pancreatic disease. Accompanying clinical data were collected prospectively. Gas chromatography-mass spectrometry (GC-MS) spectra were acquired for aqueous metabolites and analyzed using multivariate methods (orthogonal partial least squares-discriminant analysis, OPLS-DA) using SIMCA-P+ (V12.0.1) software. Metabolite identification was conducted using the TargetSearch approach. Results: Of the 136 included patients, 101 had pancreatic adenocarcinoma and 35 had benign pancreatic disease (26 pancreatitis/pseudocyst, 7 serous cystic neoplasm, 2 other). Median patient age was 66 years, 51.4% were male, 22.8% were jaundiced, and 79.4% presented with a common bile duct stricture or pancreatic mass. The metabolomic profile of serum from patients with pancreatic adenocarcinoma was significantly different from that of patients with benign pancreatic disease based on OPLS-DA modelling (p=0.0004). Overall, the profile contained 168 targeted metabolites, of which 14 were significantly different between malignant and benign pancreatic disease on multivariate modeling. Conclusion: The serum metabolomic profiles of pancreatic adenocarcinoma and benign pancreatic disease differ significantly. Further analysis of these differences may yield a novel serum test to distinguish these lesions in clinical practice. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5104. doi:10.1158/1538-7445.AM2011-5104
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".