Quantitative contrast-enhanced endoscopic ultrasound in pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors: can we predict survival using perfusion parameters? A pilot study.
Bibliographic record
Abstract
AIM: Contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS) parameters may be used to predict prognosis of pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (pNET). The aim of this study was to investigate the association between several perfusion parameters on CEH-EUS performed before treatment and survival outcome in patients with PDAC or pNET. MATERIAL AND METHODS: Thirty patients with PDAC or pNET who underwent CEH-EUS and EUS-guided fine needle aspiration (EUS-FNA) were included. Quantitative analysis of tumor vascularity was performed using time-intensity curve (TIC) analysis-derived parameters, obtained from processing CEH-EUS recordings with a commercially available software (VueBox). Cox proportional hazards models were used to determine associations with survival outcome. RESULTS: Median overall survival (OS) for PDAC patients was 9.61 months (95% CI: 0.1-38.7) while the median OS for pNET patients was 15.81 months (95% CI: 5.8-24.75. In a multivariate model for OS, a lower peak enhancement (HR=1.76, p=0.02) and a lower wash-in area under the curve (HR=1.06, p=0.001) were associated with worse survival outcome for patients with PDAC. CONCLUSIONS: CEH-EUS parameters may be used as a surrogate to predict PDAC aggressiveness and survival before treatment. After validation by large-scale studies, CEH-EUS perfusion parameters have the potential to be used in pretreatment risk stratification of patients with PDAC and in evidence-based clinical decision support.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".