Exploring a Novel Composite Diagnostic Tool Using Non-contrast EUS Enhanced Cyst Wall Microvascular Imaging and Cyst Fluid Analysis to Differentiate Pancreatic Cystic Neoplasms
Bibliographic record
Abstract
Aims Differentiating pancreatic cystic lesions (PCLs) still remains a diagnostic challange. The use of high-definition imaging modalities which detect blood flow in tumor microvasculature have been described in solid lesions. We aim to evaluate the usefulness of cystic wall microvasculature when used in combination with cyst fluid biochemistry to differentiate PCLs [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ] [ 14 ] [ 15 ] [ 16 ] [ 17 ] [ 18 ] [ 19 ] [ 20 ] [ 21 ] [ 22 ] [ 23 ] [ 24 ] [ 25 ]. Methods We retrospectively analyzed 110 patients with PCLs from 2 Italian Hospitals who underwent EUS with H-FLOW and EUS fine needle aspiration. The accuracy of fluid biomarkers was evaluated against morphological features on radiology and EUS. Gold standard diagnosis was surgical resection; for all the other patients a radiological or endosonographic follow up was performed. Cysts fluid cut-off were assigned from previous literature: CEA>192(ng/ml), CA19.9>37(U/L), amylase>250(U/L), lipase>336(U/L), glucose<50(mg/dl). Results Of 110 patients, 65 had mucinous, 41 had non-mucinous neoplasms and 4 patients were excluded. Fluid analysis alone yielded 76.7% sensitivity, 56.7% specificity, 77.8 positive predictive value (PPV), 55.3 negative predictive value (NPV) and 56% accuracy in diagnosing pancreatic cysts. Our composite method yielded 97.3% sensitivity, 77.1% specificity, 90.1% PPV, 93.1% NPV, 73.2% accuracy. Conclusions Our composite method which utilizes high-definition microvasculature imaging on EUS is superior to that of the stand-alone analysis of cystic fluid. It can be applied to a holistic approach combining cyst morphology, vascularity and fluid analysis alongside endoscopist expertise ([ Fig. 1 ]). Fig. 1 Publication History Article published online: 14 April 2023 © 2023. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".