A sensitive biomarker panel to distinguish pancreaticobiliary malignancies from benign disease.
Bibliographic record
Abstract
238 Background: Biliary strictures present a diagnostic challenge to differentiate benign disease from hepatopancreaticobiliary (HPB) malignancies. Cytology from Endoscopic retrograde cholangiopancreatography (ERCP) or Endoscopic ultrasound with fine needle aspiration (EUS-FNA) have both been plagued by poor sensitivity and high false negative rates. In a review of over 80 adjunct molecular biology tests that have been attempted on ERCP and EUS-FNA samples, four immunohistochemistry biomarkers with sensitivities ranging 74-80% were identified; von Hippel Lindau loss of expression (VHL), over-expression of insulin-like growth factor 2 mRNA-binding Protein 3 (IMP3), and EF-hand Calcium 2+ binding S100 subfamily members A4 (S100A4) and P (S100P). We sought to determine if these results could be validated in a tumor explant model and furthermore if combining these tests into a biomarker panel could boost overall diagnostic sensitivity to 100%. Methods: Tumor tissue and normal surrounding pancreas from 27 pancreaticoduodenectomy specimens were selected by an experienced pathologist, subjected to immunohistochemistry staining with VHL, IMP3, S100A4, S100P, and the intensity and percent of cells staining was graded. Using ROC curve analysis, threshold criteria were chosen for each biomarker to differentiate between tumor and normal pancreas. Biomarkers were then evaluated as a panel for their ability to discriminate malignant from benign specimens. Results: Individual sensitivity of VHL, IMP3, S100A4, and S100P were found to be 75.0%, 79.2%, 45.8%, and 0%. When VHL, IMP3, and S100A4 were grouped into a panel, they were able to distinguish cancer from normal tissue with a sensitivity of 100% and a specificity of 96%. S100P was dispensable in our assay and only VHL, IMP3, and S100A4 staining were required to achieve 100% sensitivity. Conclusions: A panel of three biomarkers, VHL, IMP3, and S100A4, were able to distinguish pancreatic cancer from surrounding normal tissue with 100% sensitivity in our tumor explant model. Prospective studies on patient biopsy specimens are required to further validate the clinical use of this biomarker panel.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".