Contrast-enhanced ultrasound as a non-invasive diagnostic modality for pancreatic ductal adenocarcinoma: The question of Ki67 for study validation
Bibliographic record
Abstract
This editorial comments on Yang et al ’s article that reported a correlation between dynamic contrast-enhanced ultrasound (CEUS) quantitative parameters and Ki67/tumor differentiation. The validation of CEUS as a diagnostic modality in this study deserves merit. However, it raises interesting points of discussion: (1) Since pancreatic cancer is an overarching term that includes conventional pancreatic ductal adenocarcinoma (PDAC), other subtypes, and neuroendocrine neoplasms (NENs), the inclusion/exclusion criteria require better clarification; (2) Most PDACs are grade 1-2 which contrasts with Yang et al ’s study where 46% were grade 3; (3) Ki67 is officially recognized for grading NENs, but not for PDAC; (4) Hotspots are selected for the Ki67 grading of NENs. However, for other tumors (e.g., breast carcinoma), the average count or hotspots are used; (5) There is no agreement for defining high-grade Ki67 cut-off for non-NENs; reports range from 10% to 50%; and (6) Ki67 reflects cellular proliferation but is not always the most important indicator for biologic aggressiveness. That notwithstanding, since the ratification of Ki67 for prognosis in NENs was based on survival outcomes, the real gold standard should be survival, instead of using Ki67 as a surrogate gold standard. In conclusion, the validation of CEUS parameters for PDAC is a work in progress. CEUS is valuable in assessing PDAC but should be viewed as augmenting other modalities such as computed tomography, magnetic resonance imaging, positron emission tomography and endoscopic ultrasound.
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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 | 0.052 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".