Regression grading in neoadjuvant treated pancreatic cancer: an interobserver study
Bibliographic record
Abstract
AIM: Several regression grading systems have been proposed for neoadjuvant chemoradiation-treated pancreatic ductal adenocarcinoma (PDAC). This study aimed to examine the utility, reproducibility and level of concordance of three most frequently used grading systems. METHODS: Four gastrointestinal pathologists used the College of American Pathologists (CAP), Evans, MD Anderson Cancer Centre (MDA) regression grading systems to grade 14 selected cases (7-20 slides from each case) of neoadjuvant chemoradiation-treated PDAC. A postscoring discussion with each pathologist was conducted. The results were entered into a standardised data collection form and statistical analyses were performed. RESULTS: There was little concordance across the three systems. The Kendall coefficient of concordance agreement scores were: CAP: 2-poor, 2-fair; Evans: 1-fair, 1-moderate, 2-good; MDA: 1-poor, 2-moderate, 1-good. Interpretation in all three grades in the CAP grading system was a source of discrepancy. Furthermore, using fibrosis as a criterion to assess regression was contentious. In the Evans system, quantifying tumour destruction using arbitrary percentage cut-offs (ie, 9% vs 10%; 50% vs 51%, etc) was imprecise and subjective. Although the MDA system generated greatest concordance, this was due to 'oversimplification' surrounding wide, arbitrarily assigned thresholds of </> 5% of tumour. CONCLUSIONS: All systems lacked precision and clarity for accurate regression grading. Presently the clinical utility and impact of histological regression grading in patient management is questionable. There is a need to re-evaluate regression grading in the pancreas and establish a reproducible, clinically relevant grading system.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".