Reproducibility of tumor budding assessment in pancreatic cancer based on a multicenter interobserver study
Bibliographic record
Abstract
Tumor budding has been reported to be an independent prognostic factor in pancreatic ductal adenocarcinoma (PDAC). Its use in daily diagnostics would improve the prognostic stratification of patients. We performed a multicenter interobserver study to test various budding assessment methods for their reproducibility. Two serial sections of 50 resected, treatment-naïve PDACs were stained for Hematoxylin and Eosin (H&E) and pancytokeratin. Tumor budding was scored by independent observers at five participating centers in Switzerland, Germany, and Canada. Pathologists assessed tumor budding on a digital platform comparing H&E with pancytokeratin staining in 10 high-power fields (10HPF) and one HPF hotspot (1HPF). Additionally, tumor budding was assessed in one H&E hotspot at × 20 magnification, as suggested by the International Tumor Budding Consensus Conference (ITBCC). Correlation coefficients for bud counts between centers ranged from r = 0.58648 to r = 0.78641 for H&E and from r = 0.69288 to r = 0.81764 for pancytokeratin. The highest interobserver agreement across all centers was observed for pancytokeratin 10HPFs (ICC = 0.6). ICC values were 0.49, 0.48, 0.41, and 0.4 for H&E in 1HPF hotspot, H&E in 10HPFs, pancytokeratin in 1HPF, and H&E in one hotspot at ×20, respectively (ITBCC method). This interobserver study reveals a range between moderately poor to moderate agreement levels between pathologists for the different tumor budding assessment methods in PDAC. Acceptable levels of agreement were reached with the pancytokeratin 10HPF method, which can thus be recommended for the assessment of tumor budding in PDAC resection specimens. To improve the levels of interobserver agreement, the implementation of machine learning applications should be considered.
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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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".