Cytokeratin‐based assessment of tumour budding in colorectal cancer: analysis in stage II patients and prospective diagnostic experience
Bibliographic record
Abstract
Abstract Tumour budding in colorectal cancer is an important prognostic factor. A recent consensus conference elaborated recommendations and key issues for future studies, among those the use of pan‐cytokeratin stains, especially in stage II patients. We report the first prospective diagnostic experience using pan‐cytokeratin for tumour budding assessment. Moreover, we evaluate tumour budding using pan‐cytokeratin stains and disease‐free survival (DFS) in stage II patients. To this end, tumour budding on pan‐cytokeratin‐stained sections was evaluated by counting the number of tumour buds in 10 high‐power fields (0.238 mm2), then categorizing counts as low/high‐grade at a cut‐off of 10 buds, in two cohorts. Cohort 1: prospective setting with 236 unselected primary resected colorectal cancers analysed by 17 pathologists during diagnostic routine. Cohort 2: retrospective cohort of 150 stage II patients with information on DFS. In prospective analysis of cohort 1, tumour budding counts correlated with advanced pT, lymph node metastasis, lymphovascular invasion, perineural invasion (all p < 0.0001), and distant metastasis (p = 0.0128). In cohort 2, tumour budding was an independent predictor of worse DFS using counts [p = 0.037, HR (95% CI): 1.007 (1.0–1.014)] and the low‐grade/high‐grade scoring approach [p = 0.02; HR (95% CI): 3.04 (1.2–7.77), 90.7 versus 73%, respectively]. In conclusion, tumour budding assessed on pan‐cytokeratin slides is feasible in a large pathology institute and leads to expected associations with clinicopathological features. Additionally, it is an independent predictor of poor prognosis in stage II patients and should be considered for risk stratification in future clinical studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".