Tumor Budding Assessment in Colorectal Carcinoma
Bibliographic record
Abstract
Tumor budding (TB) is a powerful prognostic factor in colorectal cancer (CRC). An internationally standardized method for its assessment (International Tumor Budding Consensus Conference [ITBCC] method) has been adopted by most CRC pathology protocols. This method requires that TB counts are reported by field area (0.785 mm 2 ) rather than objective lens and a normalization factor is applied for this purpose. However, the validity of this approach is yet to be tested. We sought to validate the ITBCC method with a particular emphasis on normalization as a tool for standardization. In a cohort of 365 stage I-III CRC, both normalized and non-normalized TB were significantly associated with disease-specific survival and recurrence-free survival ( P <0.0001). Examining both 0.95 and 0.785 mm 2 field areas in a subset of patients (n=200), we found that normalization markedly overcorrects TB counts: Counts obtained in a 0.95 mm 2 hotspot field were reduced by an average of 17.5% following normalization compared with only 3.8% when counts were performed in an actual 0.785 mm 2 field. This resulted in 45 (11.3%) cases being downgraded using ITBCC grading criteria following normalization, compared with only 5 cases (1.3%, P =0.0007) downgraded when a true 0.785 mm 2 field was examined. In summary, the prognostic value of TB was retained regardless of whether TB counts in a 0.95 mm 2 field were normalized. Normalization resulted in overcorrecting TB counts with consequent downgrading of most borderline cases. This has implications for risk stratification and adjuvant treatment decisions, and suggests the need to re-evaluate the role of normalization in TB assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".