A Novel Combined Tumor Budding-Poorly Differentiated Clusters Grading System Predicts Recurrence and Survival in Stage I-III Colorectal Cancer
Bibliographic record
Abstract
Tumor budding (TB) and poorly differentiated clusters (PDCs) are powerful prognostic factors in colorectal cancer (CRC). Despite their morphologic and biological overlap, TB and PDC are assessed separately and are distinguished by an arbitrary cutoff for cell cluster size. This cutoff can be challenging to apply in practice and its biological significance remains unclear. We developed a novel scoring system that incorporates TB and PDC into a single parameter ("Combined Score"; CS), eliminating the need for such cutoffs and allowing the prognostic value of PDC to be captured alongside TB. In a cohort of 481 stage I-III CRC resections, CS was significantly associated with American Joint Committee on Cancer (AJCC) stage, T-stage, N-stage, histologic grade, tumor deposits, lymphovascular invasion, and perineural invasion ( P <0.0001). In addition, CS was significantly associated with decreased 5-year recurrence-free survival, overall survival, and disease-specific survival ( P <0.0001). TB and PDC showed similar associations with oncologic outcomes, with hazard ratios consistently lower than for CS. The association between CS and oncologic outcomes remained significant in subgroup analyses stratified by AJCC stage, anatomic location (rectum/colon) and neoadjuvant therapy status. On multivariable analysis, CS retained its significant association with oncologic outcomes ( P =0.0002, 0.005, and 0.009) for recurrence-free survival, disease-specific survival, and overall survival, respectively. In conclusion, CS provides powerful risk stratification in CRC which is at least equivalent to that of TB and PDC assessed individually. If validated elsewhere, CS has practical advantages and a biological rationale that may make it an attractive alternative to assessing these features separately.
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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 | 0.002 |
| 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".