Tumor border configuration added to TNM staging better stratifies stage II colorectal cancer patients into prognostic subgroups
Bibliographic record
Abstract
BACKGROUND: Reproducible and well characterized prognostic histomorphologic criteria added to current pathological staging could have an immediate effect on refining prognosis in colorectal cancer. The aim of this study was to determine the additive effect of tumor border configuration and peritumoral lymphocytic infiltration on the selection of patients for adjuvant therapy classified by TNM. METHODS: A total of 1420 primary colorectal cancers with complete clinicopathological data from multiple treatment centers were analyzed. The prognostic effect of tumor border configuration (pushing or infiltrating) and peritumoral lymphocytic infiltration was assessed, validated by resampling of the data, and compared with TNM staging. All P values were 2-sided. RESULTS: Multivariate analysis confirmed the adverse prognostic value of the tumor border configuration (P < .001), but not of peritumoral lymphocytic infiltration. The addition of tumor border configuration to T and N category identified 2 major prognostic subgroups (relative risk of death, 4.75; 95% confidence interval [CI], 2.53-8.94). Moreover, stage II patients with a pushing border had a 5-year survival rate of 82.1% (95% CI, 71.8%-90.3%), whereas an infiltrating border resulted in a significantly more adverse outcome (5-year survival rate, 62.7%; 95% CI, 48.0-76.2%), closely resembling that of stage III patients. Similar results were obtained after adjusting for adjuvant therapy (P < .001). CONCLUSIONS: The classification of patients into prognostic subgroups is improved with the addition of tumor border configuration to TNM stage. In particular, patients with stage II disease characterized by an infiltrating tumor border have poor clinical outcome and represent a subset of lymph node-negative patients who could be considered for adjuvant therapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".