Node-Negative Colorectal Cancer at High Risk of Distant Metastasis Identified by Combined Analysis of Lymph Node Status, Vascular Invasion, and Raf-1 Kinase Inhibitor Protein Expression
Bibliographic record
Abstract
PURPOSE: To identify independent clinicopathologic factors and protein markers leading to the identification of colorectal cancer (CRC) patients with mismatch repair proficiency at risk of developing metastasis and, consequently, more likely to benefit from combined modality therapy. EXPERIMENTAL DESIGN: Immunohistochemistry for 22 tumor markers was done using a tissue microarray. A subset of 387 CRC patients with complete clinicopathologic data and TNM stage was analyzed. Univariate and multivariate analyses were done to identify independent predictive markers of metastasis. The results were validated on 810 CRC patients. RESULTS: In univariate analysis, T stage (P < 0.001), N stage (P < 0.001), tumor grade (P = 0.005), vascular invasion (P < 0.001), tumor budding (P < 0.001), positive expression of beta-catenin (P = 0.015), overexpression of RHAMM (P = 0.008), negative expression of Raf-1 kinase inhibitor protein (RKIP; P = 0.001), and absence of intraepithelial lymphocytes (P = 0.017) were significantly associated with the presence of distant metastasis. In multivariate analysis, higher N stage (P < 0.001), presence of vascular invasion (P = 0.009), and RKIP loss (P = 0.003) independently predicted distant metastatic disease. A subgroup of node-negative patients was identified as high risk for distant metastasis and showed a similar probability of metastatic risk and nearly identical survival times as node-positive patients with absence of vascular invasion and positive RKIP expression (metastatic risk, 24% and 22%; median survival time, 45.0 and 47.0 months, respectively). CONCLUSION: The combined analysis of N stage, vascular invasion, and RKIP expression is highly predictive of distant metastasis in patients with mismatch repair--proficient CRC. Additionally, a subgroup of more aggressive N(0) tumors can be identified by evaluating vascular invasion and RKIP expression.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".