New Histoprognostic Factors to Consider for the Staging of Colon Cancers: Tumor Deposits, Invasive Tumor Infiltration and High-Grade Budding
Bibliographic record
Abstract
Colorectal cancer is a major public health issue due to its high incidence and mortality. It is, therefore, essential to identify histological markers for prognostic purposes and to optimize the therapeutic management of patients. The main objective of our study was to analyze the impact of new histoprognostic factors, such as tumor deposits, budding, poorly differentiated clusters, mode of infiltration, the intensity of inflammatory infiltrate and the type of tumor stroma, on the survival of patients with colon cancer. Two hundred and twenty-nine resected colon cancers were fully histologically reviewed, and survival and recurrence data were collected. Survival was analyzed using Kaplan-Meier curves. A univariate and multivariate Cox model was constructed to identify prognostic factors for overall survival and recurrence-free survival. The median overall survival of the patients was 60.2 months and the median recurrence-free survival was 46.9 months. Overall survival and recurrence-free survival were significantly worse in the presence of isolated tumor deposits (log rank = 0.003 and 0.001, respectively) and for an infiltrative type of tumor invasion (log rank = 0.008 and 0.02, respectively). High-grade budding was associated with a poor prognosis, with no significant difference. We did not find a significant prognostic impact of the presence of poorly differentiated clusters, the intensity of the inflammatory infiltrate or the stromal type. In conclusion, the analysis of these recent histoprognostic factors, such as tumor deposits, mode of infiltration, and budding, could be integrated into the results of pathological reports of colon cancers. Thus, the therapeutic management of patients could be adjusted by providing more aggressive treatments in the presence of some of these factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".