Immunoscore as a parameter predicting time to recurrence and disease-free survival in T4N0 stage II colon cancer patients.
Bibliographic record
Abstract
4105 Background: Risk assessment is particularly important to decide when to propose an adjuvant treatment for Stage II Colon Cancer (CC) patients. However, the current tumor risk features are imperfect and additional risk factors are needed to guide treatment decisions. The consensus Immunoscore is an alternative and powerful approach that could be used in the T4N0 Stage II colon cancer population. Immunoscore is an in vitro diagnostic test that predicts the risk of relapse in patients with CC by measuring the host immune response at the tumor site. Methods: From the international Immunoscore consortium study (n = 2681) (Pagès et al. The Lancet 2018), a subgroup analysis was performed on T4N0 Stage II colon cancer patients (n = 208). Results: In stage II T4N0, Int+Hi Immunoscore represented 65.4% of the population and low-Immunoscore only 34.6%. T4N0 patients with Int+Hi Immunoscore presented a significantly prolonged survival for TTR compared to low Immunoscore patients (5 years recurrence rate Int+Hi: 84.6% (78.3-91.5), Lo: 46.3% (35.1-61); unadjusted HR [Int+Hi vs Lo] = 0.21; (95% CI 0.11-0.4); P< 0.0001), representing a restricted mean survival time (RMST) difference of 80.9 months (95% CI 51.1-110.6) ( P< 0.0001). The DFS was significantly different between Int+Hi and Low Immunoscore (5 years recurrence rate Int+Hi: 70.5% (95% CI 62.7-79.1), Lo: 38.5% (95% CI 28.2-52.5); unadjusted HR [Int+Hi vs Lo] = 0.31; (95% CI 0.19-0.49); P< 0.0001). Using restricted mean survival time (RMST) a significant ( P< 0.0001) difference of 60.4 months (95% CI 32.6-88.1) was observed between the 2 groups Importantly, Cox multivariate analysis in Stage II T4N0 colon cancer patients, revealed that Immunoscore was the only remaining significant parameter (HR [Int+Hi vs Lo] = 0.15; (95% CI 0.05-0.46); P= 0.0009). In contrast, all other parameters, gender, sidedness, mucinous, grade, T-stage, VELIPI, MSI were not significant in multivariate analysis. Finally, Immunoscore showed the highest relative contribution to predict relapse (76.2% chi2 relative contribution), stronger than all the other parameters, MSI (16.1%), Grade (5%), sidedness (2%), gender (2%), VELIPI (1%). Conclusions: Immunoscore is the most powerful parameter to predict the risk in T4N0 population, and could be a good tool for adjuvant treatment decision in Stage II patients.
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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) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".