Immunoscore clinical utility to identify good prognostic colon cancer stage II patients with high-risk clinico-pathological features for whom adjuvant treatment may be avoided.
Bibliographic record
Abstract
487 Background: Immunoscore Colon is an IVD test predicting the risk of relapse in early-stage colon cancer (CC) patients, by measuring the host immune response at the tumor site. It is a risk-assessment tool providing independent and superior prognostic value than the usual tumor risk parameters and is intended to be used as an adjunct to the TNM classification. Risk assessment is particularly important to decide when to propose an adjuvant (adj.) treatment for stage (St) II CC patients. High-risk stage II patients defined as those with poor prognostic features including T4, lymph nodes < 12, poor differentiation, VELIPI, bowel obstruction/perforation can be considered for adj. chemotherapy (CT). However, additional risk factors are needed to guide treatment decisions. Methods: A subgroup analysis was performed on the St II untreated patients (n = 1130) from the Immunoscore international validation study (Pagès The Lancet 2018). The high-risk patients (with at least 1 clinico-pathological high-risk feature) were classified in 2 categories using pre-defined cutoffs: Low Immunoscore versus High Immunoscore and their five-year time to recurrence (5Y TTR) was compared to the TTR of the low-risk patients (without any clinico-pathological high-risk feature). Results: Among the patients with high-risk features (n = 630), 438 (69.5%) had a High Immunoscore with a corresponding 5Y TTR of 87.4 (95% CI 83.9-91.0), statistically similar (logrank pv not stratified p > 0.42, wald pv stratified by center p > 0.20) to the TTR 89.1 (95% CI 86.1-92.1) observed for the 500 low-risk patients (with no clinico-pathological feature). Furthermore, 5Y TTR for these patients were statistically similar to those of St II patients with high-risk features and a High Immunoscore (n = 438), who received adj. CT (n = 162) (5Y TTR of 83.4 (95% CI 77.6-89.9). Conclusions: These data show that despite the presence of high-risk features that usually trigger adj. treatment, when not treated with CT, a significant part of these patients (69.5%) have a recurrence risk similar to the low risk patients. Therefore, the Immunoscore test could be a good tool for adj. treatment decision in St 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".