Prognostic assessment of resected colorectal liver metastases integrating pathological features, <scp><i>RAS</i></scp> mutation and Immunoscore
Bibliographic record
Abstract
Surgical resection of colorectal liver metastases combined with systemic treatment aims to maximize patient survival. However, recurrence rates are very high postsurgery. In order to assess patient prognosis after metastasis resection, we evaluated the main patho-molecular and immune parameters of all surgical specimens. Two hundred twenty-one patients who underwent, after different preoperative treatment, curative resection of 582 metastases were analyzed. Clinicopathological parameters, RAS tumor mutation, and the consensus Immunoscore (I) were assessed for all patients. Overall survival (OS) and time to relapse (TTR) were estimated using the Kaplan-Meier method and compared by log-rank tests. Cox proportional hazard models were used for uni- and multivariate analysis. Immunoscore and clinicopathological parameters (number of metastases, surgical margin, histopathological growth pattern, and steatohepatitis) were associated with relapse in multivariate analysis. Overall, pathological score (PS) that combines relevant clinicopathological factors for relapse, and I, were prognostic for TTR (2-year TTR rate PS 0-1: 49.8.% (95% CI: 42.2-58.8) versus PS 2-4: 20.9% (95% CI: 13.4-32.8), hazard ratio (HR) = 2.54 (95% CI: 1.82-3.53), p < 0.0000; and 2-year TTR rate I 0: 25.7% (95% CI: 16.3-40.5) versus I 3-4: 60% (95% CI: 47.2-76.3), HR = 2.87 (95% CI: 1.73-4.75), p = 0.0000). Immunoscore was also prognostic for OS (HR [I 3-4 versus I 0] = 4.25, 95% CI: 1.95-9.23; p = 0.0001). Immunoscore (HR [I 3-4 versus I 0] = 0.27, 95% CI: 0.12-0.58; p = 0.0009) and RAS mutation (HR [mutated versus WT] = 1.66, 95% CI: 1.06-2.58; p = 0.0265) were significant for OS. In conclusion, PS including relevant clinicopathological parameters and Immunoscore permit stratification of stage IV colorectal cancer patient prognosis in terms of TTR and identify patients with higher risk of recurrence. Immunoscore remains the major prognostic factor for OS.
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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 | 0.001 |
| 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".