CS-iCCA, A New Clinically Based Staging System for Intrahepatic Cholangiocarcinoma: Establishment and External Validation
Bibliographic record
Abstract
INTRODUCTION: Intrahepatic cholangiocarcinoma (iCCA) is a primary liver malignancy with poor prognosis. Current prognostic methods are most accurate for patients with surgically resectable disease. However, a significant proportion of patients with iCCA are not surgical candidates. We aimed to develop a generalizable staging system based on clinical variables to determine prognosis of all patients with iCCA. METHODS: The derivation cohort included 436 patients with iCCA seen between 2000 and 2011. For external validation, 249 patients with iCCA seen from 2000 to 2014 were enrolled. Survival analysis was performed to identify prognostic predictors. All-cause mortality was the primary end point. RESULTS: Eastern Cooperative Oncology Group status, tumor number, tumor size, metastasis, albumin, and carbohydrate antigen 19-9 were incorporated into a 4-stage algorithm. Kaplan-Meier estimates for 1-year survival were 87.1% (95% confidence interval [CI] 76.1-99.7), 72.7% (95% CI 63.4-83.4), 48.0% (95% CI 41.2-56.0), and 16% (95% CI 11-23.5), respectively, for stages I, II, III, and IV. Univariate analysis yielded significant differences in risk of death for stages II (hazard ratio [HR] 1.71; 95% CI 1.0-2.8), III (HR 3.32; 95% CI 2.07-5.31), and IV (HR 7.44; 95% CI 4.61-12.01) compared with stage I (reference). Concordance indices showed the new staging system was superior to the TNM staging for predicting mortality in the derivation cohort, P < 0.0001. In the validation cohort, however, the difference between the 2 staging systems was not significant. DISCUSSION: The proposed independently validated staging system uses nonhistopathologic data to successfully stratify patients into 4 stages. This staging system has better prognostic accuracy compared with the TNM staging and can assist physicians and patients in treatment of iCCA.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".