Recurrence-Free Survival as a Surrogate for Overall Survival Among Patients with Intrahepatic Cholangiocarcinoma Following Upfront Surgery: An International Multi-institutional Analysis
Bibliographic record
Abstract
INTRODUCTION: The role of recurrence-free survival (RFS) as a validated surrogate endpoint for overall survival (OS) among patients undergoing upfront surgery for intrahepatic cholangiocarcinoma (ICC) has not been defined. We sought to evaluate the correlation between RFS and OS after surgical resection for ICC. We hypothesized that RFS was a reliable surrogate endpoint for OS among patients with ICC. METHODS: Patients who underwent upfront curative-intent surgery for ICC between 2000 and 2023 were identified from an international, multi-institutional database. The correlation between RFS and OS was assessed using rank correlation. Landmark analysis evaluated concordance between survival at 5 years and recurrence status at 6, 12, 24, 36, 48, and 54 months postoperatively. RESULTS: Among 1541 patients who underwent curative-intent hepatic resection, the median RFS and OS were 22.6 months and 41.5 months, respectively. A moderately strong correlation between RFS and OS was identified (ρ = 0.79, 95% CI 0.76 to 0.82). In the landmark analysis, the concordance between 5-year OS after surgery and recurrence status at different time points (6, 12, 24, 36, 48, and 54 months) was 60.7%, 72.0%, 81.4%, 83.1%, 83.0%, and 82.5%, respectively. Restricted cubic spline analysis indicated that the prediction of OS based on RFS increased with time and plateaued 3 years after surgery. CONCLUSIONS: Among patients undergoing curative-intent resection of ICC, there was a moderately strong correlation between RFS and OS. Three-year RFS may be a reliable surrogate endpoint to predict 5-year OS and should be considered in future trial design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".