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Record W4414450104 · doi:10.1159/000548606

Surrogate Endpoints for Overall Survival in Advanced Hepatocelluar Carcinoma in the Era of Immunotherapy: A Trial Level Meta-Analysis

2025· article· en· W4414450104 on OpenAlexaff
Yacob Saleh, Zeynep Baskurt, Abdul Rehman Farooq, Rachel Goodwin, Jennifer J. Knox, Grainne O ́Kane, Eric X. Chen

Bibliographic record

VenueLiver Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsOttawa HospitalPrincess Margaret Cancer Centre
Fundersnot available
KeywordsSurrogate endpointOverall survivalClinical endpointProgression-free survivalEndpoint DeterminationSurvival analysis

Abstract

fetched live from OpenAlex

Background: Systemic therapy containing immune checkpoint inhibitors (ICIs) has become the standard of care for patients with advanced hepatocellular carcinoma (HCC). A prior analysis from the pre-ICI era demonstrated a moderate correlation between progression-free survival (PFS) and overall survival (OS). We performed a literature-based meta-analysis to include randomized phase III trials (RCTs) of ICIs to evaluate surrogate endpoints for OS in advanced HCC. Methods: RCTs evaluating systemic therapies in advanced HCC published/presented between 2007 and 2024 were identified through a systemic literature search. Hazard ratios (HRs) for OS and PFS were extracted. The change in the overall response rates (ΔORR) was calculated as the difference between experimental and control arms. Pearson correlation and mixed-effects meta-regression analyses were performed. Strength of correlation was determined using the criteria from the Institute for Quality and Efficiency in Health Care (IQWIG). Surrogate threshold effect (STE) was determined for each comparison when possible. Subgroup analysis was performed. A p < 0.05 was considered to be statistically significant. Results: In total, 21 1st-line and 12 2nd-line RCTs were included. Of 1st-line RCTs, 48% evaluated ICIs either alone or in combination. There was a weak correlation between HR-PFS and HR-OS (n = 18, r = 0.64, 95% confidence interval (CI): 0.25–0.85, p = 0.004) and no correlation between ΔORR and HR-OS (n = 21, r = 0.42, 95% CI: −0.01 – 0.72, p = 0.06). The STE for HR-PFS was 0.68. Subgroup analyses revealed a moderate correlation between HR-PFS and HR-OS in 1st-line RCTs enrolling fewer patients with nonviral etiology (n = 9, r = 0.75, 95% CI: 0.17–0.94, p = 0.02). There was, however, a strong correlation between HR-PFS and HR-OS in 2nd-line RCTs (n = 10, r = 0.90, 95% CI: 0.61–0.98, p < 0.001). The STE for HR-PFS in 2nd-line RCTs was 0.87. Conclusion: The correlation between HR-PFS and HR-OS is weak in 1st-line RCTs in advanced HCC where OS remains the most appropriate endpoint. There is a strong association between HR-PFS and HR-OS for 2nd-line RCTs, suggesting that PFS is a suitable surrogate endpoint in that setting.

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 imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.038
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0170.074
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.183
GPT teacher head0.337
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
DomainMethods
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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