Optimal sequencing of locoregional and systemic therapies for intermediate and advanced hepatocellular carcinoma: a network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Transarterial chemoembolization (TACE), anti-angiogenic drugs (AADs), and immune checkpoint inhibitors (ICIs) are common therapies for hepatocellular carcinoma (HCC). Despite proven benefits of combined regimens, optimal sequencing remains unclear. This network meta-analysis evaluates safety and efficacy of therapeutic sequences in intermediate-advanced HCC. METHODS: We conducted a comprehensive search of multiple databases, including PubMed, Cochrane Library, Web of Science, and EMBASE, for studies published until February 1, 2025. Cochrane's tools and the Newcastle-Ottawa Scale were used to assess the evaluation of bias. We performed data compilation and conducted a network meta-analysis to compare the relative efficacy of different treatments. RESULTS: A total of 56 studies (10,456 patients) evaluated 11 therapeutic sequences. Survival outcomes favored TACE-AADs-ICIs (TAI), which ranked highest for overall survival (OS: SUCRA 90.0%) and progression-free survival (PFS: SUCRA 91.3%). Tumor responses differed significantly across regimens: TACE-ICIs (TI) achieved the highest probability of complete response rate (CRR: SUCRA 83.9%), while AADs-ICIs-TACE (AIT) ranked first in objective response rate (ORR: SUCRA 85.8%). Notably, ICIs-AADs (IA) achieved superior disease control rate (DCR: SUCRA 88.1%). ICIs monotherapy (I) was associated with the lowest incidence of grade ≥ 3 adverse events (AEs: SUCRA 11.7%). CONCLUSION: Our comprehensive network meta-analysis establishes a multidimensional efficacy-safety profile for sequential therapies in intermediate and advanced HCC management. TACE-initiated sequences (TAI/TIA) optimize survival (OS/PFS: SUCRA > 90%), while systemic-first regimens (AIT/IA) maximize tumor response (ORR/DCR: SUCRA > 85%). ICIs monotherapy exhibits the safest profile. Further clinical studies are warranted to determine optimal treatment sequencing for intermediate and advanced HCC.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.050 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".