Second-Line Treatment after Failure of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma: Tyrosine Kinase Inhibitor, Retrial of Immunotherapy, or Locoregional Therapy?
Bibliographic record
Abstract
Background: Immune checkpoint inhibitor (ICI)-based therapy such as atezolizumab plus bevacizumab or durvalumab plus tremelimumab became mainstream first-line systemic treatment in advanced hepatocellular carcinoma (HCC) patients since remarkably superior efficacy of ICI-based therapy compared to tyrosine kinase inhibitors (TKIs) was reported in two recent randomized controlled trials (RCTs) (IMbrave150, HIMALAYA). However, the optimal second-line therapy after treatment failure of first-line ICI-based therapy remains unknown as no RCT has examined this issue. Summary: Therefore, at present, most clinicians are empirically treating patients with TKIs or retrial of ICI or locoregional treatment (LRT) modality such as transarterial therapy, radiofrequency ablation, and radiation therapy in this clinical setting without solid evidence. In this review, we will discuss current optimal strategies for second-line treatment after the failure of first-line ICI-based therapy by reviewing published studies and ongoing prospective trials. Key Messages: Clinicians should consider carefully whether to treat the patients with TKI, other ICI-based therapy, or LRT in this situation by considering several factors including liver function reserve, performance status, adverse events of previous therapy, and presence of lesion that can consider LRT such as oligoprogression and vascular invasion. In the meantime, we await the results of ongoing prospective trials to elucidate the best management options.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".