Second-Line Treatment after Failure of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma: Tyrosine Kinase Inhibitor, Retrial of Immunotherapy, or Locoregional Therapy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it