Second-line treatment patterns and outcomes in advanced HCC after progression on atezolizumab/bevacizumab
Bibliographic record
Abstract
Background & Aims: Atezolizumab/bevacizumab (A/B) is now a standard first-line treatment for advanced hepatocellular carcinoma (HCC), but the optimal second-line regimen is not known. We evaluated real-world treatment patterns and outcomes to investigate factors associated with post-progression survival (PPS). Methods: In this multicenter, international, retrospective study, we examined clinical characteristics and outcomes of patients with advanced HCC who progressed on first-line A/B. The primary outcome of PPS was defined as time from first radiographic progression on A/B to death. Results: 0.256). Conclusions: Continuation of active therapy after A/B progression was independently associated with better survival even after adjusting for baseline disease characteristics. mPPS with IO-based therapy exceeded a year, suggesting that IO continuation post-progression may retain benefit. The precise sequencing of TKI and IO regimens warrants further investigation. Impact and implications: There is currently a lack of level 1 data on second-line treatment options for patients with advanced hepatocellular carcinoma who progress after frontline atezolizumab plus bevacizumab, as all second-line approvals were established during the frontline sorafenib era. Our study aims to fill in some of the knowledge gap by investigating real-world patient outcomes in the second-line treatment setting. Findings from this study show that patients who continued active treatment had improved post-progression survival compared to those who received best supportive care, and medication regimens incorporating tyrosine kinase inhibitors as well as immunotherapy agents were active. These results can help inform clinicians of possible treatment options for patients who progress after frontline atezolizumab plus bevacizumab while we await maturing data from randomized-controlled trials.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".