Usefulness of atezolizumab plus bevacizumab as second-line therapy for patients with unresectable hepatocellular carcinoma
Bibliographic record
Abstract
AIM: To clarify the efficacy of atezolizumab (ATZ) plus bevacizumab (BEV) as the second-line therapy for patients with unresectable hepatocellular carcinoma (HCC). METHODS: The subjects were 82 patients with HCC receiving ATZ/BEV, including 33 patients with previous therapies with molecular-targeted agents (MTA). Therapeutic efficacy was evaluated using contrast-enhanced CT according to the mRECIST. RESULTS: The Child-Pugh scores were 5, 6,7 and 8 in 40, 35, 5 and 2 patients, respectively, and the extents of HCC progression were BCLC stage A, B and C in 3, 31 and 48 patients, respectively. Early therapeutic efficacy was evaluated in 67 patients, and percentages of patients achieving CR/PR/SD/PD until 12 weeks were 3.0%/29.9%/49.3%/17.9%, respectively, indicating ORR of 32.8% and DCR of 82.1%, The ORR was higher in MTA-naïve patients (40.5%) than in those after discontinuation of lenvatinib due to PD (7.7%, P = 0.0410), while the DCR was equivalent between both patients (83.3% vs 80.0%, P = 0.1184), and the multivariate analysis revealed previous MTA therapies with lenvatinib alone as a factor to deteriorate the ORR (HR of 4.846 (P = 0.0619)). The OS rates at 24 and 48 weeks were 86% and 72%, respectively, and the rates did not differ between MTA-naïve and MTA-experienced patients. Multivariate analyses revealed that achievement of CR, PR or SD and peripheral neutrophil/lymphocyte ratio were associated with a favorable outcome (HR of 0.124, P<0.0001 and 0.351, P = 0.0303). CONCLUSIONS: ATZ/BEV merits consideration even for MTA-experienced patients, since the OS was equivalent to those in MTA-naïve patients despite of an unfavorable early therapeutic efficacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".