Safety and Efficacy of Concurrent Atezolizumab/Bevacizumab or Nivolumab Combination Therapy with Yttrium-90 Radioembolization of Advanced Unresectable Hepatocellular Carcinoma
Bibliographic record
Abstract
To evaluate the safety and efficacy of combining yttrium-90 radioembolization (Y90-RE) with immune checkpoint inhibitor therapy, consecutive advanced unresectable hepatocellular carcinoma (HCC) patients treated between 2016 and 2022 with atezolizumab/bevacizumab or nivolumab within three-months pre- and post-Y90-RE were retrospectively evaluated. Tumor response and treatment-related clinical/laboratory adverse events (AE) were assessed at 1 and 6 months, as well as differences in clinical and laboratory variables and median overall survival (OS) from initial treatment (whether it was Y90-RE or systemic therapy) between the two cohorts. A total of 19 patients (10 atezolizumab/bevacizumab; 9 nivolumab), comprising 84% males with median age 69 years, met the inclusion criteria. Compared to the atezolizumab/bevacizumab group, there were less males (100% vs. 67%; p = 0.02) and more ECOG ≥ 2 patients in the nivolumab group (0% vs. 33%; p = 0.02). Baseline characteristics or incidence of 6-month post-treatment any-grade AE (60% vs. 56%; p = 0.7), grade ≥ 3 AE (0% vs. 11%; p = 0.3), objective response (58% total, 60% vs. 56%; p = 0.7), and complete response (16% total; 10% vs. 22%; p = 0.8) were similar between the atezolizumab/bevacizumab and the nivolumab cohorts. Median OS was 12.9 months for the whole cohort, 16.4 months for nivolumab, and 10.7 months for atezolizumab/bevacizumab. Among patients with advanced unresectable HCC, the utilization of Y90-RE concurrently or within 90 days of nivolumab or atezolizumab/bevacizumab immunotherapy, appears to be well-tolerated and with a low incidence of severe AE.
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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".