Efficacy of Atezolizumab Plus Bevacizumab–Transcatheter Arterial Chemoembolization Sequential Therapy for Patients with Intermediate-Stage Hepatocellular Carcinoma
Bibliographic record
Abstract
This retrospective study aimed to evaluate the impact of atezolizumab plus bevacizumab–transcatheter arterial chemoembolization (TACE) sequential therapy in unresectable hepatocellular carcinoma (HCC), especially in patients with intermediate-stage HCC. A total of 212 patients were enrolled and categorized into the Atez/Bev-TACE sequential therapy (n = 23) or Atez/Bev monotherapy group (n = 189) between 2020 and 2024. Of these, patients with intermediate-stage HCC were categorized into the Atez/Bev-TACE sequential (n = 18) or Atez/Bev monotherapy group (n = 91). The best objective response rate, disease control rate, and median progression-free survival (PFS) after TACE were 73.9%, 82.6%, and 6.1 months, respectively. The PFS after TACE was significantly higher in the Atez/Bev sequential therapy group than in the no-Atez/Bev-administration group after TACE (6.9 months vs. 5.0 months, p = 0.025). The median overall survival (OS) was significantly higher in the Atez/Bev-TACE sequential therapy group than in the Atez/Bev monotherapy group for intermediate-stage HCC (34.9 months vs. 17.8 months; p = 0.016). Independent factors associated with OS were low alpha-fetoprotein levels, modified albumin–bilirubin 1 or 2a levels, and Atez/Bev-TACE sequential therapy. Atez/Bev-TACE sequential therapy improved prognosis compared with Atez/Bev monotherapy in patients with intermediate-stage HCC. Moreover, Atez/Bev should be readministered after TACE.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".