Safety and Feasibility of Combining On-Demand Selective Locoregional Treatment with First-Line Atezolizumab Plus Bevacizumab for Patients with Unresectable Hepatocellular Carcinoma
Bibliographic record
Abstract
Various locoregional treatments for localized hepatocellular carcinoma (HCC) have been developed. This retrospective study investigated the safety and feasibility of combining on-demand selective locoregional treatment for residual lesions after tumor shrinkage (complete response [CR] oriented) or for solitary or few drug-resistant lesions (progressive disease (PD) salvage) with first-line atezolizumab plus bevacizumab (atezo/bev) for unresectable HCC. Twenty-nine patients with unresectable HCC were included. Fourteen locoregional treatments were performed (CR oriented, 7; PD salvage, 7) in ten patients in the combination-therapy group. All patients in the combination-therapy group successfully achieved a CR or PD salvage status after the planned locoregional treatment. The objective response rate of the combination-therapy group (80.0%) was higher than that of the atezo/bev alone group (21.1%; p = 0.005). Progression-free survival (PFS) and overall survival (OS) were longer in the combination group (medians for PFS and OS not reached) than in the atezo/bev alone group (median PFS, 7.4 months; median OS, 19.8 months) (PFS, p = 0.004; OS, p < 0.001). The albumin–bilirubin score did not change, and no severe complications occurred after locoregional treatment. When performed in a minimally invasive manner, on-demand selective locoregional treatment combined with first-line atezo/bev could be safe and feasible for unresectable HCC.
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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 | 0.002 |
| 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".