Characteristics and Prognosis of Patients with Advanced Hepatocellular Carcinoma Treated with Atezolizumab/Bevacizumab Combination Therapy Who Achieved Complete Response
Bibliographic record
Abstract
AIM: To investigate the characteristics and prognosis of patients with advanced hepatocellular carcinoma (HCC) treated with atezolizumab and bevacizumab (Atz/Bev) who achieved a complete response (CR) according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). METHODS: A total of 120 patients with Eastern Cooperative Oncology Group performance status (PS) 0 or 1 and Child-Pugh A at the start of Atz/Bev treatment were included. Barcelona Clinic Liver Cancer stage C was recorded in 59 patients. RESULTS: The CR rate with Atz/Bev alone was 15.0%. The median time to CR was 3.4 months, and the median duration of CR was 15.6 months. A significant factor associated with achieving CR with Atz/Bev alone was an AFP ratio of 0.34 or less at 3 weeks. Adding transarterial chemoembolization (TACE) in the six patients who achieved a partial response increased the overall CR rate to 20%. Among the 24 patients who achieved CR, the median progression-free survival was 19.3 months, the median overall survival was not reached, and 14 patients (58.3%) were able to discontinue Atz/Bev and achieve a drug-free status. Twelve of these patients developed progressive disease (PD), but eleven successfully received post-PD treatments and responded well. CONCLUSIONS: Achieving CR by mRECIST using Atz/Bev alone or with additional TACE can be expected to offer an extremely favorable prognosis.
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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 | 0.000 |
| 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) | 0.001 | 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".