Outcome and management of patients with hepatocellular carcinoma who achieved a complete response to immunotherapy-based systemic therapy
Bibliographic record
Abstract
BACKGROUND AND AIMS: The outcome of patients with HCC who achieved complete response (CR) to immune-checkpoint inhibitor (ICI)-based systemic therapies is unclear. APPROACH AND RESULTS: Retrospective study of patients with HCC who had CR according to modified Response Evaluation Criteria in Solid Tumors (CR-mRECIST) to ICI-based systemic therapies from 28 centers in Asia, Europe, and the United States. Of 3933 patients with HCC treated with ICI-based noncurative systemic therapies, 174 (4.4%) achieved CR-mRECIST, and 97 (2.5%) had CR according to RECISTv1.1 (CR-RECISTv1.1) as well. The mean age of the total cohort (male, 85%; Barcelona-Clinic Liver Cancer-C, 70%) was 65.9±9.8 years. The majority (83%) received ICI-based combination therapies. Median follow-up was 32.2 (95% CI: 29.9-34.4) months. One- and 3-year overall survival rates were 98% and 86%. One- and 3-year recurrence-free survival rates were excellent in patients with CR-mRECIST-only and CR-RECISTv1.1 (78% and 55%; 70% and 42%). Among patients who discontinued ICIs for reasons other than recurrence, those who received immunotherapy for ≥6 months after the first mRECIST CR had a longer recurrence-free survival than those who discontinued immunotherapy earlier ( p =0.008). Of 9 patients who underwent curative surgical conversion therapy, 8 (89%) had pathological CR (CR-RECISTv1.1, n= 2/2; CR-mRECIST-only, n= 6/7). CONCLUSIONS: Overall survival and recurrence-free survival of patients with CR-mRECIST-only and CR-RECISTv1.1 were excellent, and 6 of 7 patients with CR-mRECIST-only who underwent surgical conversion therapy had pathological CR. Despite potential limitations, these findings support the use of mRECIST in the context of immunotherapy for clinical decision-making. When considering ICI discontinuation, treatment for at least 6 months beyond CR seems advisable.
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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 | 0.002 |
| 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) | 0.000 | 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".