The New Era of Systemic Treatment for Hepatocellular Carcinoma: From the First Line to the Optimal Sequence
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) represents the most common primary liver cancer and is considered a major global health problem as one of the leading causes of cancer-related death in the world. Due to the increase in life expectancy and the epidemiological growth of specific risk factors, such as metabolic dysfunction-associated steatotic liver disease (MASLD), the incidence of HCC is growing globally, and mortality rates are still high. Moreover, patients frequently present at an intermediate or advanced tumor stage, when curative treatments, such as surgical resection, liver transplantation or ablation are no longer applicable. In these cases, trans-arterial chemoembolization (TACE), trans-arterial radioembolization (TARE), and systemic therapy are the only suitable options to achieve disease control. The multi-kinase inhibitor Sorafenib has been the only systemic treatment available for unresectable advanced HCC for almost a decade, but in the last couple of years new therapeutic options have emerged. Recent advances in understanding the interactions between the tumor and its microenvironment, especially cancer immune escape, led to the advent of immunotherapy. Currently, first-line systemic treatment for HCC is represented by the combination of the immune checkpoint inhibitor (ICI) Atezolizumab plus Bevacizumab, an anti-vascular endothelial growth factor (VEGF) monoclonal antibody, but many other ICIs have been investigated, such as Nivolumab, Pembrolizumab, Durvalumab and Ipilimumab. However, the problem of second- and third-line therapies, and the correct sequence of treatments remains open and is not addressed in most studies. This explains the urge to find new systemic treatments that can improve the survival and quality of life in patients that can go beyond the first line of treatment. The aim of this paper is to offer a complete overview of the most recent innovations in systemic treatments for unresectable locally advanced and metastatic HCC, including emerging therapies, with a particular focus on treatment sequences. Moreover, we will provide an outlook on possible future approaches to patients who progress beyond first-line therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".