Merits and boundaries of the BCLC staging and treatment algorithm: Learning from the past to improve the future with a novel proposal.

In this Expert Opinion, we thoroughly analyse the Barcelona Clinic Liver Cancer (BCLC) staging and treatment algorithm for hepatocellular carcinoma (HCC) that, since 1999, has standardised HCC management, offering a structured approach for the prognostic evaluation and treatment of patients with HCC. The first part of the article presents the strengths and evolutionary improvements of the BCLC staging system. Nevertheless, both patient characteristics and available treatments have changed in the last two decades, limiting the role of the BCLC criteria for treatment allocation in a growing number of patients. As therapeutic options expand and become more effective, the stage-linked treatment decision-making algorithm may lead to undertreatment and suboptimal outcomes for patients with disease beyond early-stage HCC. Consequently, strict adherence to BCLC criteria is limited in expert centres, particularly for patients diagnosed beyond early-stage HCC. Although the BCLC system remains the benchmark against which other therapeutic frameworks must be judged, the era of precision medicine calls for patient-tailored therapeutic decision-making (by a multidisciplinary tumour board) rather than stage-dictated treatment allocation. Acknowledging this conceptual difference in clinical management, the second part of the article describes a novel "multiparametric therapeutic hierarchy", which integrates a comprehensive assessment of clinical factors, biomarkers, technical feasibility, and resource availability. Lastly, considering the increasing efficacy of locoregional and systemic treatments, the concept of "converse therapeutic hierarchy" is introduced. These treatments can increase the feasibility (conversion approach) and effectiveness (adjuvant approach of systemic therapy) of potentially curative approaches to greatly improve clinical outcomes.

Introducing the Treatment Hierarchy Question in Network Meta-Analysis.

Comparative effectiveness research using network meta-analysis can present a hierarchy of competing treatments, from the most to the least preferable option. However, in published reviews, the research question associated with the hierarchy of multiple interventions is typically not clearly defined. Here we introduce the novel notion of a treatment hierarchy question that describes the criterion for choosing a specific treatment over one or more competing alternatives. For example, stakeholders might ask which treatment is most likely to improve mean survival by at least 2 years, or which treatment is associated with the longest mean survival. We discuss the most commonly used ranking metrics (quantities that compare the estimated treatment-specific effects), how the ranking metrics produce a treatment hierarchy, and the type of treatment hierarchy question that each ranking metric can answer. We show that the ranking metrics encompass the uncertainty in the estimation of the treatment effects in different ways, which results in different treatment hierarchies. When using network meta-analyses that aim to rank treatments, investigators should state the treatment hierarchy question they aim to address and employ the appropriate ranking metric to answer it. Following this new proposal will avoid some controversies that have arisen in comparative effectiveness research.

A Markov chain approach for ranking treatments in network meta-analysis.

When interpreting the relative effects from a network meta-analysis (NMA), researchers are usually aware of the potential limitations that may render the results for some comparisons less useful or meaningless. In the presence of sufficient and appropriate data, some of these limitations (eg, risk of bias, small-study effects, publication bias) can be taken into account in the statistical analysis. Very often, though, the necessary data for applying these methods are missing and data limitations cannot be formally integrated into ranking. In addition, there are other important characteristics of the treatment comparisons that cannot be addressed within a statistical model but only through qualitative judgments; for example, the relevance of data to the research question, the plausibility of the assumptions, and so on. Here, we propose a new measure for treatment ranking called the Probability of Selecting a Treatment to Recommend (POST-R). We suggest that the order of treatments should represent the process of considering treatments for selection in clinical practice and we assign to each treatment a probability of being selected. This process can be considered as a Markov chain model that allows the end-users of NMA to select the most appropriate treatments based not only on the NMA results but also to information external to the NMA. In this way, we obtain rankings that can inform decision-making more efficiently as they represent not only the relative effects but also their potential limitations. We illustrate our approach using a NMA comparing treatments for chronic plaque psoriasis and we provide the Stata commands.

What Is the Role of Minimally Invasive Liver Surgery in Treating Patients with Hepatocellular Carcinoma on Cirrhosis?

Minimally invasive liver surgery (MILS) has been slowly introduced in the past two decades and today represents a major weapon in the fight against HCC, for several reasons. This narrative review conveys the major emerging concepts in the field. The rise in metabolic-associated steatotic liver disease (MASLD)-related HCC means that patients with significant cardiovascular risk will benefit more profoundly from MILS. The advent of efficacious therapy is leading to conversion from non-resectable to resectable cases, and therefore more patients will be able to undergo MILS. In fact, resection outcomes with MILS are superior compared to open surgery both in the short and long term. Furthermore, indications to surgery may be further expanded by its use in Child B7 patients and by the use of laparoscopic ablation, a curative technique, instead of trans-arterial approaches in cases not amenable to radiofrequency. Therefore, in a promising new approach, multi-parametric treatment hierarchy, MILS is hierarchically superior to open surgery and comes second only to liver transplantation.

Network meta-analysis results against a fictional treatment of average performance: Treatment effects and ranking metric.

BACKGROUND: Network meta-analysis (NMA) produces complex outputs as many comparisons between interventions are of interest. The estimated relative treatment effects are usually displayed in a forest plot or in a league table and several ranking metrics are calculated and presented. METHODS: In this article, we estimate relative treatment effects of each competing treatment against a fictional treatment of average performance using the "deviation from the means" coding that has been used to parametrize categorical covariates in regression models. We then use this alternative parametrization of the NMA model to present a ranking metric (PreTA: Preferable Than Average) interpreted as the probability that a treatment is better than a fictional treatment of average performance. RESULTS: We illustrate the alternative parametrization of the NMA model using two networks of interventions, a network of 18 antidepressants for acute depression and a network of four interventions for heavy menstrual bleeding. We also use these two networks to highlight differences among PreTA and existing ranking metrics. We further examine the agreement between PreTA and existing ranking metrics in 232 networks of interventions and conclude that their agreement depends on the precision with which relative effects are estimated. CONCLUSIONS: A forest plot with NMA relative treatment effects using "deviation from means" coding could complement presentation of NMA results in large networks and in absence of an obvious reference treatment. PreTA is a viable alternative to existing probabilistic ranking metrics that naturally incorporates uncertainty.