The Role of “treatment Unfeasibility” in the Multiparametric, Multidisciplinary, and Expert Evaluation of HCC Patients

The Multiparametric Therapeutic Hierarchy (MTH) has been proposed as an evolution of the Barcelona Clinic Liver Cancer (BCLC) algorithm to better reflect the complexity of real-world hepatocellular carcinoma (HCC) management. While the BCLC often assumes idealised conditions, the MTH explicitly incorporates “treatment unfeasibility” as a variable in clinical decision-making, a concept this review seeks to examine and refine. We propose that unfeasibility should not be limited to absolute contraindications but should be understood as a multidimensional construct comprising four interacting dimensions: (1) Technical Feasibility; (2) Resources; (3) Equity; and (4) Values and Acceptability. This concept of unfeasibility adapts the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence-to-Decision (EtD) framework, originally designed for population-level guideline development, to individual-level clinical reasoning. At the point of care, the five EtD domains (Feasibility, Resources, Acceptability, Equity, and Values and Preferences) converge into a unified assessment of treatment feasibility for the specific patient. A precise mapping between our four dimensions and the original GRADE domains is provided. This approach is complementary to, rather than a departure from, GRADE methodology and is particularly relevant in settings characterised by low or very low certainty of evidence. Our analysis reveals a recurring inverse relationship between therapeutic efficacy and feasibility: the most effective treatments, such as liver transplantation, are often constrained by technical, resource-related, and systemic barriers. This tension results in widespread undertreatment across the HCC population. By defining unfeasibility as a key element of expert reasoning, this framework aims to enhance transparency in how multidisciplinary tumour boards weigh multiple considerations when making individualised treatment decisions under uncertainty, without prescribing decision rules or treatment recommendations.