BACKGROUND: Evidence-based medicine (EBM) was conceived to support decisions for individual patients by integrating the best available evidence, clinical expertise, and patient values. However, the application of EBM in many fields has become associated with rigid algorithms and guideline pathways, sometimes interpreted as prescriptive rather than as aids to decision-making. Hepatocellular carcinoma (HCC) offers an informative case study of this dichotomy. This paper traces the evolution of personalized decision-making in HCC over two decades and its implications for the future of EBM. METHODS: This is a narrative, conceptual review. Rather than a systematic synthesis, it draws selectively on landmark staging frameworks, guidelines, and methodological literature to construct a schematic account of how clinical reasoning in HCC has evolved. RESULTS: HCC decision-making has evolved through a sequence of schematic frames: from stage-centred algorithms (the Ptolemaic frame), to recognition of differential treatment effects (the Copernican shift), to multiparametric expert deliberation (the Newtonian perspective), and finally to dynamic, time-dependent, bidirectional strategies (the Einsteinian perspective). The expansion of systemic immunotherapy options has reinforced this bidirectional logic while complicating stage reassessment. This trajectory culminates in a Heisenberg moment, in which uncertainty is acknowledged as intrinsic to individual decisions. The multiparametric therapeutic hierarchy, developed within multidisciplinary HCC practice, reflects this maturation towards a personalized, transparent, context-sensitive approach. CONCLUSION: HCC is a paradigmatic example of personalized decision-making in complex, multidisciplinary care. The future of EBM lies not in choosing between algorithms and personalization, but rather in transparent approaches that apply evidence rigorously while acknowledging uncertainty and respecting patient values and local context.
Personalised decision-making in hepatocellular carcinoma: conceptual evolution over two decades
Giannini, Edoardo G;
2026-01-01
Abstract
BACKGROUND: Evidence-based medicine (EBM) was conceived to support decisions for individual patients by integrating the best available evidence, clinical expertise, and patient values. However, the application of EBM in many fields has become associated with rigid algorithms and guideline pathways, sometimes interpreted as prescriptive rather than as aids to decision-making. Hepatocellular carcinoma (HCC) offers an informative case study of this dichotomy. This paper traces the evolution of personalized decision-making in HCC over two decades and its implications for the future of EBM. METHODS: This is a narrative, conceptual review. Rather than a systematic synthesis, it draws selectively on landmark staging frameworks, guidelines, and methodological literature to construct a schematic account of how clinical reasoning in HCC has evolved. RESULTS: HCC decision-making has evolved through a sequence of schematic frames: from stage-centred algorithms (the Ptolemaic frame), to recognition of differential treatment effects (the Copernican shift), to multiparametric expert deliberation (the Newtonian perspective), and finally to dynamic, time-dependent, bidirectional strategies (the Einsteinian perspective). The expansion of systemic immunotherapy options has reinforced this bidirectional logic while complicating stage reassessment. This trajectory culminates in a Heisenberg moment, in which uncertainty is acknowledged as intrinsic to individual decisions. The multiparametric therapeutic hierarchy, developed within multidisciplinary HCC practice, reflects this maturation towards a personalized, transparent, context-sensitive approach. CONCLUSION: HCC is a paradigmatic example of personalized decision-making in complex, multidisciplinary care. The future of EBM lies not in choosing between algorithms and personalization, but rather in transparent approaches that apply evidence rigorously while acknowledging uncertainty and respecting patient values and local context.| File | Dimensione | Formato | |
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