Imaging for molecular and pathological subtyping of hepatocellular carcinoma-a critical appraisal and future directions.

Abstract

Hepatocellular carcinoma (HCC) is characterized by distinct molecular and pathological subtypes, each with unique prognostic implications. This review aims to synthesize the imaging features associated with these HCC subtypes and discuss their potential to guide therapeutic decision-making. We searched PubMed and Embase for articles published from September 2004 to December 2024. The search strategy combined terms for imaging modalities ("CT," "MRI"), the primary disease ("hepatocellular carcinoma"), and various molecular and pathological subtypes (e.g., "macrotrabecular-massive," "steatohepatitic," "CK19," and "CTNNB1"). HCC is a biologically heterogeneous malignancy with varied prognosis and sensitivity to treatment. Assessment of its molecular and pathological subtypes relies on invasive histopathological examination, which is subject to sampling errors and often unavailable prior to treatment selection. A growing body of evidence suggests that radiologic features aid in the non-invasive classification of HCC subtypes, thereby informing individualized therapy. Given the substantial overlap between molecular, pathological, and imaging features, this review hypothesize that a comprehensive phenotyping system integrating all these information could significantly enhance personalized prognostication and treatment strategies. Radiologic imaging features not only provide valuable information for identifying molecular and pathological subtypes of HCC but also serve as practical tools to guide individualized therapeutic decision-making. Question Can CT and MRI reliably infer the molecular classification and pathological subtypes that drive prognosis in HCC? Findings Several imaging features have been found to reflect underlying molecular and pathological subtypes, but they do not demonstrate a one-to-one correlation. Clinical relevance An integrated classification system incorporating clinical, imaging, pathological, and molecular data may help mitigate the limitations of histologic and molecular analyses and facilitate individualized prognostication.