Abstract
Immune checkpoint inhibitors and anti-angiogenic targeted therapies have improved outcomes in hepatocellular carcinoma (HCC), but responses remain heterogeneous, creating a need for noninvasive biomarkers to enable early treatment adaptation. We review clinical and translational evidence on nuclear medicine approaches - PET/computed tomography (CT), single-photon emission computed tomography (SPECT) , radiomics, machine learning, and theranostics - for response prediction and prognostication in HCC treated with immunotherapy alone or in combination with targeted agents. Metabolic PET/CT, most commonly with 18 F-fluorodeoxyglucose , supports pragmatic risk stratification; volumetric indices such as metabolic tumor volume (MTV) and total lesion glycolysis generally provide stronger prognostic enrichment than single-voxel metrics, and an MTV threshold of greater than or equal to39.65 cm³ has been reported to associate with poorer outcomes. Immune-targeted PET/SPECT extends beyond metabolism by mapping target availability and heterogeneity (e.g. PD-L1) and immune activation or effector function (e.g. CD137, granzyme B), although current studies are often small and retrospective. PET-based radiomics and machine learning can generate imaging surrogates of immune phenotypes and aggressive biology, but reproducibility is limited by acquisition/reconstruction differences, segmentation variability, and scarce external validation. Theranostics offers an image-guided 'select-and-treat' paradigm for radionuclide therapy, yet target heterogeneity, dosimetry standardization, cost, and infrastructure remain barriers. Translation to routine care will require harmonized protocols, multicenter prospective validation, and demonstration of decision impact.