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Abstract

Abstract Predicting transarterial chemoembolization (TACE) refractoriness is critical for optimizing treatment strategies in hepatocellular carcinoma (HCC); however, accurate risk assessment remains challenging. The initial therapeutic response, captured through imaging and clinical indicators, reflects intrinsic treatment sensitivity and holds substantial predictive value for long-term refractoriness. This study evaluated treatment effect heterogeneity following the initial TACE to develop a multimodal nomogram for predicting TACE refractoriness after repeated sessions. Using multiparametric MRI spatial habitat radiomics, we identified three imaging-defined habitats with signal profiles resembling liquefactive necrosis, residual viable tumor, and coagulative necrosis. By integrating habitat and conventional radiomic features, an interpretable radiomic score was constructed utilizing eight machine learning algorithms. This score was subsequently combined with longitudinal dynamic clinical parameters to build a joint clinical-radiomic nomogram. The nomogram achieved areas under the curve (AUCs) of 0.924, 0.860, and 0.864 in the training, internal validation, and external validation cohorts, respectively. These findings suggest that integrating habitat radiomics with dynamic clinical features may help stratify the risk of subsequent TACE refractoriness after the initial TACE session and before repeated treatment, potentially supporting early treatment-response assessment and individualized clinical decision-making.