Quantitative Magnetic Resonance Imaging Methods for the Assessment and Prediction of Treatment Response to Transarterial Chemoembolization in Hepatocellular Carcinoma.

Abstract

This article reviews the state-of-the-art applications of quantitative magnetic resonance imaging (qMRI) in predicting and evaluating response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). HCC is a highly heterogeneous tumor, and its response to TACE varies significantly among patients. Early identification of treatment response is critical for optimizing management. Promising results have been reported using various qMRI methods, including hepatocyte-specific contrast-enhanced MRI, diffusion imaging, perfusion imaging, magnetic resonance spectroscopy (MRS), blood oxygen level-dependent functional MRI (BOLD-fMRI), magnetic resonance elastography (MRE), and artificial intelligence (AI). The coefficient of variation in the hepatobiliary phase of hepatocyte-specific contrast-enhanced MRI, which quantifies signal heterogeneity, may predict TACE outcomes. Among diffusion imaging methods, diffusion kurtosis imaging has outperformed intravoxel incoherent motion and diffusion-weighted imaging (DWI), while perfusion imaging has shown a lower area under the curve (AUC) compared to diffusion imaging. Combining MRS with DWI has achieved an AUC of 1.000 for early assessment of TACE response. However, BOLD-fMRI and MRE remain underexplored and lack established models with key quantitative parameters. AI models incorporating radiomics or deep learning with clinical factors have shown promising AUC values ranging from 0.690 to 1.000 in test sets. However, their added value requires validation through larger prospective studies.