Magnetic resonance imaging for treatment response evaluation and prognostication of hepatocellular carcinoma after thermal ablation

Author:

Zhang Yun,Wei Hong,Song BinORCID

Abstract

Abstract Hepatocellular carcinoma (HCC) accounts for the vast majority of primary liver cancer and constitutes a major global health challenge. Tumor ablation with either radiofrequency ablation (RFA) or microwave ablation (MWA) is recommended as a curative-intent treatment for early-stage HCC. Given the widespread use of thermal ablation in routine clinical practice, accurate evaluation of treatment response and patient outcomes has become crucial in optimizing individualized management strategies. Noninvasive imaging occupies the central role in the routine management of patients with HCC. Magnetic resonance imaging (MRI) could provide full wealth of information with respect to tumor morphology, hemodynamics, function and metabolism. With accumulation of liver MR imaging data, radiomics analysis has been increasingly applied to capture tumor heterogeneity and provide prognostication by extracting high-throughput quantitative imaging features from digital medical images. Emerging evidence suggests the potential role of several qualitative, quantitative and radiomic MRI features in prediction of treatment response and patient prognosis after ablation of HCC. Understanding the advancements of MRI in the evaluation of ablated HCCs may facilitate optimal patient care and improved outcomes. This review provides an overview of the emerging role of MRI in treatment response evaluation and prognostication of HCC patients undergoing ablation. Clinical relevance statement MRI-based parameters can help predict treatment response and patient prognosis after HCC ablation and thus guide treatment planning. Key points ECA-MRI provides morphological and hemodynamic assessment of ablated HCC. EOB-MRI provides more information for tumor response prediction after ablation. DWI improve the characterization of HCC and optimize treatment decision. Radiomics analysis enables characterization of tumor heterogeneity guidance of clinical decision-making. Further studies with multiple radiologists and sufficient follow-up period are needed. Graphical abstract

Funder

National Natural Science Foundation of China

Sichuan Province Science and Technology Support Program

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging

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