Diffusion-Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?

Takashi Ota; Masatoshi Hori; Denis Le Bihan; Hideyuki Fukui; Hiromitsu Onishi; Atsushi Nakamoto; Takahiro Tsuboyama; Mitsuaki Tatsumi; Kazuya Ogawa; Noriyuki Tomiyama

doi:10.3390/jcm10194553

Diffusion-Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?

J Clin Med. 2021 Sep 30;10(19):4553. doi: 10.3390/jcm10194553.

Authors

Affiliations

¹ Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
² Department of Radiology, Kobe University Graduate School of Medicine, Kobe 650-0017, Japan.
³ NeuroSpin, CEA-Saclay, Paris-Saclay University, 91191 Saclay, France.
⁴ National Institute for Physiological Sciences (NIPS), Okazaki 444-8585, Japan.
⁵ Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8303, Japan.

Abstract

Background: Strong correlation has been reported between tissue water diffusivity and tissue elasticity in the liver. The purpose of this study is to explore the capability of diffusion-based virtual MR elastography (VMRE) in the characterization of liver tumors by extending beyond liver fibrosis assessments. Methods: Fifty-four patients (56 liver tumors: hepatocellular carcinoma (HCC), 31; metastases, 25) who underwent MRE, diffusion-weighted imaging (DWI) (b: 0, 800 s/mm²), and VMRE (b: 200, 1500 s/mm²) were enrolled. The MRE shear modulus (µ_MRE), apparent diffusion coefficient (ADC), and shifted ADC (sADC) were obtained. Virtual stiffness (µ_diff) was estimated from the relationship between µ_MRE and sADC. A linear discriminant analysis combining VMRE and MRE to classify HCC and metastases was performed in a training cohort (thirty-two patients) to estimate a classifier (C), and evaluate its accuracy in a testing cohort (twenty-two patients). Pearson's correlations between µ_MRE, sADC, and ADC were evaluated. In addition to the discriminant analysis, a receiver operating characteristic (ROC) curve was used to assess the discrimination capability between HCC and metastases. Results: The correlations between µ_MRE and sADC were significant for liver, HCC, and metastases (r = 0.91, 0.68, 0.71; all p < 0.05). Those between µ_MRE and ADC were weaker and significant only for metastases (r = 0.17, 0.20, 0.55). µ_diff values were not significantly different between HCC and metastases (p = 0.56). Areas under the curves (AUC) to differentiate HCC from metastases were as follows: VMRE, 0.46; MRE alone, 0.89; MRE + VMRE, 0.96. The classifier C also provided better performance than MRE alone, in terms of sensitivity (100 vs. 93.5%, respectively) and specificity (92 vs. 76%, respectively, p = 0.046). Conclusions: The correlation between sADC and µ_MRE was strong both in the liver and in tumors. However, VMRE alone could not classify HCC and metastases. The combination of MRE and VMRE, however, allowed discriminant performance between HCC and metastases.

Keywords: MR elastography; diffusion-weighted imaging; hepatocellular carcinoma; liver; metastatic liver cancer; virtual MR elastography.