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

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 as...

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Autores principales: Ota, Takashi, Hori, Masatoshi, Le Bihan, Denis, Fukui, Hideyuki, Onishi, Hiromitsu, Nakamoto, Atsushi, Tsuboyama, Takahiro, Tatsumi, Mitsuaki, Ogawa, Kazuya, Tomiyama, Noriyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509260/
https://www.ncbi.nlm.nih.gov/pubmed/34640568
http://dx.doi.org/10.3390/jcm10194553
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author Ota, Takashi
Hori, Masatoshi
Le Bihan, Denis
Fukui, Hideyuki
Onishi, Hiromitsu
Nakamoto, Atsushi
Tsuboyama, Takahiro
Tatsumi, Mitsuaki
Ogawa, Kazuya
Tomiyama, Noriyuki
author_facet Ota, Takashi
Hori, Masatoshi
Le Bihan, Denis
Fukui, Hideyuki
Onishi, Hiromitsu
Nakamoto, Atsushi
Tsuboyama, Takahiro
Tatsumi, Mitsuaki
Ogawa, Kazuya
Tomiyama, Noriyuki
author_sort Ota, Takashi
collection PubMed
description 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/mm(2)), and VMRE (b: 200, 1500 s/mm(2)) 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.
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spelling pubmed-85092602021-10-13 Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis? Ota, Takashi Hori, Masatoshi Le Bihan, Denis Fukui, Hideyuki Onishi, Hiromitsu Nakamoto, Atsushi Tsuboyama, Takahiro Tatsumi, Mitsuaki Ogawa, Kazuya Tomiyama, Noriyuki J Clin Med Article 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/mm(2)), and VMRE (b: 200, 1500 s/mm(2)) 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. MDPI 2021-09-30 /pmc/articles/PMC8509260/ /pubmed/34640568 http://dx.doi.org/10.3390/jcm10194553 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ota, Takashi
Hori, Masatoshi
Le Bihan, Denis
Fukui, Hideyuki
Onishi, Hiromitsu
Nakamoto, Atsushi
Tsuboyama, Takahiro
Tatsumi, Mitsuaki
Ogawa, Kazuya
Tomiyama, Noriyuki
Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?
title Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?
title_full Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?
title_fullStr Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?
title_full_unstemmed Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?
title_short Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?
title_sort diffusion–based virtual mr elastography of the liver: can it be extended beyond liver fibrosis?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509260/
https://www.ncbi.nlm.nih.gov/pubmed/34640568
http://dx.doi.org/10.3390/jcm10194553
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