Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?
<i>Background</i>: 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 beyon...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Journal of Clinical Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0383/10/19/4553 |
| _version_ | 1797516143557083136 |
|---|---|
| author | Takashi Ota Masatoshi Hori Denis Le Bihan Hideyuki Fukui Hiromitsu Onishi Atsushi Nakamoto Takahiro Tsuboyama Mitsuaki Tatsumi Kazuya Ogawa Noriyuki Tomiyama |
| author_facet | Takashi Ota Masatoshi Hori Denis Le Bihan Hideyuki Fukui Hiromitsu Onishi Atsushi Nakamoto Takahiro Tsuboyama Mitsuaki Tatsumi Kazuya Ogawa Noriyuki Tomiyama |
| author_sort | Takashi Ota |
| collection | DOAJ |
| description | <i>Background</i>: 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. <i>Methods</i>: Fifty-four patients (56 liver tumors: hepatocellular carcinoma (HCC), 31; metastases, 25) who underwent MRE, diffusion-weighted imaging (DWI) (<i>b</i>: 0, 800 s/mm<sup>2</sup>), and VMRE (<i>b</i>: 200, 1500 s/mm<sup>2</sup>) were enrolled. The MRE shear modulus (µ<sub>MRE</sub>), apparent diffusion coefficient (ADC), and shifted ADC (sADC) were obtained. Virtual stiffness (µ<sub>diff</sub>) was estimated from the relationship between µ<sub>MRE</sub> 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<sub>MRE</sub>, 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. <i>Results</i>: The correlations between µ<sub>MRE</sub> and sADC were significant for liver, HCC, and metastases (<i>r</i> = 0.91, 0.68, 0.71; all <i>p</i> < 0.05). Those between µ<sub>MRE</sub> and ADC were weaker and significant only for metastases (<i>r</i> = 0.17, 0.20, 0.55). µ<sub>diff</sub> values were not significantly different between HCC and metastases (<i>p</i> = 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, <i>p</i> = 0.046). <i>Conclusions</i>: The correlation between sADC and µ<sub>MRE</sub> 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. |
| first_indexed | 2024-03-10T06:58:09Z |
| format | Article |
| id | doaj.art-9cecd8509e7c4a6b935a8dcc85a6b54e |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T06:58:09Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-9cecd8509e7c4a6b935a8dcc85a6b54e2023-11-22T16:21:07ZengMDPI AGJournal of Clinical Medicine2077-03832021-09-011019455310.3390/jcm10194553Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis?Takashi Ota0Masatoshi Hori1Denis Le Bihan2Hideyuki Fukui3Hiromitsu Onishi4Atsushi Nakamoto5Takahiro Tsuboyama6Mitsuaki Tatsumi7Kazuya Ogawa8Noriyuki Tomiyama9Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanNeuroSpin, CEA-Saclay, Paris-Saclay University, 91191 Saclay, FranceDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, JapanDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan<i>Background</i>: 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. <i>Methods</i>: Fifty-four patients (56 liver tumors: hepatocellular carcinoma (HCC), 31; metastases, 25) who underwent MRE, diffusion-weighted imaging (DWI) (<i>b</i>: 0, 800 s/mm<sup>2</sup>), and VMRE (<i>b</i>: 200, 1500 s/mm<sup>2</sup>) were enrolled. The MRE shear modulus (µ<sub>MRE</sub>), apparent diffusion coefficient (ADC), and shifted ADC (sADC) were obtained. Virtual stiffness (µ<sub>diff</sub>) was estimated from the relationship between µ<sub>MRE</sub> 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<sub>MRE</sub>, 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. <i>Results</i>: The correlations between µ<sub>MRE</sub> and sADC were significant for liver, HCC, and metastases (<i>r</i> = 0.91, 0.68, 0.71; all <i>p</i> < 0.05). Those between µ<sub>MRE</sub> and ADC were weaker and significant only for metastases (<i>r</i> = 0.17, 0.20, 0.55). µ<sub>diff</sub> values were not significantly different between HCC and metastases (<i>p</i> = 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, <i>p</i> = 0.046). <i>Conclusions</i>: The correlation between sADC and µ<sub>MRE</sub> 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.https://www.mdpi.com/2077-0383/10/19/4553diffusion-weighted imagingMR elastographyvirtual MR elastographyliverhepatocellular carcinomametastatic liver cancer |
| spellingShingle | Takashi Ota Masatoshi Hori Denis Le Bihan Hideyuki Fukui Hiromitsu Onishi Atsushi Nakamoto Takahiro Tsuboyama Mitsuaki Tatsumi Kazuya Ogawa Noriyuki Tomiyama Diffusion–Based Virtual MR Elastography of the Liver: Can It Be Extended beyond Liver Fibrosis? Journal of Clinical Medicine diffusion-weighted imaging MR elastography virtual MR elastography liver hepatocellular carcinoma metastatic liver cancer |
| 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 | diffusion-weighted imaging MR elastography virtual MR elastography liver hepatocellular carcinoma metastatic liver cancer |
| url | https://www.mdpi.com/2077-0383/10/19/4553 |
| work_keys_str_mv | AT takashiota diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT masatoshihori diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT denislebihan diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT hideyukifukui diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT hiromitsuonishi diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT atsushinakamoto diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT takahirotsuboyama diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT mitsuakitatsumi diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT kazuyaogawa diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis AT noriyukitomiyama diffusionbasedvirtualmrelastographyofthelivercanitbeextendedbeyondliverfibrosis |