Diagnostic performance of microvascular flow imaging for noninvasive assessment of liver fibrosis in chronic liver disease
Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools. Betw...
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| Vydáno v: | PloS one Ročník 20; číslo 6; s. e0322102 |
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Public Library of Science
04.06.2025
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| Abstract | Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools.
Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value.
A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system's cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system's performance in predicting advanced fibrosis (stage 3) was comparable to TE (p = 0.170) and 2D-SWE (p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%.
MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD. |
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| AbstractList | Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools. Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value. A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system's cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system's performance in predicting advanced fibrosis (stage 3) was comparable to TE (p = 0.170) and 2D-SWE (p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%. MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD. Background and aims Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools. Methods Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value. Results A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system's cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system's performance in predicting advanced fibrosis (stage 3) was comparable to TE (p = 0.170) and 2D-SWE (p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%. Conclusion MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD. Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools. Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value. A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system's cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system's performance in predicting advanced fibrosis (stage 3) was comparable to TE (p = 0.170) and 2D-SWE (p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%. MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD. Background and aimsChronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools.MethodsBetween July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value.ResultsA total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system’s cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system’s performance in predicting advanced fibrosis (stage 3) was comparable to TE (p = 0.170) and 2D-SWE (p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%.ConclusionMV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD. Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools.BACKGROUND AND AIMSChronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools.Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value.METHODSBetween July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value.A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system's cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system's performance in predicting advanced fibrosis (stage 3) was comparable to TE (p = 0.170) and 2D-SWE (p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%.RESULTSA total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system's cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system's performance in predicting advanced fibrosis (stage 3) was comparable to TE (p = 0.170) and 2D-SWE (p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%.MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD.CONCLUSIONMV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD. Background and aims Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools. Methods Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value. Results A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system’s cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system’s performance in predicting advanced fibrosis (stage 3) was comparable to TE ( p = 0.170) and 2D-SWE ( p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%. Conclusion MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD. |
| Audience | Academic |
| Author | Kim, Sang Gyune Jeong, Soung Won Kim, Hong Soo Lee, Sae Hwan Yoo, Jeong-Ju Cheon, Gab Jin Yoo, Hae Won Yang, Chan Jin Kim, Young Seok Chang, Young Jang, Jae Young |
| AuthorAffiliation | 1 Department of Internal Medicine, Division of Gastroenterology and Hepatology, Soon Chun Hyang University Bucheon Hospital, Bucheon, Korea 4 Department of Internal Medicine, Gangneung Asan Hospital, Gangneung, Korea Changhua Christian Hospital, TAIWAN 2 Department of Internal Medicine, Soon Chun Hyang University Seoul Hospital, Seoul, Korea 3 Department of Internal Medicine, Soon Chun Hyang University Chunan Hospital, Chunan, Korea |
| AuthorAffiliation_xml | – name: 1 Department of Internal Medicine, Division of Gastroenterology and Hepatology, Soon Chun Hyang University Bucheon Hospital, Bucheon, Korea – name: Changhua Christian Hospital, TAIWAN – name: 2 Department of Internal Medicine, Soon Chun Hyang University Seoul Hospital, Seoul, Korea – name: 3 Department of Internal Medicine, Soon Chun Hyang University Chunan Hospital, Chunan, Korea – name: 4 Department of Internal Medicine, Gangneung Asan Hospital, Gangneung, Korea |
| Author_xml | – sequence: 1 givenname: Hae Won surname: Yoo fullname: Yoo, Hae Won – sequence: 2 givenname: Chan Jin surname: Yang fullname: Yang, Chan Jin – sequence: 3 givenname: Jeong-Ju surname: Yoo fullname: Yoo, Jeong-Ju – sequence: 4 givenname: Young surname: Chang fullname: Chang, Young – sequence: 5 givenname: Sae Hwan surname: Lee fullname: Lee, Sae Hwan – sequence: 6 givenname: Soung Won surname: Jeong fullname: Jeong, Soung Won – sequence: 7 givenname: Jae Young surname: Jang fullname: Jang, Jae Young – sequence: 8 givenname: Gab Jin surname: Cheon fullname: Cheon, Gab Jin – sequence: 9 givenname: Young Seok orcidid: 0000-0002-7113-3623 surname: Kim fullname: Kim, Young Seok – sequence: 10 givenname: Hong Soo surname: Kim fullname: Kim, Hong Soo – sequence: 11 givenname: Sang Gyune orcidid: 0000-0001-8694-777X surname: Kim fullname: Kim, Sang Gyune |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40465598$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright: © 2025 Yoo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Yoo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Yoo et al 2025 Yoo et al 2025 Yoo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1371/journal.pone.0322102 |
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| License | Copyright: © 2025 Yoo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
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