Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI

Hyo Jung Park; Jee Seok Yoon; Seung Soo Lee; Heung Il Suk; Bumwoo Park; Yu Sub Sung; Seung Baek Hong; Hwaseong Ryu

doi:10.3348/kjr.2021.0892

Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI

Hyo Jung Park, Jee Seok Yoon, Seung Soo Lee, Heung Il Suk, Bumwoo Park, Yu Sub Sung, Seung Baek Hong, Hwaseong Ryu

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14 Citations (Scopus)

Abstract

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LV_BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR x LV_BSA, and aLSSR x LV_BSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume,-0.02% ± 1.28% for the liver SI, and-0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR x LV_BSA showed the strongest correlation with ICG-R15 (r =-0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895–0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

Original language	English
Pages (from-to)	720-731
Number of pages	12
Journal	Korean journal of radiology
Volume	23
Issue number	7
DOIs	https://doi.org/10.3348/kjr.2021.0892
Publication status	Published - 2022 Jul

Bibliographical note

Funding Information:
ORCID iDs Hyo Jung Park https://orcid.org/0000-0002-2364-9940 Jee Seok Yoon https://orcid.org/0000-0003-0721-504X Seung Soo Lee https://orcid.org/0000-0002-5518-2249 Heung-Il Suk https://orcid.org/0000-0001-7019-8962 Bumwoo Park https://orcid.org/0000-0002-1651-364X Yu Sub Sung https://orcid.org/0000-0002-9215-735X Seung Baek Hong https://orcid.org/0000-0002-1731-0430 Hwaseong Ryu https://orcid.org/0000-0003-3143-3733 Funding Statement This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1F1A1048826).

Publisher Copyright:
© 2022 The Korean Society of Radiology.

Keywords

Deep learning
Gadoxetic acid
Liver
Magnetic resonance imaging
Volumetry

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging

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10.3348/kjr.2021.0892

Cite this

@article{2b5853b2f29c4659a97af3ab056fc20a,

title = "Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI",

abstract = "Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LVBSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR x LVBSA, and aLSSR x LVBSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume,-0.02% ± 1.28% for the liver SI, and-0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR x LVBSA showed the strongest correlation with ICG-R15 (r =-0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895–0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.",

keywords = "Deep learning, Gadoxetic acid, Liver, Magnetic resonance imaging, Volumetry",

author = "Park, {Hyo Jung} and Yoon, {Jee Seok} and Lee, {Seung Soo} and Suk, {Heung Il} and Bumwoo Park and Sung, {Yu Sub} and Hong, {Seung Baek} and Hwaseong Ryu",

note = "Funding Information: ORCID iDs Hyo Jung Park https://orcid.org/0000-0002-2364-9940 Jee Seok Yoon https://orcid.org/0000-0003-0721-504X Seung Soo Lee https://orcid.org/0000-0002-5518-2249 Heung-Il Suk https://orcid.org/0000-0001-7019-8962 Bumwoo Park https://orcid.org/0000-0002-1651-364X Yu Sub Sung https://orcid.org/0000-0002-9215-735X Seung Baek Hong https://orcid.org/0000-0002-1731-0430 Hwaseong Ryu https://orcid.org/0000-0003-3143-3733 Funding Statement This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1F1A1048826). Publisher Copyright: {\textcopyright} 2022 The Korean Society of Radiology.",

year = "2022",

month = jul,

doi = "10.3348/kjr.2021.0892",

language = "English",

volume = "23",

pages = "720--731",

journal = "Korean journal of radiology",

issn = "1229-6929",

publisher = "Korean Radiological Society",

number = "7",

}

TY - JOUR

T1 - Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI

AU - Park, Hyo Jung

AU - Yoon, Jee Seok

AU - Lee, Seung Soo

AU - Suk, Heung Il

AU - Park, Bumwoo

AU - Sung, Yu Sub

AU - Hong, Seung Baek

AU - Ryu, Hwaseong

N1 - Funding Information: ORCID iDs Hyo Jung Park https://orcid.org/0000-0002-2364-9940 Jee Seok Yoon https://orcid.org/0000-0003-0721-504X Seung Soo Lee https://orcid.org/0000-0002-5518-2249 Heung-Il Suk https://orcid.org/0000-0001-7019-8962 Bumwoo Park https://orcid.org/0000-0002-1651-364X Yu Sub Sung https://orcid.org/0000-0002-9215-735X Seung Baek Hong https://orcid.org/0000-0002-1731-0430 Hwaseong Ryu https://orcid.org/0000-0003-3143-3733 Funding Statement This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1F1A1048826). Publisher Copyright: © 2022 The Korean Society of Radiology.

PY - 2022/7

Y1 - 2022/7

N2 - Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LVBSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR x LVBSA, and aLSSR x LVBSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume,-0.02% ± 1.28% for the liver SI, and-0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR x LVBSA showed the strongest correlation with ICG-R15 (r =-0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895–0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

AB - Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LVBSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR x LVBSA, and aLSSR x LVBSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume,-0.02% ± 1.28% for the liver SI, and-0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR x LVBSA showed the strongest correlation with ICG-R15 (r =-0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895–0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

KW - Deep learning

KW - Gadoxetic acid

KW - Liver

KW - Magnetic resonance imaging

KW - Volumetry

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JO - Korean journal of radiology

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ER -

Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI

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Keywords

ASJC Scopus subject areas

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