Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Park, Hyo Jung, Yoon, Jee Seok, Lee, Seung Soo, Suk, Heung-Il, Park, Bumwoo, Sung, Yu Sub, Hong, Seung Baek, Ryu, Hwaseong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240292/
https://www.ncbi.nlm.nih.gov/pubmed/35434977
http://dx.doi.org/10.3348/kjr.2021.0892
_version_ 1784737506753773568
author Park, Hyo Jung
Yoon, Jee Seok
Lee, Seung Soo
Suk, Heung-Il
Park, Bumwoo
Sung, Yu Sub
Hong, Seung Baek
Ryu, Hwaseong
author_facet Park, Hyo Jung
Yoon, Jee Seok
Lee, Seung Soo
Suk, Heung-Il
Park, Bumwoo
Sung, Yu Sub
Hong, Seung Baek
Ryu, Hwaseong
author_sort Park, Hyo Jung
collection PubMed
description 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 × LV(BSA), and aLSSR × 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 × 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.
format Online
Article
Text
id pubmed-9240292
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher The Korean Society of Radiology
record_format MEDLINE/PubMed
spelling pubmed-92402922022-07-07 Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI Park, Hyo Jung Yoon, Jee Seok Lee, Seung Soo Suk, Heung-Il Park, Bumwoo Sung, Yu Sub Hong, Seung Baek Ryu, Hwaseong Korean J Radiol Gastrointestinal Imaging 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 × LV(BSA), and aLSSR × 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 × 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. The Korean Society of Radiology 2022-07 2022-04-04 /pmc/articles/PMC9240292/ /pubmed/35434977 http://dx.doi.org/10.3348/kjr.2021.0892 Text en Copyright © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Gastrointestinal Imaging
Park, Hyo Jung
Yoon, Jee Seok
Lee, Seung Soo
Suk, Heung-Il
Park, Bumwoo
Sung, Yu Sub
Hong, Seung Baek
Ryu, Hwaseong
Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI
title Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI
title_full Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI
title_fullStr Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI
title_full_unstemmed Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI
title_short Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI
title_sort deep learning-based assessment of functional liver capacity using gadoxetic acid-enhanced hepatobiliary phase mri
topic Gastrointestinal Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240292/
https://www.ncbi.nlm.nih.gov/pubmed/35434977
http://dx.doi.org/10.3348/kjr.2021.0892
work_keys_str_mv AT parkhyojung deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri
AT yoonjeeseok deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri
AT leeseungsoo deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri
AT sukheungil deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri
AT parkbumwoo deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri
AT sungyusub deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri
AT hongseungbaek deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri
AT ryuhwaseong deeplearningbasedassessmentoffunctionallivercapacityusinggadoxeticacidenhancedhepatobiliaryphasemri