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

Korean J Radiol. 2022 Jul;23(7):720-731. doi: 10.3348/kjr.2021.0892. Epub 2022 Apr 4.

Authors

Hyo Jung Park^#¹, Jee Seok Yoon^#², Seung Soo Lee³, Heung-Il Suk^{2

4}, Bumwoo Park¹, Yu Sub Sung¹, Seung Baek Hong⁵, Hwaseong Ryu⁶

Affiliations

¹ Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
² Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
³ Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. seungsoolee@amc.seoul.kr.
⁴ Department of Artificial Intelligence, Korea University, Seoul, Korea. hisuk@korea.ac.kr.
⁵ Department of Radiology, Pusan National University Hospital, Busan, Korea.
⁶ Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.

^# Contributed equally.

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 × 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.

Keywords: Deep learning; Gadoxetic acid; Liver; Magnetic resonance imaging; Volumetry.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Contrast Media
Deep Learning*
Female
Gadolinium DTPA
Humans
Liver / diagnostic imaging
Liver / pathology
Liver Neoplasms* / pathology
Magnetic Resonance Imaging / methods
Male
Middle Aged
Retrospective Studies

Substances

Contrast Media
gadolinium ethoxybenzyl DTPA
Gadolinium DTPA