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 (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.

Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI

“…The HBP MRI data for the baseline and 1-year follow-up examinations were analyzed. The HBP images were processed using a DLA for automated liver and spleen segmentation [ 11 ] implemented in the software (GoDCSS; SmartCareworks Inc.). The details of the algorithm were described in a previous study [ 11 ].…”

“…The HBP images were processed using a DLA for automated liver and spleen segmentation [ 11 ] implemented in the software (GoDCSS; SmartCareworks Inc.). The details of the algorithm were described in a previous study [ 11 ]. Briefly, the algorithm outlines liver and spleen margins on HBP images while excluding vessels or focal hepatic lesions, with a reported dice similarity score of 95%–98% [ 11 ].…”

“…Additionally, gadoxetic acid-enhanced MRI may assess liver function because the degree of liver enhancement on hepatobiliary phase (HBP) images reflects hepatocyte uptake function [ 9 10 11 ]. The liver-to-spleen signal intensity (SI) ratio (LS-SIR) measured on HBP images was used as an index of functional liver capacity [ 11 ]. In contrast, image-based liver and spleen volumetry may have prognostic implications in patients with chronic liver disease.…”

“…It is also considered an alternative method for HCC surveillance in high-risk patients and those with inadequate ultrasound examinations [ 6 7 8 ]. Additionally, gadoxetic acid-enhanced MRI may assess liver function because the degree of liver enhancement on hepatobiliary phase (HBP) images reflects hepatocyte uptake function [ 9 10 11 ]. The liver-to-spleen signal intensity (SI) ratio (LS-SIR) measured on HBP images was used as an index of functional liver capacity [ 11 ].…”

“…A deep learning algorithm (DLA) has enabled the automated measurement of volumes and SIs of the liver and spleen using gadoxetic acid-enhanced MRI [ 11 ], which facilitates the clinical application of LS-SIR and LS-VR. If these indices can predict the prognosis of chronic liver disease, the use of a DLA could generate add-on information using routine gadoxetic acid-enhanced MRI without additional cost or effort.…”