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Start Over Author "Doppler, Michael" Topic liver fibrosis
2 results on '"Doppler, Michael"'

1. Diagnostic Accuracy of Indocyanine Green Clearance Test for Different Stages of Liver Fibrosis and Cirrhosis.

Author

Luerken, Lukas, Dollinger, Marco, Goetz, Andrea, Utpatel, Kirstin, Doppler, Michael Christian, Weiss, Jakob Benedikt, Uller, Wibke, Ignee, André, Verloh, Niklas, and Haimerl, Michael

Subjects
HEPATIC fibrosis, CIRRHOSIS of the liver, INDOCYANINE green, NONINVASIVE diagnostic tests, MANN Whitney U Test
Abstract

(1) Background: This study aimed to correlate the indocyanine green clearance (ICG) test with histopathological grades of liver fibrosis and liver cirrhosis to assess its diagnostic accuracy in differentiating normal liver parenchyma from liver fibrosis and liver cirrhosis. (2) Methods: A total of 82 patients who received a histopathological liver examination, imaging, and ICG test within three months were included in this retrospective study. The histopathological level of fibrosis was graded using the Ishak scoring system, and the patients were divided into five categories: no liver fibrosis (NLF), mild liver fibrosis (MLF), advanced liver fibrosis (ALF), severe liver fibrosis (SLF), and liver cirrhosis (LC). The non-parametric Kruskal–Wallis test with post hoc pairwise comparison utilizing Mann–Whitney U tests and Bonferroni adjustment was used to analyze differences in the ICG test results between the patient groups. Cross correlation between the individual fibrosis/cirrhosis stages and the score of the ICG test was performed, and the sensitivity, specificity, and positive and negative predictive values were calculated for each model predicting liver fibrosis/cirrhosis. (3) Results: A significant difference (p ≤ 0.001) between stages of NLF, LF, and LC was found for the ICG parameters (ICG plasma disappearance rate (ICG-PDR) and ICG retention percentage at 15 min (ICG-R15)). The post hoc analysis revealed that NLF significantly differed from SLF (ICG-PDR: p = 0.001; ICG-R15: p = 0.001) and LC (ICG-PDR: p = 0.001; ICG-R15: p = 0.001). ALF also significantly differed from SLF (ICG-PDR: p = 0.033; ICG-R15: p = 0.034) and LC (ICG-PDR: p = 0.014; ICG-R15: p = 0.014). The sensitivity for detection of an initial stage of liver fibrosis compared to no liver fibrosis (Ishak  ≥  1) was 0.40; the corresponding specificity was 0.80. The differentiation of advanced liver fibrosis or cirrhosis (Ishak ≥ 4) compared to other stages of liver fibrosis was 0.75, with a specificity of 0.81. (4) Conclusions: This study shows that the ICG test, as a non-invasive diagnostic test, is able to differentiate patients with no liver fibrosis from patients with advanced liver fibrosis and liver cirrhosis. The ICG test seems to be helpful in monitoring patients with liver fibrosis regarding compensation levels, thus potentially enabling physicians to both detect progression from compensated liver fibrosis to advanced liver fibrosis and cirrhosis and to initiate antifibrotic treatment at an earlier stage. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Application of A U-Net for Map-Like Segmentation and Classification of Discontinuous Fibrosis Distribution in Gd-EOB-DTPA-Enhanced Liver MRI

Author

Strotzer, Quirin David, Winther, Hinrich, Utpatel, Kirsten, Scheiter, Alexander, Fellner, Claudia, Doppler, Michael Christian, Ringe, Kristina Imeen, Raab, Florian, Haimerl, Michael, Uller, Wibke, Stroszczynski, Christian, Luerken, Lukas, and Verloh, Niklas

Subjects
liver fibrosis, cirrhosis, segmentation, Artificial Intelligence, U-Net, convolutional neural network, ddc:610, Clinical Biochemistry, 610 Medizin
Abstract

We aimed to evaluate whether U-shaped convolutional neuronal networks can be used to segment liver parenchyma and indicate the degree of liver fibrosis/cirrhosis at the voxel level using contrast-enhanced magnetic resonance imaging. This retrospective study included 112 examinations with histologically determined liver fibrosis/cirrhosis grade (Ishak score) as the ground truth. The T1-weighted volume-interpolated breath-hold examination sequences of native, arterial, late arterial, portal venous, and hepatobiliary phases were semi-automatically segmented and co-registered. The segmentations were assigned the corresponding Ishak score. In a nested cross-validation procedure, five models of a convolutional neural network with U-Net architecture (nnU-Net) were trained, with the dataset being divided into stratified training/validation (n = 89/90) and holdout test datasets (n = 23/22). The trained models precisely segmented the test data (mean dice similarity coefficient = 0.938) and assigned separate fibrosis scores to each voxel, allowing localization-dependent determination of the degree of fibrosis. The per voxel results were evaluated by the histologically determined fibrosis score. The micro-average area under the receiver operating characteristic curve of this seven-class classification problem (Ishak score 0 to 6) was 0.752 for the test data. The top-three-accuracy-score was 0.750. We conclude that determining fibrosis grade or cirrhosis based on multiphase Gd-EOB-DTPA-enhanced liver MRI seems feasible using a 2D U-Net. Prospective studies with localized biopsies are needed to evaluate the reliability of this model in a clinical setting.

Published
2022

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