A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI

Abstract The implementation of radiomics in radiology is gaining interest due to its wide range of applications. To develop a radiomics-based model for classifying the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI, 248 patients with a known etiology of liver cirrhosis who underwent 3...

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Autores principales:	Aboelyazid Elkilany, Uli Fehrenbach, Timo Alexander Auer, Tobias Müller, Wenzel Schöning, Bernd Hamm, Dominik Geisel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f735e45fbe144a9790fb2b0dec16d6ed
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spelling	oai:doaj.org-article:f735e45fbe144a9790fb2b0dec16d6ed2021-12-02T15:49:31ZA radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI10.1038/s41598-021-90257-92045-2322https://doaj.org/article/f735e45fbe144a9790fb2b0dec16d6ed2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90257-9https://doaj.org/toc/2045-2322Abstract The implementation of radiomics in radiology is gaining interest due to its wide range of applications. To develop a radiomics-based model for classifying the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI, 248 patients with a known etiology of liver cirrhosis who underwent 306 gadoxetic acid-enhanced MRI examinations were included in the analysis. MRI examinations were classified into 6 groups according to the etiology of liver cirrhosis: alcoholic cirrhosis, viral hepatitis, cholestatic liver disease, nonalcoholic steatohepatitis (NASH), autoimmune hepatitis, and other. MRI examinations were randomized into training and testing subsets. Radiomics features were extracted from regions of interest segmented in the hepatobiliary phase images. The fivefold cross-validated models (2-dimensional—(2D) and 3-dimensional—(3D) based) differentiating cholestatic cirrhosis from noncholestatic etiologies had the best accuracy (87.5%, 85.6%), sensitivity (97.6%, 95.6%), predictive value (0.883, 0.877), and area under curve (AUC) (0.960, 0.910). The AUC was larger in the 2D-model for viral hepatitis, cholestatic cirrhosis, and NASH-associated cirrhosis (P-value of 0.05, 0.05, 0.87, respectively). In alcoholic cirrhosis, the AUC for the 3D model was larger (P = 0.01). The overall intra-class correlation coefficient (ICC) estimates and their 95% confident intervals (CI) for all features combined was 0.68 (CI 0.56–0.87) for 2D and 0.71 (CI 0.61–0.93) for 3D measurements suggesting moderate reliability. Radiomics-based analysis of hepatobiliary phase images of gadoxetic acid-enhanced MRI may be a promising noninvasive method for identifying the etiology of liver cirrhosis with better performance of the 2D- compared with the 3D-generated models.Aboelyazid ElkilanyUli FehrenbachTimo Alexander AuerTobias MüllerWenzel SchöningBernd HammDominik GeiselNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aboelyazid Elkilany Uli Fehrenbach Timo Alexander Auer Tobias Müller Wenzel Schöning Bernd Hamm Dominik Geisel A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI
description	Abstract The implementation of radiomics in radiology is gaining interest due to its wide range of applications. To develop a radiomics-based model for classifying the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI, 248 patients with a known etiology of liver cirrhosis who underwent 306 gadoxetic acid-enhanced MRI examinations were included in the analysis. MRI examinations were classified into 6 groups according to the etiology of liver cirrhosis: alcoholic cirrhosis, viral hepatitis, cholestatic liver disease, nonalcoholic steatohepatitis (NASH), autoimmune hepatitis, and other. MRI examinations were randomized into training and testing subsets. Radiomics features were extracted from regions of interest segmented in the hepatobiliary phase images. The fivefold cross-validated models (2-dimensional—(2D) and 3-dimensional—(3D) based) differentiating cholestatic cirrhosis from noncholestatic etiologies had the best accuracy (87.5%, 85.6%), sensitivity (97.6%, 95.6%), predictive value (0.883, 0.877), and area under curve (AUC) (0.960, 0.910). The AUC was larger in the 2D-model for viral hepatitis, cholestatic cirrhosis, and NASH-associated cirrhosis (P-value of 0.05, 0.05, 0.87, respectively). In alcoholic cirrhosis, the AUC for the 3D model was larger (P = 0.01). The overall intra-class correlation coefficient (ICC) estimates and their 95% confident intervals (CI) for all features combined was 0.68 (CI 0.56–0.87) for 2D and 0.71 (CI 0.61–0.93) for 3D measurements suggesting moderate reliability. Radiomics-based analysis of hepatobiliary phase images of gadoxetic acid-enhanced MRI may be a promising noninvasive method for identifying the etiology of liver cirrhosis with better performance of the 2D- compared with the 3D-generated models.
format	article
author	Aboelyazid Elkilany Uli Fehrenbach Timo Alexander Auer Tobias Müller Wenzel Schöning Bernd Hamm Dominik Geisel
author_facet	Aboelyazid Elkilany Uli Fehrenbach Timo Alexander Auer Tobias Müller Wenzel Schöning Bernd Hamm Dominik Geisel
author_sort	Aboelyazid Elkilany
title	A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI
title_short	A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI
title_full	A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI
title_fullStr	A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI
title_full_unstemmed	A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI
title_sort	radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced mri
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f735e45fbe144a9790fb2b0dec16d6ed
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A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI

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