dc.contributor.author
Elkilany, Aboelyazid
dc.contributor.author
Fehrenbach, Uli
dc.contributor.author
Auer, Timo Alexander
dc.contributor.author
Müller, Tobias
dc.contributor.author
Schöning, Wenzel
dc.contributor.author
Hamm, Bernd
dc.contributor.author
Geisel, Dominik
dc.date.accessioned
2023-03-03T13:45:08Z
dc.date.available
2023-03-03T13:45:08Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/38200
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-37917
dc.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.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
liver cirrhosis
en
dc.subject
radiomics-based model
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
10778
dcterms.bibliographicCitation.doi
10.1038/s41598-021-90257-9
dcterms.bibliographicCitation.journaltitle
Scientific Reports
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.volume
11
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
34031487
dcterms.isPartOf.eissn
2045-2322