dc.contributor.author
Michallek, Florian
dc.contributor.author
Sartoris, Riccardo
dc.contributor.author
Beaufrère, Aurélie
dc.contributor.author
Dioguardi Burgio, Marco
dc.contributor.author
Cauchy, François
dc.contributor.author
Cannella, Roberto
dc.contributor.author
Paradis, Valérie
dc.contributor.author
Ronot, Maxime
dc.contributor.author
Dewey, Marc
dc.contributor.author
Vilgrain, Valérie
dc.date.accessioned
2023-12-13T14:25:02Z
dc.date.available
2023-12-13T14:25:02Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/41868
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-41589
dc.description.abstract
Background: To investigate whether fractal analysis of perfusion differentiates hepatocellular adenoma (HCA) subtypes and hepatocellular carcinoma (HCC) in non-cirrhotic liver by quantifying perfusion chaos using four-dimensional dynamic contrast-enhanced magnetic resonance imaging (4D-DCE-MRI).
Results: A retrospective population of 63 patients (47 female) with histopathologically characterized HCA and HCC in non-cirrhotic livers was investigated. Our population consisted of 13 hepatocyte nuclear factor (HNF)-1 alpha-inactivated (H-HCAs), 7 beta-catenin-exon-3-mutated (b(ex3)-HCAs), 27 inflammatory HCAs (I-HCAs), and 16 HCCs. Four-dimensional fractal analysis was applied to arterial, portal venous, and delayed phases of 4D-DCE-MRI and was performed in lesions as well as remote liver tissue. Diagnostic accuracy of fractal analysis was compared to qualitative MRI features alone and their combination using multi-class diagnostic accuracy testing including kappa-statistics and area under the receiver operating characteristic curve (AUC). Fractal analysis allowed quantification of perfusion chaos, which was significantly different between lesion subtypes (multi-class AUC = 0.90, p < 0.001), except between I-HCA and HCC. Qualitative MRI features alone did not allow reliable differentiation between HCA subtypes and HCC (kappa = 0.35). However, combining qualitative MRI features and fractal analysis reliably predicted the histopathological diagnosis (kappa = 0.89) and improved differentiation of high-risk lesions (i.e., HCCs, b(ex3)-HCAs) and low-risk lesions (H-HCAs, I-HCAs) from sensitivity and specificity of 43% (95% confidence interval [CI] 23-66%) and 47% (CI 32-64%) for qualitative MRI features to 96% (CI 78-100%) and 68% (CI 51-81%), respectively, when adding fractal analysis.
Conclusions: Combining qualitative MRI features with fractal analysis allows identification of HCA subtypes and HCCs in patients with non-cirrhotic livers and improves differentiation of lesions with high and low risk for malignant transformation.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Hepatocellular adenoma
en
dc.subject
Hepatocellular carcinoma
en
dc.subject
Magnetic resonance imaging
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Differentiation of hepatocellular adenoma by subtype and hepatocellular carcinoma in non-cirrhotic liver by fractal analysis of perfusion MRI
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
81
dcterms.bibliographicCitation.doi
10.1186/s13244-022-01223-6
dcterms.bibliographicCitation.journaltitle
Insights into Imaging
dcterms.bibliographicCitation.number
1
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.volume
13
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
35482151
dcterms.isPartOf.eissn
1869-4101