Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease.

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ID: 96017
2020
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Abstract
To evaluate whether the liver and spleen volumetric indices, measured on portal venous phase CT images, could be used to assess liver fibrosis severity in chronic liver disease.From 2007 to 2017, 558 patients (mean age 48.7 ± 13.1 years; 284 men and 274 women) with chronic liver disease (n = 513) or healthy liver (n = 45) were retrospectively enrolled. The liver volume (sVol) and spleen volume (sVol), normalized to body surface area and liver-to-spleen volume ratio (Vol/Vol), were measured on CT images using a deep learning algorithm. The correlation between the volumetric indices and the pathologic liver fibrosis stages combined with the presence of decompensation (F0, F1, F2, F3, F4C [compensated cirrhosis], and F4D [decompensated cirrhosis]) were assessed using Spearman's correlation coefficient. The performance of the volumetric indices in the diagnosis of advanced fibrosis, cirrhosis, and decompensated cirrhosis were evaluated using the area under the receiver operating characteristic curve (AUC).The sVol (ρ = 0.47-0.73; p < .001) and Vol/Vol (ρ = -0.77-- 0.48; p < .001) showed significant correlation with liver fibrosis stage in all etiological subgroups (i.e., viral hepatitis, alcoholic and non-alcoholic fatty liver, and autoimmune diseases), while the significant correlation of sVol was noted only in the viral hepatitis subgroup (ρ = - 0.55; p < .001). To diagnose advanced fibrosis, cirrhosis, and decompensated cirrhosis, the Vol/Vol (AUC 0.82-0.88) and sVol (AUC 0.82-0.87) significantly outperformed the sVol (AUC 0.63-0.72; p < .001).The Vol/Vol and sVol may be used for assessing liver fibrosis severity in chronic liver disease.• Volumetric indices of liver and spleen measured on computed tomography images may allow liver fibrosis severity to be assessed in patients with chronic liver disease.
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son2020assessmenteuropean Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Son, Jung Hee;Lee, Seung Soo;Lee, Yedaun;Kang, Bo-Kyeong;Sung, Yu Sub;Jo, SoRa;Yu, Eunsil;
Journal European radiology
Year 2020
DOI
10.1007/s00330-020-06665-4
URL
Keywords
Deep learning Liver Fibrosis multidetector computed tomography organ volume

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