A novel fast kilovoltage switching dual-energy computed tomography technique with deep learning: Utility for non-invasive assessments of liver fibrosis

被引：12

作者：

Wada, Noriaki ^{[1
]}

Fujita, Nobuhiro ^{[1
,5
]}

Ishimatsu, Keisuke ^{[1
]}

Takao, Seiichiro ^{[1
]}

Yoshizumi, Tomoharu ^{[2
]}

Miyazaki, Yoshiko ^{[3
]}

Oda, Yoshinao ^{[3
]}

Nishie, Akihiro ^{[4
]}

Ishigami, Kousei ^{[1
]}

Ushijima, Yasuhiro ^{[1
]}

机构：

[1] Kyushu Univ, Grad Sch Med Sci, Dept Clin Radiol, Fukuoka, Japan

[2] Kyushu Univ, Grad Sch Med Sci, Dept Surg & Sci, Fukuoka, Japan

[3] Kyushu Univ, Grad Sch Med Sci, Dept Anat Pathol, Fukuoka, Japan

[4] Univ Ryukyus, Grad Sch Med Sci, Dept Radiol, Okinawa, Japan

[5] Kyushu Univ, Grad Sch Med Sci, Dept Clin Radiol, 3-1-1 Maidashi,Higashi Ku, Fukuoka 8128582, Japan

来源：

EUROPEAN JOURNAL OF RADIOLOGY | 2022年 / 155卷

基金：

日本学术振兴会;

关键词：

Deep learning-based spectral CT; Iodine density; Extracellular volume; Liver fibrosis; SAMPLING VARIABILITY; CT; ELASTOGRAPHY; DISEASE; REGRESSION; CIRRHOSIS; BIOPSY;

D O I：

10.1016/j.ejrad.2022.110461

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Purpose: To investigate whether the iodine density of liver parenchyma in the equilibrium phase and extracellular volume fraction (ECV) measured by deep learning-based spectral computed tomography (CT) can enable noninvasive liver fibrosis staging. Method: We retrospectively analyzed 63 patients who underwent dynamic CT using deep learning-based spectral CT before a hepatectomy or liver transplantation. The iodine densities of the liver parenchyma (I-liver) and abdominal aorta (I-aorta) were independently measured by two radiologists using iodine density images at the equilibrium phase. The iodine-density ratio (I-ratio: I-liver/I-aorta) and CT-ECV were calculated. Spearman's rank correlation analysis was used to evaluate the relationship between the I-ratio or CT-ECV and liver fibrosis stage, and receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performances of the I-ratio and CT-ECV. Results: The I-ratio and CT-ECV showed significant positive correlations with liver fibrosis stage (rho = 0.648, p < 0.0001 and rho = 0.723, p < 0.0001, respectively). The areas under the ROC curve for the CT-ECV were 0.882 (F0 vs >= F1), 0.873 (<= F1 vs >= F2), 0.848 (<= F2 vs >= F3), and 0.891 (<= F3 vs F4). Conclusions: Deep learning-based spectral CT may be useful for noninvasive assessments of liver fibrosis.

引用

页数：8

共 38 条

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