Prediction of liver fibrosis using CT under respiratory control: New attempt using deformation vectors obtained by non-rigid registration technique

Akihiro Nishie, Sadato Akahori, Yoshiki Asayama, Kousei Ishigami, Yasuhiro Ushijima, Daisuke Kakihara, Tomohiro Nakayama, Yukihisa Takayama, Nobuhiro Fujita, Koichiro Morita, Keisuke Ishimatsu, Seiichiro Takao, Tomoharu Yoshizumi, Kenichi Kohashi, Yuanzhong Li, Hiroshi Honda

Research output: Contribution to journalArticle

Abstract

Aim: To investigate whether liver fibrosis can be predicted by quantifying the deformity of the liver obtained based on computed tomographic (CT) images scanned under respiratory control. Materials and Methods: For dynamic CT of 47 patients, portal venous and equilibrium phases were scanned during inspiration and expiration, respectively. After rigid registration of the two images, non-rigid registration of the liver was performed, and the amount and direction of each voxel's shift during non-rigid registration was defined as the deformation vector. The correlation of each CT parameter for the obtained deformation vectors with the pathologically-proven degree of liver fibrosis was assessed using Spearman's rank correlation test. Receiver operating characteristic curve analysis was conducted for prediction of liver fibrosis. Results: The standard deviation, coefficient of variance (CV) and skewness were significantly negatively correlated with the degree of liver fibrosis (p=0.030, 0.009 and 0.037, respectively). Of these measures, CV was best correlated and significantly decreased as liver fibrosis progressed (rho=−0.376). CV showed accuracies of 66.0-70.2%, and the areas under curves were 0.654-0.727 for prediction of fibrosis of grade F1 or greater, F2 or greater, F3 or greater and F4 fibrosis. Conclusion: The deformation vector is a potential CT parameter for evaluating liver fibrosis.

Original languageEnglish
Pages (from-to)1417-1424
Number of pages8
JournalAnticancer research
Volume39
Issue number3
DOIs
Publication statusPublished - Mar 2019

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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