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 Kouhashi, Yuanzhong Li, Hiroshi Honda

研究成果: ジャーナルへの寄稿記事

抄録

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.

元の言語英語
ページ(範囲)1417-1424
ページ数8
ジャーナルAnticancer research
39
発行部数3
DOI
出版物ステータス出版済み - 3 1 2019

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Liver Cirrhosis
Fibrosis
Liver
ROC Curve
Area Under Curve

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

これを引用

Prediction of liver fibrosis using CT under respiratory control : New attempt using deformation vectors obtained by non-rigid registration technique. / Nishie, Akihiro; Akahori, Sadato; Asayama, Yoshiki; Ishigami, Kousei; ushijima, yasuhiro; Kakihara, Daisuke; Nakayama, Tomohiro; Takayama, Yukihisa; Fujita, Nobuhiro; Morita, Koichiro; Ishimatsu, Keisuke; Takao, Seiichiro; Yoshizumi, Tomoharu; Kouhashi, Kenichi; Li, Yuanzhong; Honda, Hiroshi.

:: Anticancer research, 巻 39, 番号 3, 01.03.2019, p. 1417-1424.

研究成果: ジャーナルへの寄稿記事

Nishie, Akihiro ; Akahori, Sadato ; Asayama, Yoshiki ; Ishigami, Kousei ; ushijima, yasuhiro ; Kakihara, Daisuke ; Nakayama, Tomohiro ; Takayama, Yukihisa ; Fujita, Nobuhiro ; Morita, Koichiro ; Ishimatsu, Keisuke ; Takao, Seiichiro ; Yoshizumi, Tomoharu ; Kouhashi, Kenichi ; Li, Yuanzhong ; Honda, Hiroshi. / Prediction of liver fibrosis using CT under respiratory control : New attempt using deformation vectors obtained by non-rigid registration technique. :: Anticancer research. 2019 ; 巻 39, 番号 3. pp. 1417-1424.
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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.",
author = "Akihiro Nishie and Sadato Akahori and Yoshiki Asayama and Kousei Ishigami and yasuhiro ushijima and Daisuke Kakihara and Tomohiro Nakayama and Yukihisa Takayama and Nobuhiro Fujita and Koichiro Morita and Keisuke Ishimatsu and Seiichiro Takao and Tomoharu Yoshizumi and Kenichi Kouhashi and Yuanzhong Li and Hiroshi Honda",
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T1 - Prediction of liver fibrosis using CT under respiratory control

T2 - New attempt using deformation vectors obtained by non-rigid registration technique

AU - Nishie, Akihiro

AU - Akahori, Sadato

AU - Asayama, Yoshiki

AU - Ishigami, Kousei

AU - ushijima, yasuhiro

AU - Kakihara, Daisuke

AU - Nakayama, Tomohiro

AU - Takayama, Yukihisa

AU - Fujita, Nobuhiro

AU - Morita, Koichiro

AU - Ishimatsu, Keisuke

AU - Takao, Seiichiro

AU - Yoshizumi, Tomoharu

AU - Kouhashi, Kenichi

AU - Li, Yuanzhong

AU - Honda, Hiroshi

PY - 2019/3/1

Y1 - 2019/3/1

N2 - 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.

AB - 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.

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