Literature DB >> 27866030

Diagnostic accuracy of CT for the detection of left ventricular myocardial fibrosis in various myocardial diseases.

Hiroyuki Takaoka1, Nobusada Funabashi2, Masae Uehara1, Yasunori Iida3, Yoshio Kobayashi1.   

Abstract

PURPOSE: To evaluate the diagnostic accuracy of computed tomography (CT) for the detection of myocardial fibrosis, we compared the frequency of abnormal late enhancement (LE) in left ventricular myocardium (LVM) on CT with that on gadolinium-enhanced cardiac magnetic resonance (CMR) in patients with various myocardial diseases.
METHODS: Fifty-six patients with suspected various myocardial diseases (19 with hypertrophic cardiomyopathy, 3 with cardiac amyloidosis, 3 with post myocarditis, 2 with dilated cardiomyopathy, 2 with cardiac sarcoidosis, 2 with cardiac tumor, 2 with previous myocardial infarction, 2 with hypertensive heart disease) underwent 1.5-T CMR and cardiac CT within 2months without clinical accidents.
RESULTS: LE on LVM was detected in 31 and 31 patients on CT and CMR, respectively, and in 192 and 197 LVM segments on CT and CMR, respectively, among a total of 952 LVM segments. The sensitivity, specificity, positive and negative predictive values, and consistency for detection of LE on CT in comparison with CMR were 90, 89, 90, 89 and 89%, respectively, on patient-based analysis, and 67, 92, 68, 91 and 87%, respectively, on segment-based analysis. Inter-observer agreement for detection of LE on CT was 0.71 (kappa coefficient), and it was significantly lower than that on CMR (0.82) on segment-based analysis (P<0.05).
CONCLUSIONS: Compared with CMR, diagnostic accuracy of CT for the evaluation of LE in LVM in patients with myocardial diseases was relatively higher on patient-based analysis, but was limited on segment-based analysis, and the inter-observer agreement on CT was significantly lower than that on CMR.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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Year:  2016        PMID: 27866030     DOI: 10.1016/j.ijcard.2016.11.140

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  6 in total

1.  2D speckle-tracking TTE-based quantitative classification of left ventricular myocardium in patients with hypertrophic cardiomyopathy by the presence or the absence of fibrosis and/or hypertrophy.

Authors:  Nobusada Funabashi; Hiroyuki Takaoka; Koya Ozawa; Masae Uehara; Issei Komuro; Yoshio Kobayashi
Journal:  Heart Vessels       Date:  2018-03-22       Impact factor: 2.037

2.  Delayed contrast-enhanced computed tomography in patients with known or suspected cardiac sarcoidosis: A feasibility study.

Authors:  Tadao Aikawa; Noriko Oyama-Manabe; Masanao Naya; Hiroshi Ohira; Ayako Sugimoto; Ichizo Tsujino; Masahiko Obara; Osamu Manabe; Kohsuke Kudo; Hiroyuki Tsutsui; Nagara Tamaki
Journal:  Eur Radiol       Date:  2017-04-05       Impact factor: 5.315

Review 3.  Coronary CT Angiography to Guide Percutaneous Coronary Intervention.

Authors:  Georgios Tzimas; Gaurav S Gulsin; Hidenobu Takagi; Niya Mileva; Jeroen Sonck; Olivier Muller; Jonathon A Leipsic; Carlos Collet
Journal:  Radiol Cardiothorac Imaging       Date:  2022-01-06

4.  Improved Diagnostic Performance of New-generation 320-slice Computed Tomography with Forward-projected Model-based Iterative Reconstruction SoluTion for the Assessment of Late Enhancement in Left Ventricular Myocardium.

Authors:  Hiroyuki Takaoka; Masae Uehara; Yuichi Saito; Joji Ota; Yasunori Iida; Manami Takahashi; Koichi Sano; Issei Komuro; Yoshio Kobayashi
Journal:  Intern Med       Date:  2020-06-02       Impact factor: 1.271

Review 5.  Novel Approaches in Cardiac Imaging for Non-invasive Assessment of Left Heart Myocardial Fibrosis.

Authors:  Giulia Elena Mandoli; Flavio D'Ascenzi; Giulia Vinco; Giovanni Benfari; Fabrizio Ricci; Marta Focardi; Luna Cavigli; Maria Concetta Pastore; Nicolò Sisti; Oreste De Vivo; Ciro Santoro; Sergio Mondillo; Matteo Cameli
Journal:  Front Cardiovasc Med       Date:  2021-04-15

6.  The auto segmentation for cardiac structures using a dual-input deep learning network based on vision saliency and transformer.

Authors:  Jing Wang; Shuyu Wang; Wei Liang; Nan Zhang; Yan Zhang
Journal:  J Appl Clin Med Phys       Date:  2022-04-01       Impact factor: 2.243

  6 in total

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