Literature DB >> 30084736

Cardiac MRI Texture Analysis of T1 and T2 Maps in Patients with Infarctlike Acute Myocarditis.

Bettina Baessler1, Christian Luecke1, Julia Lurz1, Karin Klingel1, Maximilian von Roeder1, Suzanne de Waha1, Christian Besler1, David Maintz1, Matthias Gutberlet1, Holger Thiele1, Philipp Lurz1.   

Abstract

Purpose To assess the diagnostic potential of texture analysis applied to T1 and T2 maps obtained with cardiac MRI for the diagnosis of acute infarctlike myocarditis. Materials and Methods This prospective study from August 2012 to May 2015 included 39 participants (overall mean age ± standard deviation, 34.7 years ± 12.2 [range, 18-63 years]; mean age of women, 46.1 years ± 10.8 [range, 24-63 years]; mean age of men, 29.8 years ± 9.2 [range, 18-56 years]) from the Magnetic Resonance Imaging in Myocarditis (MyoRacer) trial with clinical suspicion of acute myocarditis and infarctlike presentation. Participants underwent biventricular endomyocardial biopsy, cardiac catheterization, and cardiac MRI at 1.5 T, in which native T1 and T2 mapping as well as Lake Louise criteria (LLC) were assessed. Texture analysis was applied on T1 and T2 maps by using a freely available software package. Stepwise dimension reduction and texture feature selection was performed for selecting features enabling the diagnosis of myocarditis by using endomyocardial biopsy as the reference standard. Results Endomyocardial biopsy confirmed the diagnosis of acute myocarditis in 26 patients, whereas 13 participants had no signs of acute inflammation. Mean T1 and T2 values and LLC showed a low diagnostic performance, with area under the curve in receiver operating curve analyses as follows: 0.65 (95% confidence interval [CI]: 0.45, 0.85) for T1, 0.67 (95% CI: 0.49, 0.85) for T2, and 0.62 (95% CI: 0.42, 0.79) for LLC. Combining the texture features T2 run-length nonuniformity and gray-level nonuniformity resulted in higher diagnostic performance with an area under the curve of 0.88 (95% CI: 0.73, 1.00) (P < .001) and a sensitivity and specificity of 89% [95% CI: 81%, 93%] and 92% [95% CI: 77%, 93%], respectively. Conclusion Texture analysis of T2 maps shows high sensitivity and specificity for the diagnosis of acute infarctlike myocarditis. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 30084736     DOI: 10.1148/radiol.2018180411

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  42 in total

Review 1.  Cardiac MRI Evaluation of Myocarditis.

Authors:  Lewis Hahn; Seth Kligerman
Journal:  Curr Treat Options Cardiovasc Med       Date:  2019-11-16

2.  Radiomics for Distinguishing Myocardial Infarction from Myocarditis at Late Gadolinium Enhancement at MRI: Comparison with Subjective Visual Analysis.

Authors:  Tommaso Di Noto; Jochen von Spiczak; Manoj Mannil; Elena Gantert; Paolo Soda; Robert Manka; Hatem Alkadhi
Journal:  Radiol Cardiothorac Imaging       Date:  2019-12-19

3.  Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.

Authors:  Suyon Chang; Kyunghwa Han; Young Joo Suh; Byoung Wook Choi
Journal:  Eur Radiol       Date:  2022-03-01       Impact factor: 5.315

4.  Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy.

Authors:  Ulf Neisius; Hossam El-Rewaidy; Shiro Nakamori; Jennifer Rodriguez; Warren J Manning; Reza Nezafat
Journal:  JACC Cardiovasc Imaging       Date:  2019-01-16

5.  Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.

Authors:  Zhen-Yu Shu; Si-Jia Cui; Yue-Qiao Zhang; Yu-Yun Xu; Shng-Che Hung; Li-Ping Fu; Pei-Pei Pang; Xiang-Yang Gong; Qin-Yang Jin
Journal:  J Nucl Cardiol       Date:  2020-06-18       Impact factor: 5.952

Review 6.  Clinical Importance of Myocardial T2 Mapping and Texture Analysis.

Authors:  Yasuo Amano; Yuko Omori; Chisato Ando; Fumi Yanagisawa; Yasuyuki Suzuki; Xiaoyan Tang; Hiroko Kobayashi; Ryo Takagi; Naoya Matsumoto
Journal:  Magn Reson Med Sci       Date:  2020-05-11       Impact factor: 2.471

Review 7.  Artificial intelligence: improving the efficiency of cardiovascular imaging.

Authors:  Andrew Lin; Márton Kolossváry; Ivana Išgum; Pál Maurovich-Horvat; Piotr J Slomka; Damini Dey
Journal:  Expert Rev Med Devices       Date:  2020-06-16       Impact factor: 3.166

8.  Segmental strain analysis for the detection of chronic ischemic scars in non-contrast cardiac MRI cine images.

Authors:  M Polacin; M Karolyi; M Eberhard; A Gotschy; B Baessler; H Alkadhi; S Kozerke; R Manka
Journal:  Sci Rep       Date:  2021-06-11       Impact factor: 4.379

9.  Reproducibility of Segmentation-based Myocardial Radiomic Features with Cardiac MRI.

Authors:  Jihye Jang; Long H Ngo; Jennifer Mancio; Selcuk Kucukseymen; Jennifer Rodriguez; Patrick Pierce; Beth Goddu; Reza Nezafat
Journal:  Radiol Cardiothorac Imaging       Date:  2020-06-25

10.  Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models.

Authors:  Sarv Priya; Tanya Aggarwal; Caitlin Ward; Girish Bathla; Mathews Jacob; Alicia Gerke; Eric A Hoffman; Prashant Nagpal
Journal:  Sci Rep       Date:  2021-06-16       Impact factor: 4.996

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