Literature DB >> 31361205

Cardiac MRI and Texture Analysis of Myocardial T1 and T2 Maps in Myocarditis with Acute versus Chronic Symptoms of Heart Failure.

Bettina Baessler1, Christian Luecke1, Julia Lurz1, Karin Klingel1, Arijit Das1, Maximilian von Roeder1, Suzanne de Waha-Thiele1, Christian Besler1, Karl-Philipp Rommel1, David Maintz1, Matthias Gutberlet1, Holger Thiele1, Philipp Lurz1.   

Abstract

BackgroundThe establishment of a timely and correct diagnosis in heart failure-like myocarditis remains one of the most challenging in clinical cardiology.PurposeTo assess the diagnostic potential of texture analysis in heart failure-like myocarditis with comparison to endomyocardial biopsy (EMB) as the reference standard.Materials and MethodsSeventy-one study participants from the Magnetic Resonance Imaging in Myocarditis (MyoRacer) trial (ClinicalTrials.gov registration no. NCT02177630) with clinical suspicion for myocarditis and symptoms of heart failure were prospectively included (from August 2012 to May 2015) in the study. Participants underwent biventricular EMB and cardiac MRI at 1.5 T, including native T1 and T2 mapping and standard Lake Louise criteria. Texture analysis was applied on T1 and T2 maps by using an open-source software. Stepwise dimension reduction was performed for selecting features enabling the diagnosis of myocarditis. Diagnostic performance was assessed from the area under the curve (AUC) from receiver operating characteristic analyses with 10-fold cross validation.ResultsIn participants with acute heart failure-like myocarditis (n = 31; mean age, 47 years ± 17; 10 women), the texture feature GrayLevelNonUniformity from T2 maps (T2_GLNU) showed diagnostic performance similar to that of mean myocardial T2 time (AUC, 0.69 for both). The combination of mean T2 time and T2_GLNU had the highest AUC (0.76; 95% confidence interval [CI]: 0.43, 0.95), with sensitivity of 81% (25 of 31) and specificity of 71% (22 of 31). In patients with chronic heart failure-like myocarditis (n = 40; mean age, 48 years ± 13; 12 women), the histogram feature T2_kurtosis demonstrated superior diagnostic performance compared to that of all other single parameters (AUC, 0.81; 95% CI: 0.66, 0.96). The combination of the two texture features, T2_kurtosis and the GrayLevelNonUniformity from T1, had the highest diagnostic performance (AUC, 0.85; 95% CI: 0.57, 0.90; sensitivity, 90% [36 of 40]; and specificity, 72% [29 of 40]).ConclusionIn this proof-of-concept study, texture analysis applied on cardiac MRI T1 and T2 mapping delivers quantitative imaging parameters for the diagnosis of acute or chronic heart failure-like myocarditis and might be superior to Lake Louise criteria or averaged myocardial T1 or T2 values.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by de Roos in this issue.

Entities:  

Year:  2019        PMID: 31361205     DOI: 10.1148/radiol.2019190101

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


  27 in total

1.  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

Review 2.  [Artificial intelligence and radiomics : Value in cardiac MRI].

Authors:  Alexander Rau; Martin Soschynski; Jana Taron; Philipp Ruile; Christopher L Schlett; Fabian Bamberg; Tobias Krauss
Journal:  Radiologie (Heidelb)       Date:  2022-08-25

Review 3.  Challenges in ensuring the generalizability of image quantitation methods for MRI.

Authors:  Kathryn E Keenan; Jana G Delfino; Kalina V Jordanova; Megan E Poorman; Prathyush Chirra; Akshay S Chaudhari; Bettina Baessler; Jessica Winfield; Satish E Viswanath; Nandita M deSouza
Journal:  Med Phys       Date:  2021-09-29       Impact factor: 4.506

4.  Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy.

Authors:  Daniela Marfisi; Carlo Tessa; Chiara Marzi; Jacopo Del Meglio; Stefania Linsalata; Rita Borgheresi; Alessio Lilli; Riccardo Lazzarini; Luca Salvatori; Claudio Vignali; Andrea Barucci; Mario Mascalchi; Giancarlo Casolo; Stefano Diciotti; Antonio Claudio Traino; Marco Giannelli
Journal:  Sci Rep       Date:  2022-06-17       Impact factor: 4.996

5.  Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.

Authors:  Hamada R H Al-Absi; Mohammad Tariqul Islam; Mahmoud Ahmed Refaee; Muhammad E H Chowdhury; Tanvir Alam
Journal:  Sensors (Basel)       Date:  2022-06-07       Impact factor: 3.847

Review 6.  Artificial Intelligence in Cardiology-A Narrative Review of Current Status.

Authors:  George Koulaouzidis; Tomasz Jadczyk; Dimitris K Iakovidis; Anastasios Koulaouzidis; Marc Bisnaire; Dafni Charisopoulou
Journal:  J Clin Med       Date:  2022-07-05       Impact factor: 4.964

7.  Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.

Authors:  Jihye Jang; Hossam El-Rewaidy; Long H Ngo; Jennifer Mancio; Ibolya Csecs; Jennifer Rodriguez; Patrick Pierce; Beth Goddu; Ulf Neisius; Warren Manning; Reza Nezafat
Journal:  J Magn Reson Imaging       Date:  2021-03-01       Impact factor: 5.119

8.  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

9.  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

10.  Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar.

Authors:  Ulf Neisius; Hossam El-Rewaidy; Selcuk Kucukseymen; Connie W Tsao; Jennifer Mancio; Shiro Nakamori; Warren J Manning; Reza Nezafat
Journal:  J Magn Reson Imaging       Date:  2020-01-23       Impact factor: 5.119

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