Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach

Abstract Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (...

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Autores principales:	Manoj Mannil, Ken Kato, Robert Manka, Jochen von Spiczak, Benjamin Peters, Victoria L. Cammann, Christoph Kaiser, Stefan Osswald, Thanh Ha Nguyen, John D. Horowitz, Hugo A. Katus, Frank Ruschitzka, Jelena R. Ghadri, Hatem Alkadhi, Christian Templin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8b9558fcbb414f078925eee1ae9987cc
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spelling	oai:doaj.org-article:8b9558fcbb414f078925eee1ae9987cc2021-12-02T11:40:20ZPrognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach10.1038/s41598-020-76432-42045-2322https://doaj.org/article/8b9558fcbb414f078925eee1ae9987cc2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-76432-4https://doaj.org/toc/2045-2322Abstract Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. In this multicenter study (InterTAK Registry), we investigated CMR imaging data of 58 patients (56 women, mean age 68 ± 12 years) with TTS. CMR imaging was performed in the acute to subacute phase (median time after symptom onset 4 days) of TTS. TA of the left ventricle was performed using free-hand regions-of-interest in short axis late gadolinium-enhanced and on T2-weighted (T2w) images. A total of 608 TA features adding the parameters age, gender, and body mass index were included. Dimension reduction was performed removing TA features with poor intra-class correlation coefficients (ICC ≤ 0.6) and those being redundant (correlation matrix with Pearson correlation coefficient r > 0.8). Five common machine-learning classifiers (artificial neural network Multilayer Perceptron, decision tree J48, NaïveBayes, RandomForest, and Sequential Minimal Optimization) with tenfold cross-validation were applied to assess 5-year outcome including major adverse cardiac and cerebrovascular events (MACCE). Dimension reduction yielded 10 TA features carrying prognostic information, which were all based on T2w images. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80–86.2), specificity of 83.7% (CI 75.7–92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83–0.92). This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. These features might serve as imaging prognostic biomarkers in TTS patients.Manoj MannilKen KatoRobert MankaJochen von SpiczakBenjamin PetersVictoria L. CammannChristoph KaiserStefan OsswaldThanh Ha NguyenJohn D. HorowitzHugo A. KatusFrank RuschitzkaJelena R. GhadriHatem AlkadhiChristian TemplinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Manoj Mannil Ken Kato Robert Manka Jochen von Spiczak Benjamin Peters Victoria L. Cammann Christoph Kaiser Stefan Osswald Thanh Ha Nguyen John D. Horowitz Hugo A. Katus Frank Ruschitzka Jelena R. Ghadri Hatem Alkadhi Christian Templin Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach
description	Abstract Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. In this multicenter study (InterTAK Registry), we investigated CMR imaging data of 58 patients (56 women, mean age 68 ± 12 years) with TTS. CMR imaging was performed in the acute to subacute phase (median time after symptom onset 4 days) of TTS. TA of the left ventricle was performed using free-hand regions-of-interest in short axis late gadolinium-enhanced and on T2-weighted (T2w) images. A total of 608 TA features adding the parameters age, gender, and body mass index were included. Dimension reduction was performed removing TA features with poor intra-class correlation coefficients (ICC ≤ 0.6) and those being redundant (correlation matrix with Pearson correlation coefficient r > 0.8). Five common machine-learning classifiers (artificial neural network Multilayer Perceptron, decision tree J48, NaïveBayes, RandomForest, and Sequential Minimal Optimization) with tenfold cross-validation were applied to assess 5-year outcome including major adverse cardiac and cerebrovascular events (MACCE). Dimension reduction yielded 10 TA features carrying prognostic information, which were all based on T2w images. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80–86.2), specificity of 83.7% (CI 75.7–92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83–0.92). This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. These features might serve as imaging prognostic biomarkers in TTS patients.
format	article
author	Manoj Mannil Ken Kato Robert Manka Jochen von Spiczak Benjamin Peters Victoria L. Cammann Christoph Kaiser Stefan Osswald Thanh Ha Nguyen John D. Horowitz Hugo A. Katus Frank Ruschitzka Jelena R. Ghadri Hatem Alkadhi Christian Templin
author_facet	Manoj Mannil Ken Kato Robert Manka Jochen von Spiczak Benjamin Peters Victoria L. Cammann Christoph Kaiser Stefan Osswald Thanh Ha Nguyen John D. Horowitz Hugo A. Katus Frank Ruschitzka Jelena R. Ghadri Hatem Alkadhi Christian Templin
author_sort	Manoj Mannil
title	Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach
title_short	Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach
title_full	Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach
title_fullStr	Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach
title_full_unstemmed	Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach
title_sort	prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with takotsubo syndrome: a machine learning based proof-of-principle approach
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/8b9558fcbb414f078925eee1ae9987cc
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Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach

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