Literature DB >> 32785772

Myocardial extracellular volume fraction radiomics analysis for differentiation of reversible versus irreversible myocardial damage and prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction.

Bing-Hua Chen1, Dong-Aolei An1, Jie He2, Chong-Wen Wu1, Ting Yue1, Rui Wu1, Ruo-Yang Shi1, Khalid Eteer3, Bobby Joseph3, Jiani Hu3, Jian-Rong Xu4, Lian-Ming Wu5, Jun Pu6.   

Abstract

OBJECTIVES: Our study sought to explore the prognostic value of radiomic TA (texture analysis) on quantitative ECV (extracellular volume) fraction mapping to differentiate between reversible and irreversible myocardial damage and to predict left ventricular adverse remodeling in patients with reperfused STEMI (ST-elevation myocardial infarction).
METHODS: This observational prospective cohort study identified 70 patients (62 ± 9 years, 62 men [85.70%]) with STEMI for TA who consecutively performed native and contrast T1 mapping. Texture features were extracted from each stack of ECV mapping based on ROI (region of interest) analysis.
RESULTS: After texture feature selection and dimension reduction, five selected texture features were found to be statistically significant for differentiating the extent of myocardial injury. ROC (receiver operating characteristic) curve analysis for the differentiation of unsalvageable infarction and salvageable myocardium demonstrated a significantly higher AUC (area under the curve) (0.91 [95% CI, 0.86-0.96], p < 0.0001) for horizontal fraction than other texture features (p < 0.05). LVAR (left ventricular adverse remodeling) was predicted by those selected features. The differences in qualitative and quantitative baseline parameters and horizontal fractions were significant between the patients with and without LVAR. LGE (late gadolinium enhancement) and horizontal fraction features of infarcted myocardium in acute STEMI were the only two parameters selected in forming the optimal overall multivariable model for LVAR at 6 months.
CONCLUSIONS: Radiomic analysis of ECV could discriminate reversible from irreversible myocardial injury after STEMI. LGE as well as radiomics TA (texture analysis) of ECV may provide an alternative to predict LVAR and functional recovery. KEY POINTS: • ECV quantification was able to differentiate between infarcted myocardium and non-infarcted myocardium. • Radiomics analysis of ECV could discriminate reversible from irreversible myocardial injury. • Radiomics TA analysis shows a promising similarity with LGE findings which could aid the prognosis of myocardial infarction patients.

Entities:  

Keywords:  Extracellular matrix; Magnetic resonance imaging; Myocardial infarction; Ventricular remodeling

Mesh:

Substances:

Year:  2020        PMID: 32785772     DOI: 10.1007/s00330-020-07117-9

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  6 in total

Review 1.  T1-mapping in the heart: accuracy and precision.

Authors:  Peter Kellman; Michael S Hansen
Journal:  J Cardiovasc Magn Reson       Date:  2014-01-04       Impact factor: 5.364

2.  Accuracy and reproducibility of semi-automated late gadolinium enhancement quantification techniques in patients with hypertrophic cardiomyopathy.

Authors:  Yoko Mikami; Louis Kolman; Sebastien X Joncas; John Stirrat; David Scholl; Martin Rajchl; Carmen P Lydell; Sarah G Weeks; Andrew G Howarth; James A White
Journal:  J Cardiovasc Magn Reson       Date:  2014-10-07       Impact factor: 5.364

3.  Comparison of semi-automated methods to quantify infarct size and area at risk by cardiovascular magnetic resonance imaging at 1.5T and 3.0T field strengths.

Authors:  Jamal N Khan; Sheraz A Nazir; Mark A Horsfield; Anvesha Singh; Prathap Kanagala; John P Greenwood; Anthony H Gershlick; Gerry P McCann
Journal:  BMC Res Notes       Date:  2015-02-25

4.  Mechanisms for overestimating acute myocardial infarct size with gadolinium-enhanced cardiovascular magnetic resonance imaging in humans: a quantitative and kinetic study.

Authors:  Sophia Hammer-Hansen; W Patricia Bandettini; Li-Yueh Hsu; Steve W Leung; Sujata Shanbhag; Christine Mancini; Anders M Greve; Lars Køber; Jens Jakob Thune; Peter Kellman; Andrew E Arai
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2015-05-16       Impact factor: 6.875

5.  Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI).

Authors:  Daniel R Messroghli; James C Moon; Vanessa M Ferreira; Lars Grosse-Wortmann; Taigang He; Peter Kellman; Julia Mascherbauer; Reza Nezafat; Michael Salerno; Erik B Schelbert; Andrew J Taylor; Richard Thompson; Martin Ugander; Ruud B van Heeswijk; Matthias G Friedrich
Journal:  J Cardiovasc Magn Reson       Date:  2017-10-09       Impact factor: 5.364

6.  Defining left ventricular remodeling following acute ST-segment elevation myocardial infarction using cardiovascular magnetic resonance.

Authors:  Heerajnarain Bulluck; Yun Yun Go; Gabriele Crimi; Andrew J Ludman; Stefania Rosmini; Amna Abdel-Gadir; Anish N Bhuva; Thomas A Treibel; Marianna Fontana; Silvia Pica; Claudia Raineri; Alex Sirker; Anna S Herrey; Charlotte Manisty; Ashley Groves; James C Moon; Derek J Hausenloy
Journal:  J Cardiovasc Magn Reson       Date:  2017-03-13       Impact factor: 5.364

  6 in total
  3 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

3.  Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms.

Authors:  Fatemeh Arian; Mehdi Amini; Shayan Mostafaei; Kiara Rezaei Kalantari; Atlas Haddadi Avval; Zahra Shahbazi; Kianosh Kasani; Ahmad Bitarafan Rajabi; Saikat Chatterjee; Mehrdad Oveisi; Isaac Shiri; Habib Zaidi
Journal:  J Digit Imaging       Date:  2022-08-22       Impact factor: 4.903

  3 in total

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