Literature DB >> 28836886

Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images.

Bettina Baessler1, Manoj Mannil1, Sabrina Oebel1, David Maintz1, Hatem Alkadhi1, Robert Manka1.   

Abstract

Purpose To test whether texture analysis (TA) allows for the diagnosis of subacute and chronic myocardial infarction (MI) on noncontrast material-enhanced cine cardiac magnetic resonance (MR) images. Materials and Methods In this retrospective, institutional review board-approved study, 120 patients who underwent cardiac MR imaging and showed large transmural (volume of enhancement on late gadolinium enhancement [LGE] images >20%, n = 72) or small (enhanced volume ≤20%, n = 48) subacute or chronic ischemic scars were included. Sixty patients with normal cardiac MR imaging findings served as control subjects. Regions of interest for TA encompassing the left ventricle were drawn by two blinded, independent readers on cine images in end systole by using a freely available software package. Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning, and correlation analyses were performed for selecting features, enabling the diagnosis of MI on nonenhanced cine MR images by using LGE imaging as the standard of reference. Results Five independent texture features allowed for differentiation between ischemic scar and normal myocardium on cine MR images in both subgroups: Teta1, Perc.01, Variance, WavEnHH.s-3, and S(5,5)SumEntrp (in patients with large MI: all P values < .001; in patients with small MI: Teta1 and Perc.01, P < .001; Variance, P = .026; WavEnHH.s-3, P = .007; S[5,5]SumEntrp, P = .045). Multiple logistic regression models revealed that combining the features Teta1 and Perc.01 resulted in the highest accuracy for diagnosing large and small MI on cine MR images, with an area under the curve of 0.93 and 0.92, respectively. Conclusion This proof-of-concept study indicates that TA of nonenhanced cine MR images allows for the diagnosis of subacute and chronic MI with high accuracy. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28836886     DOI: 10.1148/radiol.2017170213

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


  52 in total

1.  Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening.

Authors:  Gelareh Valizadeh; Farshid Babapour Mofrad; Ahmad Shalbaf
Journal:  Med Biol Eng Comput       Date:  2021-05-13       Impact factor: 2.602

2.  Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS).

Authors:  Manoj Mannil; Jakob M Burgstaller; Ulrike Held; Mazda Farshad; Roman Guggenberger
Journal:  Eur Radiol       Date:  2018-06-14       Impact factor: 5.315

3.  Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features.

Authors:  Fei Dong; Qian Li; Duo Xu; Wenji Xiu; Qiang Zeng; Xiuliang Zhu; Fangfang Xu; Biao Jiang; Minming Zhang
Journal:  Eur Radiol       Date:  2018-11-12       Impact factor: 5.315

4.  Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.

Authors:  Zhi-Cheng Li; Guangtao Zhai; Jinheng Zhang; Zhongqiu Wang; Guiqin Liu; Guang-Yu Wu; Dong Liang; Hairong Zheng
Journal:  Eur Radiol       Date:  2018-12-06       Impact factor: 5.315

5.  Use of 18F-FDG PET/CT texture analysis to diagnose cardiac sarcoidosis.

Authors:  Osamu Manabe; Hiroshi Ohira; Kenji Hirata; Souichiro Hayashi; Masanao Naya; Ichizo Tsujino; Tadao Aikawa; Kazuhiro Koyanagawa; Noriko Oyama-Manabe; Yuuki Tomiyama; Keiichi Magota; Keiichiro Yoshinaga; Nagara Tamaki
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-10-16       Impact factor: 9.236

6.  Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.

Authors:  Zhi-Cheng Li; Hongmin Bai; Qiuchang Sun; Qihua Li; Lei Liu; Yan Zou; Yinsheng Chen; Chaofeng Liang; Hairong Zheng
Journal:  Eur Radiol       Date:  2018-03-21       Impact factor: 5.315

Review 7.  Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.

Authors:  Tara A Retson; Alexandra H Besser; Sean Sall; Daniel Golden; Albert Hsiao
Journal:  J Thorac Imaging       Date:  2019-05       Impact factor: 3.000

8.  An integrated multi-objective whale optimized support vector machine and local texture feature model for severity prediction in subjects with cardiovascular disorder.

Authors:  M Muthulakshmi; G Kavitha
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-03-09       Impact factor: 2.924

9.  Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.

Authors:  Bettina Baessler; Tim Nestler; Daniel Pinto Dos Santos; Pia Paffenholz; Vikram Zeuch; David Pfister; David Maintz; Axel Heidenreich
Journal:  Eur Radiol       Date:  2019-12-11       Impact factor: 5.315

10.  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
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