Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.

[1]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[2]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[3]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[4]  Yuh-Jye Lee,et al.  SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[5]  Tong Zhang,et al.  A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning , 2001, COLT/EuroCOLT.

[6]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[7]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Circulation.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  K. Teraoka,et al.  Delayed contrast enhancement of MRI in hypertrophic cardiomyopathy. , 2004, Magnetic resonance imaging.

[10]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[11]  N. Merchant,et al.  MRI of hypertrophic cardiomyopathy: part 2, Differential diagnosis, risk stratification, and posttreatment MRI appearances. , 2007, AJR. American journal of roentgenology.

[12]  J. Christiansen,et al.  Myocardial scar detected by contrast-enhanced cardiac magnetic resonance imaging is associated with ventricular tachycardia in hypertrophic cardiomyopathy patients. , 2008, Heart, lung & circulation.

[13]  Mario J. Garcia,et al.  Association of myocardial fibrosis, electrocardiography and ventricular tachyarrhythmia in hypertrophic cardiomyopathy: a delayed contrast enhanced MRI study , 2008, The International Journal of Cardiovascular Imaging.

[14]  Eibe Frank,et al.  Large-scale attribute selection using wrappers , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[15]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[16]  Bernard Fertil,et al.  Texture indexes and gray level size zone matrix. Application to cell nuclei classification , 2009 .

[17]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[18]  Kevin C. Dorff,et al.  The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models , 2010, Nature Biotechnology.

[19]  Liming Zhang,et al.  Cognitive Load Theory Based Effectiveness Evaluation on Dynamic Math Teaching , 2010, ICHL.

[20]  Vivek Muthurangu,et al.  Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. , 2011, JACC. Cardiovascular imaging.

[21]  Francesca N. Delling,et al.  Intermediate-Signal-Intensity Late Gadolinium Enhancement Predicts Ventricular Tachyarrhythmias in Patients With Hypertrophic Cardiomyopathy , 2012, Circulation. Cardiovascular imaging.

[22]  A. Ramli,et al.  Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. , 2013, Clinical imaging.

[23]  Martin Rajchl,et al.  Accuracy and reproducibility of semi-automated late gadolinium enhancement quantification techniques in patients with hypertrophic cardiomyopathy , 2014, Journal of Cardiovascular Magnetic Resonance.

[24]  F. Rutten,et al.  2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). , 2014, European heart journal.

[25]  P. Lambin,et al.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[26]  H. Hricak,et al.  Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores , 2015, European Radiology.

[27]  D. Vargas,et al.  Hypertrophic Cardiomyopathy from A to Z: Genetics, Pathophysiology, Imaging, and Management. , 2016, Radiographics : a review publication of the Radiological Society of North America, Inc.

[28]  W. Dreyer,et al.  Association of Late Gadolinium Enhancement and Degree of Left Ventricular Hypertrophy Assessed on Cardiac Magnetic Resonance Imaging With Ventricular Tachycardia in Children With Hypertrophic Cardiomyopathy. , 2016, The American journal of cardiology.

[29]  Y. Amano,et al.  Relationship of Nonseptal Late Gadolinium Enhancement to Ventricular Tachyarrhythmia in Hypertrophic Cardiomyopathy , 2017, Journal of computer assisted tomography.

[30]  Andrés Larroza,et al.  Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. , 2017, European journal of radiology.

[31]  Artur Klepaczko,et al.  QMaZda - Software tools for image analysis and pattern recognition , 2017, SPA.

[32]  Geoffrey G. Zhang,et al.  Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.

[33]  Artur Klepaczko,et al.  MaZda – A Framework for Biomedical Image Texture Analysis and Data Exploration , 2017 .

[34]  N. Smedira,et al.  Late Gadolinium Enhancement in Patients With Hypertrophic Cardiomyopathy and Preserved Systolic Function. , 2018, Journal of the American College of Cardiology.

[35]  Yasuyuki Suzuki,et al.  Relationship between Extension or Texture Features of Late Gadolinium Enhancement and Ventricular Tachyarrhythmias in Hypertrophic Cardiomyopathy , 2018, BioMed research international.

[36]  G. Carr-White,et al.  Quantitative assessment of myocardial scar heterogeneity using cardiovascular magnetic resonance texture analysis to risk stratify patients post-myocardial infarction. , 2018, Clinical radiology.

[37]  F. Laurent,et al.  CT-texture analysis of subsolid nodules for differentiating invasive from in-situ and minimally invasive lung adenocarcinoma subtypes. , 2018, Diagnostic and interventional imaging.

[38]  Jie Tian,et al.  LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results , 2018, European Radiology.

[39]  O. Abe,et al.  Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. , 2018, Diagnostic and interventional imaging.

[40]  J. Choi Radiomics and Deep Learning in Clinical Imaging: What Should We Do? , 2018, Nuclear Medicine and Molecular Imaging.

[41]  S. Priya,et al.  Texture Analysis in Cerebral Gliomas: A Review of the Literature , 2019, American Journal of Neuroradiology.