Cardiac Magnetic Resonance Radiomics Reveal Differential Impact of Sex, Age, and Vascular Risk Factors on Cardiac Structure and Myocardial Tissue
暂无分享,去创建一个
P. Munroe | S. Petersen | S. Neubauer | N. Aung | K. Lekadir | Z. Raisi-Estabragh | A. Jaggi | N. Harvey | C. McCracken | P. Gkontra | C. Mccracken
[1] P. Munroe,et al. Associations of Meat and Fish Consumption With Conventional and Radiomics Cardiovascular Magnetic Resonance Phenotypes in the UK Biobank , 2021, Frontiers in Cardiovascular Medicine.
[2] P. Munroe,et al. Women With Diabetes Are at Increased Relative Risk of Heart Failure Compared to Men: Insights From UK Biobank , 2021, Frontiers in Cardiovascular Medicine.
[3] D. Bluemke,et al. Reference ranges (“normal values”) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update , 2020, Journal of Cardiovascular Magnetic Resonance.
[4] S. Petersen,et al. Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource , 2020, European heart journal cardiovascular Imaging.
[5] S. Napel,et al. Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank , 2020, Frontiers in Cardiovascular Medicine.
[6] Michael McNitt-Gray,et al. Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features , 2020, Tomography.
[7] P. Munroe,et al. Variation in left ventricular cardiac magnetic resonance normal reference ranges: systematic review and meta-analysis , 2020, European heart journal cardiovascular Imaging.
[8] Víctor M. Campello,et al. Cardiac magnetic resonance radiomics: basic principles and clinical perspectives , 2020, European heart journal cardiovascular Imaging.
[9] Leonard Wee,et al. Machine learning helps identifying volume-confounding effects in radiomics. , 2020, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[10] S. Bengs,et al. Sex and gender in cardiovascular medicine: presentation and outcomes of acute coronary syndrome. , 2019, European heart journal.
[11] S. Petersen,et al. Cardiovascular research highlights from the UK Biobank: opportunities and challenges. , 2019, Cardiovascular research.
[12] V. Regitz-Zagrosek,et al. Sex differences in cardiometabolic disorders , 2019, Nature Medicine.
[13] W. Manning,et al. Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy. , 2019, JACC. Cardiovascular imaging.
[14] Yafeng Wang,et al. Sex differences in the association between diabetes and risk of cardiovascular disease, cancer, and all-cause and cause-specific mortality: a systematic review and meta-analysis of 5,162,654 participants , 2019, BMC Medicine.
[15] Alejandro F. Frangi,et al. Quantitative CMR population imaging on 20, 000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation , 2019, Medical Image Anal..
[16] C. Kramer,et al. Recent Advances in Imaging of Hypertensive Heart Disease , 2019, Current Hypertension Reports.
[17] V. Ferrari. The EACVI Textbook of Cardiovascular Magnetic Resonance , 2018 .
[18] D. Maintz,et al. Cardiac MRI Texture Analysis of T1 and T2 Maps in Patients with Infarctlike Acute Myocarditis. , 2018, Radiology.
[19] T. Marwick,et al. Sex Differences in Cardiovascular Pathophysiology: Why Women Are Overrepresented in Heart Failure With Preserved Ejection Fraction , 2018, Circulation.
[20] Jie Tian,et al. LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results , 2018, European Radiology.
[21] S. Petersen,et al. The impact of menopausal hormone therapy (MHT) on cardiac structure and function: Insights from the UK Biobank imaging enhancement study , 2018, PloS one.
[22] H. Alkadhi,et al. Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results. , 2018, European journal of radiology.
[23] M. Delgado-Rodríguez,et al. Systematic review and meta-analysis. , 2017, Medicina intensiva.
[24] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[25] Ben Glocker,et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks , 2017, Journal of Cardiovascular Magnetic Resonance.
[26] Jackie A Cooper,et al. The impact of cardiovascular risk factors on cardiac structure and function: Insights from the UK Biobank imaging enhancement study , 2017, PloS one.
[27] S. Lai,et al. Healthy aging of the left ventricle in relationship to cardiovascular risk factors: The Multi-Ethnic Study of Atherosclerosis (MESA) , 2017, PloS one.
[28] G. Kolovou,et al. The emerging role of Cardiovascular Magnetic Resonance in the evaluation of hypertensive heart disease , 2017, BMC Cardiovascular Disorders.
[29] Geoffrey G. Zhang,et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.
[30] Stefan K. Piechnik,et al. Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort , 2017, Journal of Cardiovascular Magnetic Resonance.
[31] M. Rayner,et al. Cardiovascular disease in Europe: epidemiological update 2016. , 2016, European heart journal.
[32] J. Paton,et al. Comprehensive characterisation of hypertensive heart disease left ventricular phenotypes , 2016, Heart.
[33] P. Matthews,et al. UK Biobank’s cardiovascular magnetic resonance protocol , 2015, Journal of Cardiovascular Magnetic Resonance.
[34] M. Budoff,et al. Prevalence and Correlates of Myocardial Scar in a US Cohort. , 2015, JAMA.
[35] Martin Maechler,et al. Cluster Analysis Extended Rousseeuw et al , 2014 .
[36] Daniel Levy,et al. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective , 2014, The Lancet.
[37] Peter Scarborough,et al. Cardiovascular disease in Europe: epidemiological update. , 2014, European heart journal.
[38] D. Levy,et al. Discriminating clinical features of heart failure with preserved vs. reduced ejection fraction in the community. , 2012, European heart journal.
[39] Jeroen J. Bax,et al. Association Between Diffuse Myocardial Fibrosis by Cardiac Magnetic Resonance Contrast-Enhanced T1 Mapping and Subclinical Myocardial Dysfunction in Diabetic Patients: A Pilot Study , 2012, Circulation. Cardiovascular imaging.
[40] Fionn Murtagh,et al. Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? , 2011, Journal of Classification.
[41] B. Borlaug,et al. Why are women more likely than men to develop heart failure with preserved ejection fraction? , 2011, Current opinion in cardiology.
[42] Gary King,et al. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference , 2011 .
[43] J. Mesirov,et al. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.
[44] L Landini,et al. Increased echodensity of myocardial wall in the diabetic heart: an ultrasound tissue characterization study. , 1995, Journal of the American College of Cardiology.
[45] J. G. Miller,et al. Abnormal myocardial acoustic properties in diabetic patients and their correlation with the severity of disease. , 1992, Journal of the American College of Cardiology.
[46] Gemma Vilahur,et al. Perspectives: The burden of cardiovascular risk factors and coronary heart disease in Europe and worldwide , 2014 .
[47] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[48] M. Szklo,et al. The Multi-Ethnic Study of Atherosclerosis (MESA) , 2012 .
[49] A. Alavi,et al. Opportunities and Challenges , 1998, In Vitro Diagnostic Industry in China.
[50] G. Torpier,et al. Preliminary results , 2007 .
[51] Jill P. Mesirov,et al. A resampling-based method for class discovery and visualization of gene expression microarray data , 2003 .
[52] Jeremy Fairbank,et al. Historical Perspective , 1987, Blood Traces.
[53] R. K. Miller. Technology and applications , 1984 .