Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes.

Scientific reports - 8 Dec 2021

Antonopoulos Alexios S, Boutsikou Maria, Simantiris Spyridon, Angelopoulos Andreas, Lazaros George, Panagiotopoulos Ioannis, Oikonomou Evangelos, Kanoupaki Mikela, Tousoulis Dimitris, Mohiaddin Raad H, Tsioufis Konstantinos, Vlachopoulos Charalambos

Abstract excerpt

We explored whether radiomic features from T1 maps by cardiac magnetic resonance (CMR) could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. A total of 149 patients (n = 30 with no heart disease, n = 30 with LVH, n = 61 with hypertrophic cardiomyopathy (HCM) and n = 28 with cardiac amyloidosis) undergoing a CMR scan were included in this...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Aged
Share to Amyloidosis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cardiomyopathies

Share to Cardiomyopathy, Hypertrophic

Share to Machine Learning

Share to Magnetic Resonance Imaging, Cine

Share to Myocardium

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 34880319
DOI: 10.1038/s41598-021-02971-z
PMCID: PMC8654857

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes.PMID 34880319Read ↗

Unsupervised phenotype clustering of non-ischemic dilated cardiomyopathy with AI-assisted T1 mapping cardiac MRSemantic proximity Read ↗

Unsupervised phenotypic clustering of cardiac MRI data reveals distinct subgroups associated with outcomes in ischemic cardiomyopathy.Semantic proximity Read ↗

T1 Mapping in Discrimination of Hypertrophic Phenotypes: Hypertensive Heart Disease and Hypertrophic Cardiomyopathy: Findings From the International T1 Multicenter Cardiovascular Magnetic Resonance Study.Semantic proximity Read ↗

Genetics of myocardial interstitial fibrosis in the human heart and association with diseaseSemantic proximity Read ↗

Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat.Semantic proximity Read ↗

Native T1 and Extracellular Volume in Transthyretin Amyloidosis.Semantic proximity Read ↗

Assessing Cardiac Amyloidosis Subtypes by Unsupervised Phenotype Clustering Analysis.Semantic proximity Read ↗

Clinical and Genetic Associations of Deep Learning-Derived Cardiac Magnetic Resonance-Based Left Ventricular MassSemantic proximity Read ↗

Automated machine learning of echocardiographic strain enables identification of early myocardial changes in pre-symptomatic <i>TTR</i> carriersSemantic proximity Read ↗

Incremental Prognostic Value of Pericardial Adipose Tissue Radiomic Phenotype Based on Cardiac MRI in Patients With End-Stage Renal Disease.Semantic proximity Read ↗

Ultrasonic Texture Analysis for Acute Myocardial Infarction Risk Stratification: A Pilot StudySemantic proximity Read ↗

LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.