Observational studyMedicine2018
Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach.
Observational study in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Revisiting Secondary Dilative Cardiomyopathy.International journal of molecular sciences · 2025Pooled it
- Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.European radiology · 2022Pooled it
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- Cardiac Magnetic Resonance Radiomics for Diagnosis, Phenotyping and Risk Stratification of Cardiomyopathies.Healthcare (Basel, Switzerland) · 2026Review
- Cardiac left ventricular MRI texture analysis to derive texture characteristics of a healthy population: clinical implications.European radiology · 2026Article
- Texture analysis of cardiovascular MRI native T1 mapping in patients with Duchenne muscular dystrophy.Orphanet journal of rare diseases · 2025Article
- Next-Generation Cardiac Magnetic Resonance Imaging Techniques for Characterization of Myocardial Disease.Current treatment options in cardiovascular medicine · 2024Article
- Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases.Anatolian journal of cardiology · 2024Article
- Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms.Journal of digital imaging · 2022Article
- Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer.Frontiers in oncology · 2022Article
- Myocardial area at risk and salvage in reperfused acute MI measured by texture analysis of cardiac T2 mapping and its prediction value of functional recovery in the convalescent stage.The international journal of cardiovascular imaging · 2021Article
- Classification of Cardiomyopathies from MR Cine Images Using Convolutional Neural Network with Transfer Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Role of Artificial Intelligence in Video Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models.Scientific reports · 2021Article
- Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison-Cardiac MRI Radiomics in Pulmonary Hypertension.Journal of clinical medicine · 2021Article
- Myocardial Radiomics in Cardiac MRI.AJR. American journal of roentgenology · 2020Review
- Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping.PloS one · 2020Article
- Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank.Frontiers in cardiovascular medicine · 2020Article
- Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.Advances in bioinformatics · 2019Review
- Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis of dilated cardiomyopathy (DCM) remains a challenge in clinical radiology. This study aimed to investigate whether texture analysis (TA) parameters on magnetic resonance T1 mapping can be helpful for the diagnosis of DCM.A total of 50 DCM cases were retrospectively screened and 24 healthy controls were prospectively recruited between March 2015 and July 2017. T1 maps were acquired using the Modified Look-Locker Inversion Recovery (MOLLI) sequence at a 3.0 T MR scanner. The endocardium and epicardium were drawn on the short-axis slices of the T1 maps by an experienced radiologist. Twelve histogram parameters and 5 gray-level co-occurrence matrix (GLCM) features were extracted during the TA. Differences in texture features between DCM patients and healthy controls were evaluated by t test. Support vector machine (SVM) was used to calculate the diagnostic accuracy of those texture parameters.Most histogram features were higher in the DCM group when compared to healthy controls, and 9 of these had significant differences between the DCM group and healthy controls. In terms of GLCM features, energy, correlation, and homogeneity were higher in the DCM group, when compared with healthy controls. In addition, entropy and contrast were lower in the DCM group. Moreover, entropy, contrast, and homogeneity had significant differences between these 2 groups. The diagnostic accuracy when using the SVM classifier with all these histogram and GLCM features was 0.85 ± 0.07.A computer-based TA and machine learning approach of T1 mapping can provide an objective tool for the diagnosis of DCM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.