ArticleDiagnostics (Basel, Switzerland)2022
Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Analysis of Myocardial Textures in Relation to Nicotine Abuse Using Radiomics in Cardiac PCCT.Tomography (Ann Arbor, Mich.) · 2026Article
- Clinical application of cardiac computed tomography in cardiomyopathy.The international journal of cardiovascular imaging · 2026Review
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics.iScience · 2024Article
- Hypertensive Heart Disease: A Narrative Review Series-Part 1: Pathophysiology and Microstructural Changes.Journal of clinical medicine · 2023Review
- Radiomics in Cardiac Computed Tomography.Diagnostics (Basel, Switzerland) · 2023Review
- RUNX1 facilitates heart failure progression through regulating TGF-β-induced cardiac remodeling.PeerJ · 2023Article
- Radiomic analysis of enhanced CMR cine images predicts left ventricular remodeling after TAVR in patients with symptomatic severe aortic stenosis.Frontiers in cardiovascular medicine · 2022Article
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14 authors.
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Abstract
The aim of the study is to verify the feasibility of a radiomics based approach for the detection of LV remodeling in patients with arterial hypertension. Cardiac Computed Tomography (CCT) and clinical data of patients with and without history of arterial hypertension were collected. In one image per patient, on a 4-chamber view, left ventricle (LV) was segmented using a polygonal region of interest by two radiologists in consensus. A total of 377 radiomics features per region of interest were extracted. After dataset splitting (70:30 ratio), eleven classification models were tested for the discrimination of patients with and without arterial hypertension based on radiomics data. An Ensemble Machine Learning (EML) score was calculated from models with an accuracy >60%. Boruta algorithm was used to extract radiomic features discriminating between patients with and without history of hypertension. Pearson correlation coefficient was used to assess correlation between EML score and septum width in patients included in the test set. EML showed an accuracy, sensitivity and specificity of 0.7. Correlation between EML score and LV septum width was 0.53 (
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