ArticleDiagnostics (Basel, Switzerland)2023
Exploring the Utility of Cardiovascular Magnetic Resonance Radiomic Feature Extraction for Evaluation of Cardiac Sarcoidosis.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Cardiac Magnetic Resonance Radiomics for Diagnosis, Phenotyping and Risk Stratification of Cardiomyopathies.Healthcare (Basel, Switzerland) · 2026Review
- Late Gadolinium Enhancement Entropy as a Novel Imaging Biomarker of Myocardial Tissue Heterogeneity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- The Role of [18F]FDG PET-Based Radiomics and Machine Learning for the Evaluation of Cardiac Sarcoidosis: A Narrative Literature Review.Medicina (Kaunas, Lithuania) · 2025Review
- Current status and perspectives of nuclear cardiology.Annals of nuclear medicine · 2024Review
- An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis.Frontiers in nuclear medicine · 2024Article
- The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine.The British journal of radiology · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThe aim of this study is to explore the utility of cardiac magnetic resonance (CMR) imaging of radiomic features to distinguish active and inactive cardiac sarcoidosis (CS).
methodsSubjects were classified into active cardiac sarcoidosis (CS
resultsUnivariate analysis of individual features showed no significant differences. Of all features, gray level co-occurrence matrix (GLCM) joint entropy had a good area under the curve (AUC) and accuracy with the smallest confidence interval, suggesting it may be a good target for further investigation. Some ML classifiers achieved reasonable discrimination between CS
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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.