ArticleJapanese journal of radiology2021
Texture analysis of delayed contrast-enhanced computed tomography to diagnose cardiac sarcoidosis.
Article in Japanese journal of radiology, 2021. 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, 12 citations in OpenAlex.
- Improved image quality and greater diagnostic suitability in myocardial delayed enhancement CT with deep learning image reconstruction.Japanese journal of radiology · 2026Article
- Updates on Cardiac MRI and PET Imaging for the Diagnosis and Monitoring of Cardiac Sarcoidosis.Korean journal of radiology · 2025Review
- Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.Frontiers in computational neuroscience · 2024Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- Texture analysis of SPECT myocardial perfusion provides prognostic value for dilated cardiomyopathy.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Advances in Diagnostic Imaging for Cardiac Sarcoidosis.Journal of clinical medicine · 2021Review
- The Role of Multimodality Imaging in Cardiac Sarcoidosis.Korean circulation journal · 2021Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo investigate the diagnostic value of texture analysis to differentiate cardiac sarcoidosis (CS) from other non-ischemic cardiomyopathies (non-CS). MATERIALS AND
methodsTwenty CS patients and 15 non-CS patients who had undergone myocardial CT delayed enhancement (CTDE) were included. A total of 36 texture features were calculated according to the CT attenuation of CTDE. We investigated the diagnostic value to differentiate CS from non-CS. We also assessed the intra- and inter-rater reproducibility for each feature and inter-observer agreement for visual assessment.
resultsSeven extracted features had significantly higher run length non-uniformity (RLNU) values (5.4 × 10
conclusionTexture analysis of CTDE could differentiate CS from non-CS with high reproducibility.
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Registered trials
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