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Myocardial Function...
Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms
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- Arian, Fatemeh (författare)
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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- Amini, Mehdi (författare)
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
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- Mostafaei, Shayan (författare)
- Karolinska Institutet
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- Rezaei Kalantari, Kiara (författare)
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Cardio-Oncology Research Center, Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
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- Haddadi Avval, Atlas (författare)
- School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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- Shahbazi, Zahra (författare)
- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
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- Kasani, Kianosh (författare)
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
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- Bitarafan Rajabi, Ahmad (författare)
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran; Cardiovascular interventional research center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
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- Chatterjee, Saikat (författare)
- KTH,Teknisk informationsvetenskap
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- Oveisi, Mehrdad (författare)
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK; Department of Computer Science, University of British Columbia, Vancouver BC, Canada
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- Shiri, Isaac (författare)
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
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- Zaidi, Habib (författare)
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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(creator_code:org_t)
- 2022-08-22
- 2022
- Engelska.
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Ingår i: Journal of digital imaging. - : Springer Nature. - 0897-1889 .- 1618-727X. ; 35:6, s. 1708-1718
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)–penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53–0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64–0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50–0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Cardiac MRI
- Coronary artery bypass grafting
- Machine learning
- Radiomics
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Till lärosätets databas
- Av författaren/redakt...
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Arian, Fatemeh
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Amini, Mehdi
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Mostafaei, Shaya ...
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Rezaei Kalantari ...
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Haddadi Avval, A ...
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Shahbazi, Zahra
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visa fler...
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Kasani, Kianosh
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Bitarafan Rajabi ...
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Chatterjee, Saik ...
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Oveisi, Mehrdad
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Shiri, Isaac
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Zaidi, Habib
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visa färre...
- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Radiologi och bi ...
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Kardiologi
- Artiklar i publikationen
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Journal of digit ...
- Av lärosätet
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Kungliga Tekniska Högskolan
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Karolinska Institutet