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Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms
Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.
Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
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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2022 (English)In: Journal of digital imaging, ISSN 0897-1889, E-ISSN 1618-727X, Vol. 35, no 6, p. 1708-1718Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature , 2022. Vol. 35, no 6, p. 1708-1718
Keywords [en]
Cardiac MRI, Coronary artery bypass grafting, Machine learning, Radiomics
National Category
Radiology, Nuclear Medicine and Medical Imaging Cardiac and Cardiovascular Systems
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URN: urn:nbn:se:kth:diva-329056DOI: 10.1007/s10278-022-00681-0ISI: 000842878500004PubMedID: 35995896Scopus ID: 2-s2.0-85136592092OAI: oai:DiVA.org:kth-329056DiVA, id: diva2:1768059
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QC 20230614

Available from: 2023-06-14 Created: 2023-06-14 Last updated: 2023-06-14Bibliographically approved

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Chatterjee, Saikat

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