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Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling

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Abstract

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial pathology occurs. We aimed to characterize regional CS myocardial remodeling features correlating with future adverse cardiac events by coupling automated image processing and data analysis on cardiac magnetic resonance (CMR) imaging datasets. A deep convolutional neural network (DCNN) was used to process a CMR database of a 10-year cohort of 117 consecutive biopsy-proven sarcoidosis patients. The maximum relevance − minimum redundancy method was used to select the best subset of all the features—24 (from manual processing) and 232 (from automated processing) left ventricular (LV) structural/functional features. Three machine learning (ML) algorithms, logistic regression (LogR), support vector machine (SVM) and multi-layer neural networks (MLP), were used to build classifiers to categorize endpoints. Over a median follow-up of 41.8 (inter-quartile range 20.4–60.5) months, 35 sarcoidosis patients experienced a total of 43 cardiac events. After manual processing, LV ejection fraction (LVEF), late gadolinium enhancement, abnormal segmental wall motion, LV mass (LVM), LVMI index (LVMI), septal wall thickness, lateral wall thickness, relative wall thickness, and wall thickness of 9 (out of 17) individual LV segments were significantly different between patients with and without endpoints. After automated processing, LVEF, end-diastolic volume, end-systolic volume, LV mass and wall thickness of 92 (out of 216) individual LV segments were significantly different between patients with and without endpoints. To achieve the best predictive performance, ML algorithms selected lateral wall thickness, abnormal segmental wall motion, septal wall thickness, and increased wall thickness of 3 individual segments after manual image processing, and selected end-diastolic volume and 7 individual segments after automated image processing. LogR, SVM and MLP based on automated image processing consistently showed better predictive accuracies than those based on manual image processing. Automated image processing with a DCNN improves data resolution and regional CS myocardial remodeling pattern recognition, suggesting that a framework coupling automated image processing with data analysis can help clinical risk stratification.

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Author notes

  1. Chenying Lu and Yi Grace Wang have equally contributed to this work.

Authors and Affiliations

  1. Departments of Medicine and Radiology, State University of New York, Upstate Medical University Hospital, Syracuse, USA

    Chenying Lu, Ernest Scalzetti, David Feiglin & Kan Liu

  2. Zhejiang Provincial Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Zhejiang, China

    Chenying Lu, Jiansong Ji & Tiemin Wei

  3. Department of Mathematics, California State University Dominguez Hills, Carson, USA

    Yi Grace Wang

  4. Department of Electrical and Electronic Engineering, University of Iowa, Iowa City, USA

    Fahim Zaman & Xiaodong Wu

  5. Cardiovascular Division, University of Pennsylvania, Philadelphia, USA

    Yuchi Han

  6. Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, USA

    Mehul Adhaduk, Amanda Chang, Promporn Suksaranjit & Georgios Christodoulidis

  7. Division of Cardiology and Heart Vascular Center, University of Iowa, Iowa City, IA, 52242, USA

    Kan Liu

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  1. Chenying Lu
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Lu, C., Wang, Y.G., Zaman, F. et al. Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling. Int J Cardiovasc Imaging 38, 1825–1836 (2022). https://doi.org/10.1007/s10554-022-02564-5

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