Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patients. Cardiac magnetic resonance imaging (CMRI) T2* is the primary screening technique used to detect myocardial iron overload, but inherently bears some limitations. In this study...

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Publicado en:Journal of Digital Imaging Vol. 36; no. 6; pp. 2494 - 2507
Autores principales: Taleie, Haniyeh, Hajianfar, Ghasem, Sabouri, Maziar, Parsaee, Mozhgan, Houshmand, Golnaz, Bitarafan-Rajabi, Ahmad, Zaidi, Habib, Shiri, Isaac
Formato: diagnostic images research tables/charts Journal Article
Publicado: Springer Nature Dec2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Dec2023
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      pub: Springer Nature
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        atl: Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.
      aug:
        au:
          Taleie, Haniyeh
          Hajianfar, Ghasem
          Sabouri, Maziar
          Parsaee, Mozhgan
          Houshmand, Golnaz
          Bitarafan-Rajabi, Ahmad
          Zaidi, Habib
          Shiri, Isaac
        affil: https://ror.org/03w04rv71 Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran
      sug:
        subj:
          Thalassemia
          Ventricular Dysfunction, Left Diagnosis
          Electrocardiography Utilization
          Machine Learning Algorithms Utilization
          Radiomics
          Heart Failure Prevention and Control
          Magnetic Resonance Imaging
          Health Screening
          Iron Overload
          ROC Curve
          Sensitivity and Specificity
          Human
          Funding Source
      ab: Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patients. Cardiac magnetic resonance imaging (CMRI) T2* is the primary screening technique used to detect myocardial iron overload, but inherently bears some limitations. In this study, we aimed to differentiate beta-thalassemia major patients with myocardial iron overload from those without myocardial iron overload (detected by T2*CMRI) based on radiomic features extracted from echocardiography images and machine learning (ML) in patients with normal left ventricular ejection fraction (LVEF > 55%) in echocardiography. Out of 91 cases, 44 patients with thalassemia major with normal LVEF (> 55%) and T2* ≤ 20 ms and 47 people with LVEF > 55% and T2* > 20 ms as the control group were included in the study. Radiomic features were extracted for each end-systolic (ES) and end-diastolic (ED) image. Then, three feature selection (FS) methods and six different classifiers were used. The models were evaluated using various metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Maximum relevance-minimum redundancy-eXtreme gradient boosting (MRMR-XGB) (AUC = 0.73, ACC = 0.73, SPE = 0.73, SEN = 0.73), ANOVA-MLP (AUC = 0.69, ACC = 0.69, SPE = 0.56, SEN = 0.83), and recursive feature elimination-K-nearest neighbors (RFE-KNN) (AUC = 0.65, ACC = 0.65, SPE = 0.64, SEN = 0.65) were the best models in ED, ES, and ED&ES datasets. Using radiomic features extracted from echocardiographic images and ML, it is feasible to predict cardiac problems caused by iron overload.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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