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Abstract #4848

Comparison of 17 machine-learning models derived from LGE-MRI for predicting reverse LV remodeling in patients with STEMI

Jianing Cui1, Tao Li1, Xiuzheng Yue2, Sicong Huang2, Yun Kang2, and Fei Yan1
1Radiology, the First Medical center, PLA General Hospital, Beijing, China, 2Philips Healthcare, Beijing, China

Synopsis

Keywords: Heart, Heart, reverse left ventricular remodelingRadiomics is an emerging quantitative imaging method that could extract mineable high-dimensional data from medical images. We investigate the suitable models and significant radiomics features of LGE images in participants with STEMI and assess their value in predicting r-LVR. We chose 17 classification models to analyze all the adiomics features of LGE images in participants with STEMI. Our study found that the model of extra tree classifier was manifested relatively high AUC value in predicting r-LVR. The wavelet-HHH_gldm_SmallDependenceLowGrayLevelEmphasis was relatively strong predictor of r-LVR.

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Keywords