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• Clinical Articles •
The study of MRI radiomics and machine learning in the prediction of hemorrhagic transformation in acute stroke
MIAO Liqiong  PENG Mingyang  WANG Tongxing  CHEN Guozhong  YIN Xindao  WU Gang 

Cite this article as: Miao LQ, Peng MY, Wang TX, et al. The study of MRI radiomics and machine learning in the prediction of hemorrhagic transformation in acute stroke[J]. Chin J Magn Reson Imaging, 2022, 13(3): 18-21, 75. DOI:10.12015/issn.1674-8034.2022.03.004.


[Abstract] Objective To investigate MRI radiomic features before mechanical thrombectomy (MT) in acute stroke and machine learning and analyze their value in the prediction of hemorrhagic transformation (HT).Materials and Methods A total of 214 acute stroke patients receiving MRI and MT therapy in the neurology department of our hospital were retrospectively enrolled. The ITK-SNAP software was used to segment the high signal areas of diffusion weighted imaging (DWI) and the abnormal perfusion areas of perfusion weighted imaging (PWI). The AK software was used to extract the radiomic features and reduce the dimensionality. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to determine the radiomic features related to HT and support vector machine classifier was used to evaluate its value in HT prediction.Results Seven hundred and ninety-two radiomics features of each patient were extracted and 10 features highly related to HT were screened after dimension reduction. ROC analysis showed that the area under curve (AUC) of the prediction model based on the training set was 0.984, the sensitivity and specificity were 0.932 and 0.967 respectively; the AUC of the prediction model based on the test set was 0.921, the sensitivity and specificity were 0.826 and 0.852 respectively.Conclusions The analysis based on MRI radiomics and machine learning are the important tools for predicting HT, and have high efficiency in early accurate identification of HT.
[Keywords] stroke；hemorrhagic transformation；magnetic resonance imaging；radiomics；machine learning

MIAO Liqiong1   PENG Mingyang2   WANG Tongxing2   CHEN Guozhong2   YIN Xindao2   WU Gang2*  

1 Department of Radiology, Jiangyin Hospital of Traditional Chinese Medicine, Wuxi 214400, China

2 Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China

Wu G, E-mail: doc_wu@139.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82001811); Natural Science Foundation of Jiangsu Province (No. BK20201118).
Received  2021-07-31
Accepted  2022-03-02
DOI: 10.12015/issn.1674-8034.2022.03.004
Cite this article as: Miao LQ, Peng MY, Wang TX, et al. The study of MRI radiomics and machine learning in the prediction of hemorrhagic transformation in acute stroke[J]. Chin J Magn Reson Imaging, 2022, 13(3): 18-21, 75. DOI:10.12015/issn.1674-8034.2022.03.004.

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