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The study of machine learning based on DWI and FLAIR in the prediction of onset time of acute stroke
GUO Jingli  PENG Mingyang  WANG Tongxing  CHEN Guozhong  YIN Xindao  LIU Hao 

Cite this article as: Guo JL, Peng MY, Wang TX, et al. The study of machine learning based on DWI and FLAIR in the prediction of onset time of acute stroke[J]. Chin J Magn Reson Imaging, 2022, 13(3): 22-25, 42. DOI:10.12015/issn.1674-8034.2022.03.005.

[Abstract] Objective To construct a prediction model of onset time in acute stroke using machine learning based on the radiomic features of diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR).Materials and Methods A total of 188 acute stroke patients receiving MRI were retrospectively enrolled. The ITK-SNAP software was used to segment the high signal areas of DWI and the acute infarct areas of FLAIR. The artificial intelligent kit (A.K.) 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 onset time. The support vector machine classifier was used to evaluate its value in onset time prediction, and compared with those of human readings.Results A total of 10 radiomic features (7 DWI features and 3 FLAIR features) closely related to stroke onset time were screened. The receiver operating characteristic (ROC) analysis of human readings showed that the area under curve (AUC) of DWI-FLAIR mismatch in predicting onset time of acute stroke was 0.634, and the sensitivity and specificity were 0.667, 0.622, respectively. ROC analysis showed that AUC of the prediction model based on the training set was 0.975, the sensitivity and specificity were 0.932 and 0.950 respectively; the AUC of the prediction model based on the test set was 0.915, the sensitivity and specificity were 0.868 and 0.852 respectively.Conclusions Machine learning based on DWI and FLAIR radiomics can accurately predict the onset time of acute stroke patients and provide image guidance for the selection of thrombolytic therapy in clinical.
[Keywords] stroke;diffusion weighted imaging;fluid attenuated inversion recovery;machine learning;radiomic;onset time

GUO Jingli   PENG Mingyang   WANG Tongxing   CHEN Guozhong   YIN Xindao   LIU Hao*  

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

Liu H, E-mail:

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.1674-8034.2022.03.005
Cite this article as: Guo JL, Peng MY, Wang TX, et al. The study of machine learning based on DWI and FLAIR in the prediction of onset time of acute stroke[J]. Chin J Magn Reson Imaging, 2022, 13(3): 22-25, 42. DOI:10.12015/issn.1674-8034.2022.03.005.

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