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• Clinical Articles •
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: liuhao19820103@163.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.1674-8034.2022.03.005
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.

[1]
Ahmed HK, Logallo N, Thomassen L, et al. Clinical outcomes and safety profile of Tenecteplase in wake-up stroke[J]. Acta Neurol Scand, 2020, 142(5): 475-479. DOI: 10.1111/ane.13296.
[2]
Thomalla G, Boutitie F, Fiebach JB, et al. Stroke With Unknown Time of Symptom Onset: Baseline Clinical and Magnetic Resonance Imaging Data of the First Thousand Patients in WAKE-UP (Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke: A Randomized, Doubleblind, Placebo-Controlled Trial)[J]. Stroke, 2017, 48(3): 770-773. DOI: 10.1161/STROKEAHA.116.015233.
[3]
Thomalla G, Boutitie F, Ma H, et al. Intravenous alteplase for stroke with unknown time of onset guided by advanced imaging: systematic review and meta-analysis of individual patient data[J]. Lancet, 2020, 396(10262): 1574-1584. DOI: 10.1016/S0140-6736(20)32163-2.
[4]
Cheng B, Boutitie F, Nickel A, et al. Quantitative signal intensity in fluid-attenuated inversion recovery and treatment effect in the WAKE-UP Trial[J]. Stroke, 2020, 51(1): 209-215. DOI: 10.1161/STROKEAHA119.027390.
[5]
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[6]
Sirsat MS, Fermé E, Câmara J. Machine Learning for Brain Stroke: A Review[J]. J Stroke Cerebrovasc Dis, 2020, 29(10): 105162. DOI: 10.1016/j.jstrokecerebrovasdis.2020.105162.
[7]
Brugnara G, Neuberger U, Mahmutoglu MA, et al. Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning[J]. Stroke, 2020, 51(12): 3541-3551. DOI: 10.1161/STROKEAHA.120.030287.
[8]
Fahed R, Lecler A, Sabben C, et al. DWI-ASPECTS (Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Scores) and DWI-FLAIR (Diffusion-Weighted Imaging-Fluid Attenuated Inversion Recovery) Mismatch in Thrombectomy Candidates: An Intrarater and Interrater Agreement Study[J]. Stroke, 2018, 49(1): 223-227. DOI: 10.1161/STROKEAHA.117.019508.
[9]
Albers GW. Diffusion-weighted MRI for evaluation of acute stroke[J]. Neurology, 1998, 51(3Suppl 3): S47-S49. DOI: 10.1212/wnl.51.3_suppl_3.s47.
[10]
Fiehler J, Kucinski T, Zeumer H. Stroke MRI: pathophysiology, potential and perspectives[J]. Rofo, 2004, 176(3): 313-323. DOI: 10.1055/s-2004-812747.
[11]
Powers WJ, Rabinstein AA, Ackerson T, et al. Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke, 2019, 50(12): e344-e418. DOI: 10.1161/STR.0000000000000211.
[12]
Thomalla G, Cheng B, Ebinger M, et al. DWI-FLAIR mismatch for the identification of patients with acute ischaemic stroke within 4·5 h of symptom onset (PRE-FLAIR): a multicentre observational study[J]. Lancet Neurol, 2011, 10(11): 978-986. DOI: 10.1016/S1474-4422(11)70192-2.
[13]
Jakubicek S, Krebs S, Posekany A, et al. Modified DWI-FLAIR mismatch guided thrombolysis in unknown onset stroke[J]. J Thromb Thrombolysis, 2019, 47(2): 167-173. DOI: 10.1007/s11239-018-1766-3.
[14]
Guo Q, Li L, Wu H, et al. The treatment effect of modified DWI-FLAIR mismatch guided thrombosis in wake-up ischemic stroke[J]. Chin J Magn Reson Imaging, 2020, 11(10): 853-857. DOI: 10.12015/issn.1674-8034.2020.10.004.
[15]
Geng W, Jiang L, Chen HY, et al. To Explore the Application Value of DW I/FLAIR Mismatch in Predicting the Onset Time of Acute Ischemic Stroke[J]. J Clin Radiol, 2019, 38(5): 773-777.
[16]
Emeriau S, Serre I, Toubas O, et al. Can diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch (positive diffusion-weighted imaging/negative fluid-attenuated inversion recovery) at 3 Tesla identify patients with stroke at <4.5 hours?[J]. Stroke, 2013, 44(6): 1647-1651. DOI: 10.1161/STROKEAHA.113.001001.
[17]
Duchaussoy T, Budzik JF, Norberciak L, et al. Synthetic T2 mapping is correlated with time from stroke onset: a future tool in wake-up stroke management?[J]. Eur Radiol, 2019, 29(12): 7019-7026. DOI: 10.1007/s00330-019-06270-0.
[18]
Broocks G, Kemmling A, Teßarek S, et al. Quantitative Lesion Water Uptake as Stroke Imaging Biomarker: A Tool for Treatment Selection in the Extended Time Window?[J]. Stroke, 2022, 53(1): 201-209. DOI: 10.1161/STROKEAHA.120.033025.
[19]
Cheng X, Wu H, Shi J, et al. ASPECTS-based net water uptake as an imaging biomarker for lesion age in acute ischemic stroke[J]. J Neurol, 2021, 268(12): 4744-4751. DOI: 10.1007/s00415-021-10584-9.
[20]
Liu Na, Sui Qinglan, Liu Xuejun, et al. The value of contrast enhanced MRI radiomics in predicting the IDH 1 genotype in high-grade gliomas[J]. Chin J Radiol, 2020, 54(5): 445-449. DOI: 10.3760/cma.j.cn112149-20190608-00257.
[21]
Kuang H, Najm M, Chakraborty D, et al. Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning[J]. AJNR Am J Neuroradiol, 2019, 40(1): 33-38. DOI: 10.3174/ajnr.A5889.
[22]
Ortiz-Ramon R, Valdes Hernandez MDC, Gonzalez-Castro V, et al. Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images[J]. Comput Med Imaging Graph, 2019, 74: 12-24. DOI: 10.1016/j.compmedimag.2019.02.006.
[23]
Shu Z, Fang S, Ding Z, et al. MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases[J]. Sci Rep, 2019, 9(1): 3374. DOI: 10.1038/s41598-019-39651-y.
[24]
Lee H, Lee EJ, Ham S, et al. Machine Learning Approach to Identify Stroke Within 4.5 Hours[J]. Stroke, 2020, 51(3): 860-866. DOI: 10.1161/STROKEAHA.119.027611.
[25]
Ho KC, Speier W, Zhang H, et al. A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging[J]. IEEE Trans Med Imaging, 2019, 38(7): 1666-1676. DOI: 10.1109/TMI.2019.2901445.

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