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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.

[1]
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[J]. Stroke, 2019, 50(12): e344-e418. DOI: 10.1161/STR.0000000000000211.
[2]
Bracard S, Ducrocq X, Mas JL, et al. Mechanical thrombectomy after intravenous alteplase versus alteplase alone after stroke (thrace): A randomised controlled trial[J]. Lancet Neurol, 2016, 15(11): 1138-1147. DOI: 10.1016/S1474-4422(16)30177-6.
[3]
Desai SM, Tonetti DA, Morrison AA, et al. Relationship between reperfusion and intracranial hemorrhage after thrombectomy[J]. J Neurointerv Surg, 2020, 12(5): 448-453. DOI: 10.1136/neurintsurg-2019-015337.
[4]
Cappellari M, Turcato G, Forlivesi S, et al. STARTING-SICH Nomogram to Predict Symptomatic Intracerebral Hemorrhage After Intravenous Thrombolysis for Stroke[J]. Stroke, 2018, 49(2): 397-404. DOI: 10.1161/STROKEAHA.117.018427.
[5]
Erdur H, Polymeris A, Grittner U, et al. A Score for Risk of Thrombolysis-Associated Hemorrhage Including Pretreatment with Statins[J]. Front Neurol, 2018, 9: 74. DOI: 10.3389/fneur.2018.00074.
[6]
Liu M, Pan Y, Zhou L, et al. Predictors of post-thrombolysis symptomatic intracranial hemorrhage in Chinese patients with acute ischemic stroke[J]. PLoS One, 2017, 12(9): e0184646. DOI: 10.1371/journal.pone.0184646.
[7]
van Timmeren JE, Cester D, Tanadini-Lang S, et al. Radiomics in medical imaging-"how-to" guide and critical reflection[J]. Insights Imaging, 2020, 11(1): 91. DOI: 10.1186/s13244-020-00887-2.
[8]
Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6): 603-619. DOI: 10.1111/joim.12822.
[9]
Neuberger U, Kickingereder P, Schonenberger S, et al. Risk factors of intracranial hemorrhage after mechanical thrombectomy of anterior circulation ischemic stroke[J]. Neuroradiology, 2019, 61(4): 461-469. DOI: 10.1007/s00234-019-02180-6.
[10]
Jiang L, Zhou L, Zhang H, et al. MRI predictors of intracranial hemorrhage in acute ischemic stroke after endovascular thrombectomy therapy[J]. Am J Transl Res, 2020, 12(8): 4532-4541.
[11]
Wang R, Peng MY, Zhou XF, et al. Predictors of intracranial hemorrhage after mechanical thrombectomy in acute ischemic stroke[J]. Chin J Magn Reson Imaging, 2021, 12(1): 9-14. DOI: 10.12015/issn.1674-8034.2021.01.003.
[12]
Wang C, Wang L, Deng L, et al. Association Between Mean Platelet Volume and Hemorrhagic Transformation in Acute Ischemic Stroke Patients[J]. Curr Neurovasc Res, 2020, 17(1): 3-10. DOI: 10.2174/1567202617666191226115518.
[13]
Okazaki S, Yamagami H, Yoshimoto T, et al. Cerebral hyperperfusion on arterial spin labeling MRI after reperfusion therapy is related to hemorrhagic transformation[J]. J Cereb Blood Flow Metab, 2017, 37(9): 3087-3090. DOI: 10.1177/0271678X17718099.
[14]
Wang X, Song G, Pang PP, et al. A preliminary study of radiomics in predicting WHO/ISUP grading of clear cell renal cell carcinoma based on unenhanced CT texture analysis[J]. Chin J Radiol, 2021, 55(3): 276-281. DOI: 10.3760/cma.j.cn112149-20200324-00446.
[15]
Chen Q, Xia T, Zhang M, et al. Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges[J]. Aging Dis, 2021, 12(1): 143-154. DOI: 10.14336/AD.2020.0421.
[16]
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.
[17]
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.
[18]
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.
[19]
Zhang R, Zhu ZQ, Zhu L, et al. Apparent diffusion coefficient map‐based radiomics model for identifying the ischemic penumbra in acute ischemic stroke[J]. Chin J Radiol, 2021, 55(4): 383-389. DOI: 10.3760/cma.j.cn112149-20200506-00654.
[20]
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.
[21]
Neuberger U, Kickingereder P, Schonenberger S, et al. Risk factors of intracranial hemorrhage after mechanical thrombectomy of anterior circulation ischemic stroke[J]. Neuroradiology, 2019, 61(4): 461-469. DOI: 10.1007/s00234-019-02180-6.
[22]
Caparros F, Kuchcinski G, Drelon A, et al. Use of MRI to predict symptomatic haemorrhagic transformation after thrombolysis for cerebral ischaemia[J]. J Neurol Neurosurg Psychiatry, 2020, 91(4): 402-410. DOI: 10.1136/jnnp-2019-321904.
[23]
Reynolds E, Callaghan B, Banerjee M. SVM-CART for Disease Classification[J]. J Appl Stat, 2019, 46(16): 2987-3007. DOI: 10.1080/02664763.2019.1625876.
[24]
Yu Y, Guo D, Lou M, et al. Prediction of hemorrhagic transformation severity in acute stroke from source perfusion mri[J]. IEEE transactions on bio-medical engineering, 2018, 65(9): 2058-2065. DOI: 10.1109/TBME.2017.2783241.
[25]
Bouts MJ, Tiebosch IA, Rudrapatna US, et al. Prediction of hemorrhagic transformation after experimental ischemic stroke using MRI-based algorithms[J]. J Cereb Blood Flow Metab, 2017, 37(8): 3065-3076. DOI: 10.1177/0271678X16683692.

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