Share:
Share this content in WeChat
X
Review
Research progress of imaging evaluation of hemorrhagic transformation after thrombolysis in acute ischemic stroke
JI Xinggui  ZHANG Ruijie  LIU Zhenhe  ZHANG Huan  LIU Tong  SU Tong  XU Wanbo 

Cite this article as: JI X G, ZHANG R J, LIU Z H, et al. Research progress of imaging evaluation of hemorrhagic transformation after thrombolysis in acute ischemic stroke[J]. Chin J Magn Reson Imaging, 2025, 16(10): 130-136. DOI:10.12015/issn.1674-8034.2025.10.021.


[Abstract] The incidence of acute ischemic stroke is increasing day by day. Intravenous thrombolysis is one of the effective methods to treat AIS patients with great vascular occlusion within the time window, but hemorrhagic after thrombolysis is one of its main complications. It is necessary to accurately evaluate whether acute ischemic stroke patients can benefit from thrombolysis. At present, there are many clinical scoring tables, laboratory and imaging indicators to predict the hemorrhagic transformation after thrombolysis. The review points out the limitations of previous research and points out the direction of future research. In this paper, the research progress of hemorrhagic transformation classification, imaging characteristics and artificial intelligence is reviewed, aiming at providing reference for clinical diagnosis and treatment.
[Keywords] acute ischemic stroke;hemorrhagic transformation;computed tomography;magnetic resonance imaging;artificial intelligence;prediction

JI Xinggui1   ZHANG Ruijie2   LIU Zhenhe2   ZHANG Huan2   LIU Tong2   SU Tong2   XU Wanbo2*  

1 Shandong Second Medical Univeristy School of Medical Imaging, Weifang 261053, China

2 Department of Radiology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou 253000, China

Corresponding author: XU W B, E-mail: 18005342388@163.com

Conflicts of interest   None.

Received  2025-07-23
Accepted  2025-10-08
DOI: 10.12015/issn.1674-8034.2025.10.021
Cite this article as: JI X G, ZHANG R J, LIU Z H, et al. Research progress of imaging evaluation of hemorrhagic transformation after thrombolysis in acute ischemic stroke[J]. Chin J Magn Reson Imaging, 2025, 16(10): 130-136. DOI:10.12015/issn.1674-8034.2025.10.021.

[1]
The editorial team of China Stroke Prevention and Treatment Report 2021. Brief report on stroke prevention and treatment in China, 2021[J]. Chinese Journal of Cerebrovascular Diseases, 2023, 20(11): 783-793. DOI: 103969/jissn1672-5921202311009.
[2]
WANG Y, MAEDA T, YOU S J, et al. Patterns and Clinical Implications of Hemorrhagic Transformation After Thrombolysis in Acute Ischemic Stroke[J/OL]. Neurology, 2024, 103(11): e210020 [2025-07-23]. https://doi.org/10.1212/WNL.0000000000210020. DOI: 10.1212/wnl.0000000000210020.
[3]
VINCENT L, RÜDIGER V K, ACHIM M L, et al. Risk Factors for Severe Hemorrhagic Transformation in Ischemic Stroke Patients Treated With Recombinant Tissue Plasminogen Activator[J]. Stroke, 2001, 32(2): 438-441. DOI: 10.1161/01.Str.32.2.438.
[4]
Neurology Branch of the Chinese Medical Association, Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association. China Consensus on Diagnosis and Treatment of Hemorrhagic Transformation After Acute Cerebral Infarction 2019[J]. Chin J Neurol, 2019, 52(4): 252-265. DOI: 10.3760/cma.j.issn.1006-7876.2019.04.003.
[5]
HAO Y, ZHOU H, PAN C Z, et al. Prediction factors and clinical significance of different types of hemorrhagic transformation after intravenous thrombolysis[J/OL]. Eur J Med Res, 2023, 28(1): 509 [2025-07-23]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10638828/. DOI: 10.1186/s40001-023-01503-x.
[6]
YANG M M, ZHONG W, ZOU W H, et al. A novel nomogram to predict hemorrhagic transformation in ischemic stroke patients after intravenous thrombolysis[J/OL]. Front Neurol, 2022, 13: 913442 [2025-07-23]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9494598/. DOI: 10.3389/fneur.2022.913442.
[7]
SUN J C, LAM C, CHRISTIE L, et al. Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis[J/OL]. Front Neurol, 2023, 14: 1079205 [2024-06-05]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9986457/. DOI: 10.3389/fneur.2023.1079205.
[8]
MA S H, ZHANG L L, LU J M, et al. Predictive value of length of hyperdense middle cerebral artery sign on recanalization rate and curative effect of intravenous thrombolysis in acute cerebral infarction[J]. Chongqing Medical Journal, 2022, 51(9): 1478-1481, 1486. DOI: 10.3969/j.issn.1671-8348.2022.09.008.
[9]
LI H Y, YIN Y H, LÜ Y, et al. Establishment and validation of prediction model for HT after intravenous alteplase thrombolysis in elderly patients with ACI[J]. Chinese Journal of Geriatric Heart Brain and Vessel Diseases, 2023, 25(8): 810-813. DOI: 10.3969/j.issn.1009-0126.2023.08.007.
[10]
WEI C C, WU Q, LIU J F, et al. Key CT markers for predicting haemorrhagic transformation after ischaemic stroke: a prospective cohort study in China[J/OL]. BMJ Open, 2023, 13(11): e075106 [2025-07-19]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10680015/. DOI: 10.1136/bmjopen-2023-075106.
[11]
IANCU A, BULEU F, CHITA D S, et al. Early Hemorrhagic Transformation after Reperfusion Therapy in Patients with Acute Ischemic Stroke: Analysis of Risk Factors and Predictors[J/OL]. Brain Sci, 2023, 13(5): 840 [2025-07-19]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10216637/. DOI: 10.3390/brainsci13050840.
[12]
GU Y, XU C, ZHANG Z, et al. Association between infarct location and haemorrhagic transformation of acute ischaemic stroke after intravenous thrombolysis[J/OL]. Clin Radiol, 2024, 79(3): e401-e407 [2025-07-19]. https://linkinghub.elsevier.com/retrieve/pii/S0009-9260(23)00578-0. DOI: 10.1016/j.crad.2023.11.024.
[13]
ZHAN Z X, XU T, XU Y, et al. Associations between computed tomography markers of cerebral small vessel disease and hemorrhagic transformation after intravenous thrombolysis in acute ischemic stroke patients[J/OL]. Front Neurol, 2023, 14: 1144564 [2025-07-20]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10106596/. DOI: 10.3389/fneur.2023.1144564.
[14]
XIONG J, QU Z L, REN Y, et al. Value of CT perfusion imaging combined with serum EPA/AA in predicting hemorrhage transformation and short-term prognosis after thrombolysis in acute ischemic stroke with leukoaraiosis[J]. Journal of Regional Anatomy and Operative Surgery, 2025, 34(1): 32-37. DOI: 10.11659/jjssx.10E023009.
[15]
YU Y, ZHANG F L, QU Y M, et al. Intracranial Calcification is Predictive for Hemorrhagic Transformation and Prognosis After Intravenous Thrombolysis in Non-Cardioembolic Stroke Patients[J/OL]. J Atheroscler Thromb, 2021, 28(4): 356-364. DOI: 10.5551/jat.55889.
[16]
HAN S, HUANG R, YAO F, et al. Pre-treatment spectral CT combined with CT perfusion can predict hemorrhagic transformation after thrombolysis in patients with acute ischemic stroke[J/OL]. Eur J Radiol, 2022, 156: 110543 [2025-07-23]. https://doi.org/10.1016/j.ejrad.2022.110543. DOI: 10.1016/j.ejrad.2022.110543.
[17]
MUBARAK F, FATIMA H, MUSTAFA M S, et al. Assessment Precision of CT Perfusion Imaging in the Detection of Acute Ischemic Stroke: A Systematic Review and Meta-Analysis[J/OL]. Cureus, 2023, 15(8): e44396 [2025-07-20]. https://europepmc.org/article/MED/37791142. DOI: 10.7759/cureus.44396.
[18]
QIU F Z, CHEN C P, FAN Z J, et al. White Matter Hypoperfusion Associated with Leukoaraiosis Predicts Intracranial Hemorrhage after Intravenous Thrombolysis[J/OL]. J Stroke Cerebrovasc Dis, 2021, 30(2): 105528 [2025-07-21]. https://www.strokejournal.org/article/S1052-3057(20)30946-0/abstract. DOI: 10.1016/j.jstrokecerebrovasdis.2020.105528.
[19]
QIN S, DAI C W, WANG G Q, et al. The Predictive Value Study of CT Perfusion Imaging Combined with Serum D-D and MMP-9 on Hemorrhage Transformation after Thrombolysis in Patients with Acute Cerebral Infarction[J]. Chinese Journal of CT and MRI, 2023, 21(6): 24-27. DOI: 10.3969/j.issn.1672-5131.2023.06.00.
[20]
WU J, XU P. Construction of a nomogram model of hemorrhage transformation after AIS thrombolysis based on CT perfusion imaging parameters[J]. Journal of Medical Imaging, 2024, 34(11): 16-20. DOI: 1006-9011(2024)11-0016-05.
[21]
SUN F T, ZHANG H N, YU L, et al. Value of CT perfusion in predicting hemorrhagic transformation in acute ischemic cerebral infarction patients after thrombolysis[J]. Chinese Journal of Geriatric Heart Brain and Vessel Diseases, 2021, 23(1): 63-66. DOI: 10.3969/j.issn.1009-0126.2021.01.016.
[22]
ZHANG X X, YAO F R, ZHU J H, et al. Nomogram to predict haemorrhagic transformation after stroke thrombolysis: a combined brain imaging and clinical study[J/OL]. Clin Radiol, 2022, 77(1): e92-e98 [2025-07-26]. https://doi.org/10.1016/j.crad.2021.09.017. DOI: 10.1016/j.crad.2021.09.017.
[23]
WU X X, YANG J F, JI X Q, et al. Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study[J/OL]. Front Neurol, 2025, 16: 1545631 [2025-07-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11860078/. DOI: 10.3389/fneur.2025.1545631.
[24]
CHEN Y H, LEI Y M, SHENG F T. Predictive Value of Multi-Modal CT for Hemorrhagic Transformation in Acute Cerebral Infarction after Intravenous Thrombolysis[J]. Chinese Computed Medical Imaging, 2024, 30(5): 533-538. DOI: 10.19627/j.cnki.cn31-1700/th.2024.05.001.
[25]
LI C C, HAO X Z, LIN L Y, et al. Prognostic Value of a New Integrated Parameter—Both Collateral Circulation and Permeability Surface—in Hemorrhagic Transformation of Middle Cerebral Artery Occlusion Acute Ischemic Stroke: Retrospective Cohort Study[J/OL]. Front Aging Neurosci, 2021, 13: 703734 [2025-07-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8424095/. DOI: 10.3389/fnagi.2021.703734.
[26]
WANG M, YANG K, LI Y, et al. Construction of a prediction model for hemorrhagic transformation after thrombolysis in acute ischemic stroke based on total load of small cerebral vessels[J]. Journal of Qingdao University (Medical Sciences), 2025, 61(2): 224-228. DOI: 10.11712/jms.2096-5532.2025.61.067.
[27]
DU H W, WU S R, LEI H H, et al. Total Cerebral Small Vessel Disease Score and Cerebral Bleeding Risk in Patients With Acute Stroke Treated With Intravenous Thrombolysis[J/OL]. Front Aging Neurosci, 2022, 14: 790262 [2025-07-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9037754/. DOI: 10.3389/fnagi.2022.790262.
[28]
WANG Y, NI Y H, LI A H. Correlation between leukoaraiosis and hemorrhagic transformation and clinical prognosis after thrombolysis in acute cerebral infarction[J]. Journal of Brain and Nervous Diseases, 2023, 31(1): 52-57.
[29]
FREY B M, SHENAS F, BOUTITIE F, et al. Intravenous Thrombolysis in Patients With White Matter Hyperintensities in the WAKE-UP Trial[J]. Stroke, 2023, 54(7): 1718-1725. DOI: 10.1161/strokeaha.122.040247.
[30]
WU Z H, WU D Q, WANG H, et al. The Relationship between Periventricular and Deep White Matter Damage Signals in T2 Flair Magnetic Resonance Imaging and Short-term Prognosis of rt-PA in Acute Ischemic Stroke[J]. Chinese Journal of CT and MRI, 2024, 22(12): 18-21. DOI: 10.3969/j.issn.1672-5131.2024.12.007.
[31]
STÖSSER S, ULLRICH L, KASSUBEK J, et al. Recent silent infarcts do not increase the risk of haemorrhage after intravenous thrombolysis[J]. Eur J Neurol, 2020, 27(12): 2483-2490. DOI: 10.1111/ene.14453.
[32]
Neurology Branch of the Chinese Medical Association, Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association. China Acute Ischemic Stroke Diagnosis and Treatment Guidelines 2023[J]. Chinese Journal of Neurology, 2024, 57(6): 523-559. DOI: 10.3760/cma.j.cn113694-20240410-00221.
[33]
CAPUANA M L, LORENZANO S, CASELLI M C, et al. Hemorrhagic risk after intravenous thrombolysis for ischemic stroke in patients with cerebral microbleeds and white matter disease[J]. Neurol Sci, 2020, 42(5): 1969-1976. DOI: 10.1007/s10072-020-04720-y.
[34]
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.
[35]
LI H, ZHAO X J, YANG X L. Evaluation of hemorrhagic transformation in patients with cerebral infarction after thrombolytic therapy using SWI parameters combined with ADC values[J]. Journal of Medical Imaging, 2025, 35(1): 151-153. DOI: 10.20258/j.cnki.1006-9011.2025.01.036.
[36]
LI M, LV Y F, WANG M M, et al. Magnetic Resonance Perfusion-Weighted Imaging in Predicting Hemorrhagic Transformation of Acute Ischemic Stroke: A Retrospective Study[J/OL]. Diagnostics, 2023, 13(22): 3404 [2025-07-20] https://pmc.ncbi.nlm.nih.gov/articles/PMC10670343/. DOI: 10.3390/diagnostics13223404.
[37]
ZHANG G Y, LI Q D, ZHOU Z M, et al. Predictive value of ADC difference combined with infarct volume for the occurrence of hemorrhagic transformation with intravenous thrombolysis after acute cerebral infarction[J]. Journal of Jinan University (Natural Science & Medicine Edition), 2022, 43(6): 623-629. DOI: 10.11778/j.jdxb.20220250.
[38]
TANAKA K, MATSUMOTO S, FURUTA K, et al. Modified diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score including deep white matter lesions predicts symptomatic intracerebral hemorrhage following intravenous thrombolysis[J]. J Thromb Thrombolysis, 2019, 50(1): 174-180. DOI: 10.1007/s11239-019-01979-7.
[39]
GENG D Y. Application of artificial intelligence in imaging of central nervous system diseases[J]. International Journal of Medical Radiology, 2021, 44(6): 621-624. DOI: 10.19300/j.2021.S19473.
[40]
BAO W Q, PENG X, ZHANG C X, et al. Predictive value of non-contrast CT texture analysis for hemorrhage transformation after thrombolysis in hyperacute cerebral infarction[J]. Chinese Imaging Journal of Integrated Traditional and Western Medicine, 2022, 20(2): 122-127. DOI: 10.3969/j.issn.1672-0512.2022.02.005.
[41]
JI D D, WANG T L, ZHU L, et al. The value of a combined model of clinical factors and non-contrast CT radiomics in predicting symptomatic hemorrhagic transformation after intravenous thrombolysis in patients with anterior circulation ischemic stroke[J]. Chinese Journal of Radiology, 2024, 58(10): 1021-1027. DOI: 10.3760/cma.j.cn112149-20240115-00026.
[42]
XIE G, LI T, REN Y T, et al. Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke[J/OL]. Front Neurosci, 2022, 16: 1002717 [2025-07-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9533555/. DOI: 10.3389/fnins.2022.1002717.
[43]
REN H H, SONG H J, WANG J J, et al. A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study[J/OL]. Insights Imaging, 2023, 14: 52 [2025-07-26]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10050271/. DOI: 10.1186/s13244-023-01399-5.
[44]
CUI S, SONG H, REN H, et al. Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation[J/OL]. J Pers Med, 2022, 12(12): 2052 [2025-07-26]. https://pubmed.ncbi.nlm.nih.gov/36556272/. DOI: 10.3390/jpm12122052.
[45]
YAN C C, JI R S, XU P. Predictive Value of CT Texture Analysis Combined with Machine Learning in Hemorrhagic Transformation of Acute Cerebral Infarction[J]. Chinese Journal of CT and MRI, 2024, 22(8): 162-165. DOI: 10.3969/j.issn.1672-5131.2024.08.053.
[46]
DING J, CHEN J M, SHAO Y. Value of radiomics based on conventional MRI in predicting hemorrhagic transformation in acute cerebral infarction[J]. Radiologic Practice, 2024, 39(7): 859-865. DOI: 10.13609/j.cnki.1000-0313.2024.07.002.
[47]
MENG Y C, WANG H R, WU C F, et al. Prediction Model of Hemorrhage Transformation in Patient with Acute Ischemic Stroke Based on Multiparametric MRI Radiomics and Machine Learning[J/OL]. Brain Sci, 2022, 12(7): 858 [2025-07-22]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9313447/. DOI: 10.3390/brainsci12070858.
[48]
JIANG L, ZHOU L L, YONG W, et al. A deep learning‐based model for prediction of hemorrhagic transformation after stroke[J/OL]. Brain Pathol, 2021, 33(2): e13023 [2025-07-28]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10041160/. DOI: 10.1111/bpa.13023.
[49]
WANG Z Q, LIU Z, LI S. Weak lesion feature extraction by dual-branch separation and enhancement network for safe hemorrhagic transformation prediction[J/OL]. Comput Med Imaging Graph, 2022, 97: 102038 [2025-07-21]. https://www.sciencedirect.com/science/article/abs/pii/S0895611122000118?via%3Dihub. DOI: 10.1016/j.compmedimag.2022.102038.
[50]
REN H H, SONG H J, CUI S G, et al. Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study[J/OL]. Eur Radiol Exp, 2025, 9: 8 [2025-07-19]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11735721/. DOI: 10.1186/s41747-024-00535-0.
[51]
RU X S, ZHAO S L, CHEN W D, et al. A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients[J/OL]. Biomed Eng Online, 2023, 22: 129 [2025-07-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10731772/. DOI: 10.1186/s12938-023-01193-w.
[52]
XU Y, LI X L, WU D, et al. Machine Learning-Based Model for Prediction of Hemorrhage Transformation in Acute Ischemic Stroke After Alteplase[J/OL]. Front Neurol, 2022, 13: 897903 [2025-07-14]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9226411/. DOI: 10.3389/fneur.2022.897903.

PREV Research progress on evaluating the therapeutic mechanism of acupuncture for ischemic stroke based on functional magnetic resonance imaging
NEXT MRI features and research advances of symptomatic developmental venous anomaly
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn