ISSN 1004-4140
CN 11-3017/P
Volume 33 Issue 1
Jan.  2024
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Abstract
References
WANG S, ZHAO J H. Research Progress in Imaging Radiomics Based on Computed Tomography and Magnetic Resonance in Ischemic Stroke[J]. CT Theory and Applications, 2024, 33(1): 83-89. DOI: 10.15953/j.ctta.2023.080. (in Chinese).
Citation: WANG S, ZHAO J H. Research Progress in Imaging Radiomics Based on Computed Tomography and Magnetic Resonance in Ischemic Stroke[J]. CT Theory and Applications, 2024, 33(1): 83-89. DOI: 10.15953/j.ctta.2023.080. (in Chinese).
shu

Research Progress in Imaging Radiomics Based on Computed Tomography and Magnetic Resonance in Ischemic Stroke

  • 1.

    Graduate School of Inner Mongolia Medical University, Hohhot 010059, China

  • 2.

    Department of Medical Imaging, Inner Mongolia People’s Hospital, Hohhot 010017, China

More Information
  • Received Date: April 03, 2023
  • Revised Date: July 16, 2023
  • Accepted Date: July 31, 2023
  • Available Online: August 07, 2023
    Graphical Abstract
    • Abstract

  • Ischemic stroke, also known as cerebral infarction, is a disorder impacting the blood supply to the brain tissue due to complex reasons, resulting in irreversible damage to the infarct site. According to China's sixth census, approximately 1.94 million individuals died of a stroke in 2018. With the increasing age of the Chinese population, stroke is expected to continue affecting human health in the coming decade. Imaging examination is an indispensable means to diagnose the disease and evaluate its prognosis. In recent years, artificial intelligence and radiomics have been widely used in the medical industry, among which convolutional neural network is more prevalent. Mainly, it has shown obvious superiority in the imaging diagnosis of ischemic stroke, and its efficiency is far higher than manual film reading. This article reviews the research progress of imaging omics based on computed tomography (CT) and magnetic resonance in ischemic stroke.

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    • References (39)
  • [1]
    王陇德, 彭斌, 张鸿祺, 等. 《中国脑卒中防治报告2020》概要[J]. 中国脑血管病杂志, 2022,19(2): 136−144. DOI: 10.3969/j.issn.1672-5921.2022.02.011.

    WANG L D, PENG B, ZHANG H Q, et al. Brief report on stroke prevention and treatment in China, 2020[J]. Chinese Journal of Cerebrovascular Diseases, 2022, 19(2): 136−144. DOI: 10.3969/j.issn.1672-5921.2022.02.011. (in Chinese).
    [2]
    宗宁宁, 张思源, 谭逸, 等. 近10年中国缺血性卒中治疗和预防进展[J]. 国际脑血管病杂志, 2022,30(12): 881−889. DOI: 10.3760/cma.j.issn.1673-4165.2022.12.001.

    ZONG N N, ZHANG S Y, TAN Y, et al. Progress in diagnosis, treatment and prevention of ischemic stroke in China in recent 10 years[J]. International Journal of Cerebrovascular Diseases, 2022, 30(12): 881−889. DOI: 10.3760/cma.j.issn.1673-4165.2022.12.001. (in Chinese).
    [3]
    UDDIN M, WANG Y, WOODBURY-SMITH M. Artificial intelligence for precision medicine in neurodevelopmental disorders[J]. NPJ Digital Medicine, 2019, 2(1): 1−10. doi: 10.1038/s41746-018-0076-7
    [4]
    BIVARD A, CHURILOV L, PARSONS M. Artificial intelligence for decision support in acute stroke: Current roles and potential[J]. Nature Reviews Neurology, 2020, 16(10): 575−585. doi: 10.1038/s41582-020-0390-y
    [5]
    JACQUES T, FOURNIER L, ZINS M, et al. Proposals for the use of artificial intelligence in emergency radiology[J]. Diagnostic and Interventional Imaging, 2021, 102(2): 63−68. doi: 10.1016/j.diii.2020.11.003
    [6]
    HOWARD J. Artificial intelligence: Implications for the future of work[J]. American Journal of Industrial Medicine, 2019, 62(11): 917−926. doi: 10.1002/ajim.23037
    [7]
    ZAHARCHUK G, GONG E, WINTERMARK M, et al. Deep learning in neuroradiology[J]. American Journal of Neuroradiology, 2018, 39(10): 1776−1784. doi: 10.3174/ajnr.A5543
    [8]
    LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis[J]. European Journal of Cancer, 2012, 48(4): 441−446. doi: 10.1016/j.ejca.2011.11.036
    [9]
    LO C M, HUNG P H, LIN D T. Rapid assessment of acute ischemic stroke by computed tomography using deep convolutional neural networks[J]. Journal of Digital Imaging, 2021, 34(3): 637−646. DOI: 10.1007/S10278-021-00457-Y.
    [10]
    QIU W, KUANG H, TELEG E, et al. Machine learning for detecting early infarction in acute stroke with non-contrast-enhanced CT[J]. Radiology, 2020, 294(3): 638−644. doi: 10.1148/radiol.2020191193
    [11]
    WU G, CHEN X, LIN J, et al. Identification of invisible ischemic stroke in noncontrast CT based on novel two-stage convolutional neural network model[J]. Medical Physics, 2021, 48(3): 1262−1275. doi: 10.1002/mp.14691
    [12]
    KUMAR A, GHOSAL P, KUNDU S S, et al. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images[J]. Computer Methods and Programs in Biomedicine, 2022, 226: 107157. DOI: 10.1016/j.cmpb.2022.107157.
    [13]
    KUANG H, NAJM M, CHAKRABORTY D, et al. Automated ASPECTS on noncontrast CT scans in patients with acute ischemic stroke using machine learning[J]. American Journal of Neuroradiology, 2019, 40(1): 33−38. doi: 10.3174/ajnr.A5889
    [14]
    KUANG H, QIU W, NAJM M, et al. Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients[J]. International Journal of Stroke, 2020, 15(5): 528−534. doi: 10.1177/1747493019895702
    [15]
    荆利娜, 高培毅, 杜万良, 等. 自动ASPECTS评分法在急性缺血性卒中早期影像评估中的应用价值[J]. 中国卒中杂志, 2021,16(5): 463−469. DOI: 10.3969/j.issn.1673-5765.2021.05.008.

    JING L N, GAO P Y, DU W L, et al. The value of automated ASPECTS scoring in imaging assessment of early ischemic changes in acute ischemic stroke[J]. Chinese Journal of Stroke, 2021, 16(5): 463−469. DOI: 10.3969/j.issn.1673-5765.2021.05.008. (in Chinese).
    [16]
    吴亚平, 方婷, 魏焕焕, 等. 级联VB‑Net分割模型用于急性缺血性脑卒中患者扩散加权成像中缺血核心分割的研究[J]. 中华放射学杂志, 2022,56(1): 25−29. DOI: 10.3760/cma.j.cn112149-20210415-00373.

    WU Y P, FANG T, WEI H H, et al. Segmentation of core infarct in acute ischemic stroke in diffusion weighted imaging using cascaded VB-Net[J]. Chinese Journal of Radiology, 2022, 56(1): 25−29. DOI: 10.3760/cma.j.cn112149-20210415-00373. (in Chinese).
    [17]
    LU J, ZHOU Y, LV W, et al. Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model[J]. Theranostics, 2022, 12(12): 5564-5573.
    [18]
    El-HARIRI H, SOUTO MAIOR NETO L A, CIMFLOVA P, et al. Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with acute ischemic stroke[J]. Computers in Biology and Medicine, 2022, 141: 105033. DOI: 10.1016/j.compbiomed.2021.105033.
    [19]
    BARROS R S, TOLHUISEN M L, BOERS A M, et al. Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks[J]. Journal of Neuro Interventional Surgery, 2020, 12(9): 848−852.
    [20]
    TULADHAR A, SCHIMERT S, RAJASHEKAR D, et al. Automatic segmentation of stroke lesions in non-contrast computed tomography datasets with convolutional neural networks[J]. IEEE Access, 2020, 8(8): 94871−94879.
    [21]
    李晓庆, 王可欣, 额·图娅, 等. 利用 U-net算法在头CT平扫图像上分割脑梗死的初步探究[J]. 放射学实践, 2022,37(6): 669−675. DOI: 10.13609/j.cnki1000-0313.2022.06.001.

    LI X Q, WANG K X, E T Y, et al. A preliminary study of cerebral infraction segmentation on CT images based on U-Net algorithm[J]. Radiologic Practice, 2022, 37(6): 669−675. DOI: 10.13609/j.cnki1000-0313.2022.06.001. (in Chinese).
    [22]
    KASASBEH A S, CHRISTENSEN S, PARSONS M W, et al. Artificial neural network computer tomography perfusion prediction of ischemic core[J]. Stroke, 2019, 50(6): 1578−1581. doi: 10.1161/STROKEAHA.118.022649
    [23]
    SHETH S A, LOPEZ-RIVERA V, BARMAN A, et al. Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography[J]. Stroke, 2019, 50(11): 3093−3100. doi: 10.1161/STROKEAHA.119.026189
    [24]
    周运锋, 董立军, 杨晨, 等. 动态CTA对非时间窗内前循环脑缺血患者侧枝及血流状态的评估[J]. 放射学实践, 2018,33(3): 259−264. DOI: 10.13609/j.cnki.1000-0313.2018.03.007.

    ZHOU Y F, DONG L J, YANG C, et al. Study of dynamic CT angiography in the assessment of collateral circulation and blood perfusion in patients with anterior circulation cerebral ischemic patients without time window[J]. Radiologic Practice, 2018, 33(3): 259−264. DOI: 10.13609/j.cnki.1000-0313.2018.03.007. (in Chinese).
    [25]
    昝芹, 陈晓荣, 杨文琼. 颈动脉粥样硬化斑块与急性缺血性脑卒中的相关性研究[J]. CT理论与应用研究, 2023,32(1): 105−112. DOI: 10.15953/j.ctta.2022.039.

    ZAN Q, CHEN X R, YANG W Q. Correlation between carotid atherosclerotic plaques and acute ischemic stroke[J]. CT Theory and Applications, 2023, 32(1): 105−112. DOI: 10.15953/j.ctta.2022.039. (in Chinese).
    [26]
    RODRIGUES G, BARREIRA C M, BOUSLAMA M, et al. Automated large artery occlusion detection in stroke: A single-center validation study of an artificial intelligence algorithm[J]. Cerebrovascular Diseases, 2022, 51(2): 259−264. doi: 10.1159/000519125
    [27]
    YOU J, TSANG A C O, YU P L H, et al. Automated hierarchy evaluation system of large vessel occlusion in acute ischemia stroke[J]. Frontiers in Neuroinformatics, 2020, 14: 13. doi: 10.3389/fninf.2020.00013
    [28]
    WEYLAND C S, PAPANAGIOTOU P, SCHMITT N, et al. Hyperdense artery sign (HAS) in patients with acute ischemic stroke-Automated detection with artificial intelligence driven software[J]. Frontiers in Neurology, 2022, 13: 563.
    [29]
    SHINOHARA Y, TAKAHASHIN, LEE Y, et al. Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke[J]. Japanese Journal of Radiology, 2020, 38(2): 112−117. doi: 10.1007/s11604-019-00894-4
    [30]
    LIEW S L, ANGLIN J M, BANKS N W, et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations[J]. Scientific Data, 2018, 5(1): 1−11. doi: 10.1038/s41597-018-0002-5
    [31]
    中国医师协会放射医师分会. 基于深度学习脑血管病智能影像检测规范化研究设计的中国专家建议[J]. 中华放射学杂志, 2023,57(1): 17−26. DOI: 10.3760/cma.j.cn112149-20211225-01146.

    Chinese Association of Radiologists. Chinese expert recommendations on the standardization of study design of deep learning-based automatic imaging detection in cerebrovascular disease[J]. Chinese Journal of Radiology, 2023, 57(1): 17−26. DOI: 10.3760/cma.j.cn112149-20211225-01146. (in Chinese).
    [32]
    姜亮, 周蕾蕾, 艾中萍, 等. 基于DWI和FLAIR的深度学习预测急性脑卒中发病时间[J]. 中华放射学杂志, 2021, 55(8): 811-816. DOI: 10.3760/cma.j.cn112149-20201027-01187.

    JIANG L, ZHOU L L, AI Z P, et al. Prediction of the onset time of acute stroke by deep learning based on DWI and FLAIR[J]. 2021, 55(8): 811-816. DOI:10.3760/cma.j.cn112149-20201027-01187. (in Chinese).
    [33]
    郭静丽, 彭明洋, 王同兴, 等. 基于DWI和FLAIR的机器学习预测急性脑卒中发病时间的研究[J]. 磁共振成像, 2022,13(3): 22−25, 42. DOI: 10.12015/issn.1674-8034.2022.03.005.

    GUO J L, PENG M Y, WANG T X, et al. The study of machine learning based on DWI and FLAIR in the prediction of onset time of acute stroke[J]. Chinese Journal of Magnetic Resonance Imaging, 2022, 13(3): 22−25, 42. DOI: 10.12015/issn.1674-8034.2022.03.005. (in Chinese).
    [34]
    KIM Y C, LEE J E, YU I, et al. Evaluation of diffusion lesion volume measurements in acute ischemic stroke using encoder-decoder convolutional network[J]. Stroke, 2019, 50(6): 1444−1451. doi: 10.1161/STROKEAHA.118.024261
    [35]
    BENTLEY P, GANESALINGAM J, JONES A L C, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning[J]. NeuroImage: Clinical, 2014, 4(C): 635−640.
    [36]
    段祺, 段曹辉, 周世擎, 等. 基于深度学习的快速磁敏感加权成像评估急性缺血性卒中[J]. 中华放射学杂志, 20233,57(1): 34−40. DOI: 10.3760/cma.j.cn112149-20220615-00515.

    DUAN Q, DUAN C H, ZHOU S Q, et al. Application of fast susceptibility weighted imaging based on deep learning in assessment of acute ischemic stroke[J]. Chinese Journal of Radiology, 20233, 57(1): 34−40. DOI: 10.3760/cma.j.cn112149-20220615-00515. (in Chinese).
    [37]
    REHME A K, VOLZ L J, FEIS D L, et al. Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques[J]. Cerebral Cortex, 2015, 25(9): 3046−3056. doi: 10.1093/cercor/bhu100
    [38]
    CHAUHAN S, VIG L, DE FILIPPO DE GRAZIA M, et al. A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images[J]. Frontiers in Neuroinformatics, 2019, 13: 53. doi: 10.3389/fninf.2019.00053
    [39]
    LI X, WU M, SUN C, et al. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients[J]. European Journal of Neurology, 2020, 27(8): 1656−1663. doi: 10.1111/ene.14295
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