切换至 "中华医学电子期刊资源库"

中华神经创伤外科电子杂志 ›› 2022, Vol. 08 ›› Issue (04) : 242 -246. doi: 10.3877/cma.j.issn.2095-9141.2022.04.009

综述

CT联合征象及纹理分析在预测自发性脑出血血肿扩大中的研究进展
张寒1, 丁涟沭1,()   
  1. 1. 223300 淮安,南京医科大学附属淮安第一医院神经外科
  • 收稿日期:2022-06-27 出版日期:2022-08-15
  • 通信作者: 丁涟沭
  • 基金资助:
    江苏省卫生健康委医学科研重点项目(ZD2021051)

Review of CT sign combination and texture analysis in predicting hematoma enlargement of spontaneous intracerebral hemorrhage

Han Zhang1, Lianshu Ding1,()   

  1. 1. Department of Neurosurgery, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
  • Received:2022-06-27 Published:2022-08-15
  • Corresponding author: Lianshu Ding
引用本文:

张寒, 丁涟沭. CT联合征象及纹理分析在预测自发性脑出血血肿扩大中的研究进展[J/OL]. 中华神经创伤外科电子杂志, 2022, 08(04): 242-246.

Han Zhang, Lianshu Ding. Review of CT sign combination and texture analysis in predicting hematoma enlargement of spontaneous intracerebral hemorrhage[J/OL]. Chinese Journal of Neurotraumatic Surgery(Electronic Edition), 2022, 08(04): 242-246.

血肿扩大(HE)是自发性脑出血(SICH)患者病情恶化及预后不良的独立危险因素,因此早期预测HE的发生具有重要临床意义。CT作为SICH患者首选辅助检查,其特征影像已被广泛应用于预测HE的发生,但其预测效能因征象单一及个人主观判断等因素影响而降低。为提高CT征象对HE的预测能力,多征象联合及基于CT图像的纹理分析成为当前研究新聚点。本文就近年来CT联合征象及纹理分析在预测SICH患者早期HE中的研究进展作一综述。

Hematoma enlargement (HE) is an independent risk factor for deterioration and poor prognosis in patients with spontaneous intracerebral hemorrhage (SICH), so it is of great clinical significance to predict the occurrence of HE in early stage. CT as the first choice of auxiliary examination for patients with SICH, its characteristic images have been widely used to predict the occurrence of HE, but its predictive efficiency is reduced due to single signs and personal subjective judgment and other factors. In order to improve the prediction ability of CT signs to HE, the combination of multi-features and texture analysis based on CT images have become a new focus of the current research. This article reviews the research progress of CT combined signs and texture analysis in predicting early HE in patients with SICH in recent years.

[1]
Macdonald RL. Management of intracranial hemorrhage in the anticoagulated patient[J]. Neurosurg Clin N Am, 2018, 29(4): 605-613.
[2]
Marcolini E, Stretz C, DeWitt KM. Intracranial hemorrhage and intracranial hypertension[J]. Emerg Med Clin North Am, 2019, 37(3): 529-544.
[3]
Delcourt C, Huang Y, Arima H, et al. Hematoma growth and outcomes in intracerebral hemorrhage: the INTERACT1 study[J]. Neurology, 2012, 79(4): 314-319.
[4]
Serrano E, López-Rueda A, Moreno J, et al. The new hematoma maturity score is highly associated with poor clinical outcome in spontaneous intracerebral hemorrhage[J]. Eur Radiol, 2022, 32(1): 290-299.
[5]
中华医学会神经病学分会,中华医学会神经病学分会脑血管病学组.中国脑出血诊治指南(2019)[J].中华神经科杂志, 2019, 52(12): 994-1005.
[6]
Nawabi J, Elsayed S, Kniep H, et al. Inter- and intrarater agreement of spot sign and noncontrast CT markers for early intracerebral hemorrhage expansion[J]. J Clin Med, 2020, 9(4): 1020.
[7]
Dowlatshahi D, Morotti A, Al-Ajlan FS, et al. Interrater and intrarater measurement reliability of noncontrast computed tomography predictors of intracerebral hemorrhage expansion[J]. Stroke, 2019, 50(5): 1260-1262.
[8]
Cai J, Zhu H, Yang D, et al. Accuracy of imaging markers on noncontrast computed tomography in predicting intracerebral hemorrhage expansion[J]. Neurol Res, 2020, 42(11): 973-979.
[9]
Shakya MR, Fu F, Zhang M, et al. Comparison of black hole sign, satellite sign, and iodine sign to predict hematoma expansion in patients with spontaneous intracerebral hemorrhage[J]. Biomed Res Int, 2021, 2021: 3919710.
[10]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446.
[11]
Ranjbar S, Ross Mitchell J. Chapter 8-an introduction to radiomics: an evolving cornerstone of precision medicine. Biomedical texture analysis[M]. Pittsburgh: Academic Press, 2017: 223-245.
[12]
王策,钱增辉,蔡泽豪,等.机器学习结合影像组学特征鉴别间变性胶质细胞瘤和胶质母细胞瘤[J].中华神经医学杂志, 2020, 19(3): 224-228.
[13]
Li Q, Liu QJ, Yang WS, et al. Island sign: an imaging predictor for early hematoma expansion and poor outcome in patients with intracerebral hemorrhage[J]. Stroke, 2017, 48(11): 3019-3025.
[14]
Li Q, Zhang G, Xiong X, et al. Black hole sign: novel imaging marker that predicts hematoma growth in patients with intracerebral hemorrhage[J]. Stroke, 2016, 47(7): 1777-1781.
[15]
王丹丹,王学建,潘南南. CT岛征和黑洞征及其联合征象对原发性脑出血早期血肿扩大的预测价值[J].天津医药, 2021, 49(2): 199-202.
[16]
王希,仲艳,颜伟,等. CT平扫岛征和黑洞征对原发性脑出血早期血肿扩大的预测价值[J].中华神经外科杂志, 2021, 37(6): 557-561.
[17]
Li Q, Zhang G, Huang YJ, et al. Blend sign on computed tomography: novel and reliable predictor for early hematoma growth in patients with intracerebral hemorrhage[J]. Stroke, 2015, 46(8): 2119-2123.
[18]
Huang YW, Yang MF. Combining investigation of imaging markers (island sign and blend sign) and clinical factors in predicting hematoma expansion of intracerebral hemorrhage in the basal ganglia[J]. World Neurosurg, 2018, 120: e1000-e1010.
[19]
贾维,石长青,刘亚龙,等. CT平扫岛征和混合征对自发性脑出血患者早期血肿扩大的预测作用[J].中华神经外科杂志, 2019, 35(10): 1036-1040.
[20]
Ng D, Churilov L, Mitchell P, et al. The CT swirl sign is associated with hematoma expansion in intracerebral hemorrhage[J]. AJNR Am J Neuroradiol, 2018, 39(2): 232-237.
[21]
Xiong X, Li Q, Yang WS, et al. Comparison of swirl sign and black hole sign in predicting early hematoma growth in patients with spontaneous intracerebral hemorrhage[J]. Med Sci Monit, 2018, 24: 567-573.
[22]
孙羽,郝妮娜,付乐君,等.基于倾向性评分评价漩涡征预测自发性脑出血血肿扩大的价值[J].国际医学放射学杂志, 2021, 44(4): 415-419.
[23]
周志敏,周逸飞,徐亮.岛征和漩涡征预测脑出血早期血肿扩大价值[J].中国CT和MRI杂志, 2021, 19(8): 1-4.
[24]
Barras CD, Tress BM, Christensen S, et al. Density and shape as CT predictors of intracerebral hemorrhage growth[J]. Stroke, 2009, 40(4): 1325-1331.
[25]
杨文松,李琦,王星辰,等. CT平扫混合征和黑洞征及其联合征象对脑出血患者早期血肿扩大的预测价值[J].中国脑血管病杂志, 2017, 14(11): 561-565, 579.
[26]
裴潘,郝伟伟,张敏,等.脑出血血肿扩大的危险因素及CT平扫预测血肿扩大的价值分析[J].中国临床保健杂志, 2020, 23(3): 415-418.
[27]
Shimoda Y, Ohtomo S, Arai H, et al. Satellite sign: a poor outcome predictor in intracerebral hemorrhage[J]. Cerebrovasc Dis, 2017, 44(3-4): 105-112.
[28]
王治斌.血肿周围水肿等常见CT征象对自发性脑出血早期血肿扩大的预测[D].北京:中国医学科学院北京协和医学院, 2019.
[29]
Yang H, Luo Y, Chen S, et al. The predictive accuracy of satellite sign for hematoma expansion in intracerebral hemorrhage: a meta-analysis[J]. Clin Neurol Neurosurg, 2020, 197: 106139.
[30]
宋祖华,周治明,郭大静,等.基于平扫CT的Logistic回归模型和朴素贝叶斯模型预测血肿扩大[J].中国医学影像技术, 2021, 37(1): 30-34.
[31]
傅璠, Ratna Shakya Milind,於帆,等. CT平扫预测高血压脑出血早期血肿扩大的价值[J].医学影像学杂志, 2020, 30(11): 1961-1964.
[32]
Boulouis G, Morotti A, Brouwers HB, et al. Association between hypodensities detected by computed tomography and hematoma expansion in patients with intracerebral hemorrhage[J]. JAMA Neurol, 2016, 73(8): 961-968.
[33]
Morotti A, Boulouis G, Romero JM, et al. Blood pressure reduction and noncontrast CT markers of intracerebral hemorrhage expansion[J]. Neurology, 2017, 89(6): 548-554.
[34]
陈丽芳.非增强CT征象对脑出血患者早期血肿扩大预测价值比较研究[D].福州:福建医科大学, 2019.
[35]
宋杨君. CT平扫影像学征象预测自发性脑出血患者早期血肿扩张的临床价值分析[J].中国CT和MRI杂志, 2021, 19(11): 20-22.
[36]
王少华.高血压性脑出血血肿早期增大与平扫CT征象的相关性分析[D].北京:中国医学科学院北京协和医学院, 2019.
[37]
王娟,郭龙军,李昌,等.基于CT评估脑出血征象和血肿体积、高低密度差预测血肿增大及软化灶的价值研究[J].影像科学与光化学, 2021, 39(2): 298-304.
[38]
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
[39]
Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges[J]. Radiographics, 2017, 37(5): 1483-1503.
[40]
Barras CD, Tress BM, Christensen S, et al. Quantitative CT densitometry for predicting intracerebral hemorrhage growth[J]. AJNR Am J Neuroradiol, 2013, 34(6): 1139-1144.
[41]
Shen Q, Shan Y, Hu Z, et al. Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement[J]. Eur Radiol, 2018, 28(10): 4389-4396.
[42]
丁川,李小虎,王俊,等.基于CT放射组学预测高血压性脑出血血肿扩大的研究[J].安徽医科大学学报, 2022, 57(1): 161-164.
[43]
Liu Y, Fang Q, Jiang A, et al. Texture analysis based on U-Net neural network for intracranial hemorrhage identification predicts early enlargement[J]. Comput Methods Programs Biomed, 2021, 206: 106140.
[44]
彭霞,包婉秋,王磊,等. CT纹理特征在早期自发性脑出血血肿扩大的预测应用研究[J].临床放射学杂志, 2022, 41(2): 236-240.
[45]
Rodriguez-Luna D, Coscojuela P, Rodriguez-Villatoro N, et al. Multiphase CT angiography improves prediction of intracerebral hemorrhage expansion[J]. Radiology, 2017, 285(3): 932-940.
[1] 洪玮, 叶细容, 刘枝红, 杨银凤, 吕志红. 超声影像组学联合临床病理特征预测乳腺癌新辅助化疗完全病理缓解的价值[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 571-579.
[2] 明昊, 肖迎聪, 巨艳, 宋宏萍. 乳腺癌风险预测模型的研究现状[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(05): 287-291.
[3] 陈晓玲, 钟永洌, 刘巧梨, 李娜, 张志奇, 廖威明, 黄桂武. 超高龄髋膝关节术后谵妄及心血管并发症风险预测[J/OL]. 中华关节外科杂志(电子版), 2024, 18(05): 575-584.
[4] 黄鸿初, 黄美容, 温丽红. 血液系统恶性肿瘤患者化疗后粒细胞缺乏感染的危险因素和风险预测模型[J/OL]. 中华实验和临床感染病杂志(电子版), 2024, 18(05): 285-292.
[5] 奚玲, 仝瀚文, 缪骥, 毛永欢, 沈晓菲, 杜峻峰, 刘晔. 基于肌少症构建的造口旁疝危险因素预测模型[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 48-51.
[6] 屈勤芳, 束方莲. 盆腔器官脱垂患者盆底重建手术后压力性尿失禁发生的影响因素及列线图预测模型构建[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 606-612.
[7] 公宇, 廖媛, 尚梅. 肝细胞癌TACE术后复发影响因素及预测模型建立[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 818-824.
[8] 何慧玲, 鲁祖斌, 冯嘉莉, 梁声强. 术前外周血NLR和PLR对结肠癌术后肝转移的影响[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(05): 682-687.
[9] 王贝贝, 崔振义, 王静, 王晗妍, 吕红芝, 李秀婷. 老年股骨粗隆间骨折患者术后贫血预测模型的构建与验证[J/OL]. 中华老年骨科与康复电子杂志, 2024, 10(06): 355-362.
[10] 孙晗, 于冰, 武侠, 周熙朗. 基于循环肿瘤DNA 甲基化的结直肠癌筛查预测模型的构建与验证[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 500-506.
[11] 韦巧玲, 黄妍, 赵昌, 宋庆峰, 陈祖毅, 黄莹, 蒙嫦, 黄靖. 肝癌微波消融术后中重度疼痛风险预测列线图模型构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 715-721.
[12] 蔡晓雯, 李慧景, 丘婕, 杨翼帆, 吴素贤, 林玉彤, 何秋娜. 肝癌患者肝动脉化疗栓塞术后疼痛风险预测模型的构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 722-728.
[13] 王誉英, 刘世伟, 王睿, 曾娅玲, 涂禧慧, 张蒲蓉. 老年乳腺癌新辅助治疗病理完全缓解的预测因素分析[J/OL]. 中华临床医师杂志(电子版), 2024, 18(07): 641-646.
[14] 董晟, 郎胜坤, 葛新, 孙少君, 薛明宇. 反向休克指数乘以格拉斯哥昏迷评分对老年严重创伤患者发生急性创伤性凝血功能障碍的预测价值[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 541-547.
[15] 黄圣楷, 许斌, 苏健, 孙龙. 海南省2010~2020年乙型肝炎流行趋势的时间序列分析及预测[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 555-561.
阅读次数
全文


摘要