CT灌注成像联合人工智能评估脑梗死后再灌注损伤的临床意义

doi:10.3969/j.issn.1006-5725.2025.02.017

Abstract

Abstract:

Objective To analyze the significance of CT perfusion imaging combined with artificial intelligence （AI） in evaluating reperfusion injury after cerebral infarction. Methods 106 patients with cerebral infarction admitted to the hospital from January 2019 to October 2023 were prospectively selected as the study objects. Patients were divided into reperfusion injury group and no perfusion injury group according to whether reperfusion injury occurred after 14 days of thrombolytic therapy. CT perfusion imaging and AI parameters were compared between reperfusion injury group and non-perfusion injury group. The factors affecting reperfusion injury in cerebral infarction patients after thrombolytic therapy were analyzed. The value of CT perfusion imaging parameters combined with AI in predicting reperfusion injury after thrombolytic therapy in cerebral infarction patients was analyzed. Results 31 cases had reperfusion injury， and the other 75 cases had no perfusion injury. CBF， average CT value and entropy level in the reperfusion injury group were lower than those in the non-perfusion injury group （P < 0.05）， CBV， MTT， TTP and kurtosis were higher than those in the non-perfusion injury group （P < 0.05）. Logistic regression analysis showed that NIHSS （OR = 5.228， 95%CI： 2.151 ~ 12.705）， CBF（OR = 3.777， 95%CI： 1.554 ~ 9.180）， CBV（OR = 3.699， 95%CI： 1.522 ~ 9.989） and average CT value （OR = 4.125， 95%CI： 1.697 ~ 10.024） were the influencing factors of reperfusion injury in cerebral infarction patients after thrombolytic therapy （P < 0.05）. ROC curve results showed that the sensitivity of CBF， CBV， average CT value and their combination in predicting reperfusion injury after thrombolytic therapy in cerebral infarction patients were 67.74%， 70.97%， 77.42%， 87.10%， and the specificity were 70.67%， 74.67%， 77.33%， 90.67%， AUC values were 0.665， 0.667， 0.744 and 0.908. Conclusion CT perfusion imaging combined with AI is effective in evaluating reperfusion injury after cerebral infarction.

Key words: CT perfusion imaging, artificial intelligence, cerebral infarction, reperfusion injury

CLC Number:

Wei LU,Pan ZHANG,Yushu. QIN. Clinical significance of CT perfusion imaging combined with artificial intelligence in evaluating reperfusion injury after cerebral infarction[J]. The Journal of Practical Medicine, 2025, 41(2): 264-270.

Figures/Tables 6

Fig.1

Tab.1

Tab.2

Tab.3

Tab.4

Fig.2

References 25

1	王立志，罗伟良，刘武，等. Percheron动脉闭塞致双侧丘脑梗死临床及影像学特征分析［J］. 实用医学杂志， 2020，36（4）：553-556.
2	SHANG W， ZHANG Y， XUE L， et al. Evaluation of collateral circulation and short-term prognosis of patients with acute cerebral infarction by perfusion-weighted MRI［J］. Ann Palliat Med， 2022， 11（4）：1351-1359. doi:10.21037/apm-21-3589 doi: 10.21037/apm-21-3589
3	OCHIAI H， KANEMARU K， MATSUDA S， et al. A case of acute cerebral infarction with a favorable prognosis after rt-PA administration by a general physician with telestroke support［J］. J Rural Med， 2021， 16（2）：119-122. doi:10.2185/jrm.2020-048 doi: 10.2185/jrm.2020-048
4	CHIHI M， DARKWAH OPPONG M， PIERSCIANEK D， et al. Analysis of brain natriuretic peptide levels after traumatic acute subdural hematoma and the risk of post-operative cerebral infarction［J］. J Neurotrauma， 2021， 38（22）：3068-3076. doi:10.1089/neu.2021.0169 doi: 10.1089/neu.2021.0169
5	ANDE S R， GRYNSPAN J， AVIV R I， et al. Imaging for predicting hemorrhagic transformation of acute ischemic stroke-a narrative review［J］. Can Assoc Radiol J， 2022， 73（1）：194-202. doi:10.1177/08465371211018369 doi: 10.1177/08465371211018369
6	秦霜，戴才文，王根强，等. CT灌注成像联合血清D-D、MMP-9对急性脑梗死患者溶栓后出血转化的预测价值研究［J］. 中国CT和MRI杂志，2023，21（6）：24-27.
7	WOUTERS A， ROBBEN D， CHRISTENSEN S， et al. Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging［J］. Stroke， 2022， 53（2）：569-577. doi:10.1161/strokeaha.121.034444 doi: 10.1161/strokeaha.121.034444
8	RAMOS L A， VAN DER STEEN W E， SALES BARROS R， et al. Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage［J］. J Neurointerv Surg， 2019， 11（5）：497-502. doi:10.1136/neurintsurg-2018-014258 doi: 10.1136/neurintsurg-2018-014258
9	徐伟，李辉萍，贺国华，等. 利用人工智能系统预测脑梗死静脉溶栓后的出血转化［J］. 中国卫生统计，2021，38（2）：250-253.
10	MOROZOV A， TARATKIN M， BAZARKIN A， et al. A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading［J］. Prostate Cancer Prostatic Dis， 2023， 26（4）：681-692. doi:10.1038/s41391-023-00673-3 doi: 10.1038/s41391-023-00673-3
11	MAKITIE A A， ALABI R O， NG S P， et al. Artificial Intelligence in Head and Neck Cancer： A Systematic Review of Systematic Reviews［J］. Adv Ther， 2023， 40（8）：3360-3380. doi:10.1007/s12325-023-02527-9 doi: 10.1007/s12325-023-02527-9
12	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. 中国急性缺血性脑卒中诊治指南2018［J］. 中华神经科杂志， 2018， 51（9）：666-682. doi:10.3760/cma.j.issn.1006-7876.2018.09.004 doi: 10.3760/cma.j.issn.1006-7876.2018.09.004
13	KNIGHT-GREENFIELD A， NARIO J J Q， GUPTA A. Causes of acute stroke： A patterned approach［J］. Radiol Clin North Am， 2019， 57（6）：1093-1108. doi:10.1016/j.rcl.2019.07.007 doi: 10.1016/j.rcl.2019.07.007
14	CHALOS V， VAN DER ENDE N A M， LINGSMA H F， et al. National institutes of health stroke scale： An alternative primary outcome measure for trials of acute treatment for ischemic stroke［J］. Stroke， 2020， 51（1）：282-290. doi:10.1161/str.50.suppl_1.tmp6 doi: 10.1161/str.50.suppl_1.tmp6
15	李水仙，陈星宇，阳清伟，等. Y型双支架取栓治疗急性大脑中动脉M1段分叉部闭塞脑梗死7例［J］. 介入放射学杂志，2022，31（2）：167-171.
16	NG F C， CHURILOV L， YASSI N， et al. Prevalence and Significance of Impaired Microvascular Tissue Reperfusion Despite Macrovascular Angiographic Reperfusion （No-Reflow）［J］. Neurology， 2022， 98（8）：790-801. doi:10.1212/wnl.0000000000013210 doi: 10.1212/wnl.0000000000013210
17	MOUGHAL S， TRIPPIER S， AL-MOUSA A， et al. Strokectomy for malignant middle cerebral artery infarction： Experience and meta-analysis of current evidence［J］. J Neurol， 2022， 269（1）：149-158. doi:10.1007/s00415-020-10358-9 doi: 10.1007/s00415-020-10358-9
18	陈峰，王洁，甘解华，等. MR三维动脉自旋标记、双指数模型弥散加权成像评估急性前循环大血管闭塞患者再通后再灌注损伤风险［J］. 介入放射学杂志，2021，30（12）：1210-1214.
19	NOONE M L， MOIDEEN F， KRISHNA R B， et al. Mobile App Based Strategy Improves Door-to-Needle Time in the Treatment of Acute Ischemic Stroke［J］. J Stroke Cerebrovasc Dis， 2020，29（12）：105319. doi:10.1016/j.jstrokecerebrovasdis.2020.105319 doi: 10.1016/j.jstrokecerebrovasdis.2020.105319
20	BAO L， ZHANG S， GONG X， et al. Trousseau Syndrome Related Cerebral Infarction： Clinical Manifestations， Laboratory Findings and Radiological Features［J］. J Stroke Cerebrovasc Dis， 2020，29（9）：104891. doi:10.1016/j.jstrokecerebrovasdis.2020.104891 doi: 10.1016/j.jstrokecerebrovasdis.2020.104891
21	SHANG W， ZHANG Y， XUE L， et al. Evaluation of collateral circulation and short-term prognosis of patients with acute cerebral infarction by perfusion-weighted MRI［J］. Ann Palliat Med， 2022，11（4）：1351-1359. doi:10.21037/apm-21-3589 doi: 10.21037/apm-21-3589
22	WU D， YIN L， ZHANG Y， et al. Evaluation of microcirculation in asymptomatic cerebral infarction with multi-parameter imaging of spectral CT［J］. Brain Res Bull， 2023，203（1）：110775. doi:10.1016/j.brainresbull.2023.110775 doi: 10.1016/j.brainresbull.2023.110775
23	韩龙，张海莲，马琼. CT灌注成像联合miR-195在急性脑梗死诊断及预后分析中的应用［J］. 影像科学与光化学， 2022， 40（4）：927-931.
24	YUEN N， MLYNASH M， O'RIORDAN A， et al. Cerebral perfusion imaging predicts final infarct volume after basilar artery thrombectomy［J］. J Stroke Cerebrovasc Dis， 2023，32（1）：106866. doi:10.1016/j.jstrokecerebrovasdis.2022.106866 doi: 10.1016/j.jstrokecerebrovasdis.2022.106866
25	ADAMOU A， BELTSIOS E T， BANIA A， al et， Papanagiotou P. Artificial intelligence-driven ASPECTS for the detection of early stroke changes in non-contrast CT： A systematic review and meta-analysis［J］. J Neurointerv Surg， 2023， 5（2）：298-304. doi:10.1136/jnis-2022-019447 doi: 10.1136/jnis-2022-019447

因素	再灌注损伤组（n = 31）	无灌注损伤组（n = 75）	t/χ ² 值	P值
性别/［例（%）］			0.871	0.351
男	20（64.52）	41（54.67）
女	11（35.48）	34（45.33）
年龄≥ 60岁/［例（%）］	19（61.29）	36（48.00）	1.552	0.213
基础疾病/［例（%）］
高血压	15（48.39）	26（34.67）	1.741	0.187
糖尿病	13（41.94）	24（32.00）	0.953	0.329
高脂血症	12（38.71）	25（33.33）	0.279	0.597
冠心病	4（12.90）	7（9.33）	0.301	0.584
吸烟史	12（38.71）	25（33.33）	0.279	0.597
饮酒史	6（19.35）	12（16.00）	0.175	0.676
房颤史	7（22.58）	15（20.00）	0.089	0.766
脑梗死部位/［例（%）］			0.418	0.812
丘脑	7（22.58）	13（17.33）
脑叶	5（16.13）	12（16.00）
基底节	19（61.29）	50（66.67）
TOAST分型/［例（%）］			0.498	0.779
大动脉粥样硬型脑梗死	18（58.06）	49（65.33）
小血管闭塞型脑梗死	10（32.26）	20（26.67）
心源性脑梗死	3（9.68）	6（8.00）
DNT/min	134.36 ± 25.36	115.21 ± 20.04	4.131	< 0.001
收缩压/mmHg	148.95 ± 15.47	146.84 ± 13.96	0.686	0.494
舒张压/mmHg	82.94 ± 10.26	83.29 ± 10.03	0.162	0.871
TG/（mmoL/L）	1.59 ± 0.26	1.52 ± 0.24	1.333	0.185
TC/（mmoL/L）	4.48 ± 0.62	4.31 ± 0.59	1.330	0.187
血肌酐/（μmoL /L）	91.32 ± 10.51	89.27 ± 9.95	0.949	0.345
尿素氮/（mmol/L）	4.52 ± 0.71	4.41 ± 0.68	0.748	0.456
纤维蛋白原/（g/L）	3.15 ± 0.48	2.81 ± 0.42	3.634	< 0.001
WBC计数/（× 10⁹/L）	43.36 ± 8.15	42.94 ± 7.92	0.246	0.806
PT/s	15.01 ± 2.01	14.54 ± 1.95	1.119	0.266
TT/s	14.36 ± 2.15	13.99 ± 2.08	0.825	0.411
NIHSS评分/分	9.01 ± 1.53	7.15 ± 1.29	6.388	< 0.001

组别	例数	CBF/［mL/（100g·min）］	CBV/（mL/100 g）	MTT/s	TTP/s	平均CT值	峰度	熵
再灌注损伤组	31	12.05 ± 1.76	1.23 ± 0.18	4.31 ± 0.65	26.28 ± 3.26	9.25 ± 1.49	5.23 ± 1.01	8.05 ± 1.23
无灌注损伤组	75	14.39 ± 1.92	1.01 ± 0.15	3.96 ± 0.61	24.51 ± 3.09	10.89 ± 1.58	4.06 ± 0.79	8.64 ± 1.36
t值		5.844	6.471	2.636	2.640	4.941	6.377	2.087
P值		< 0.001	< 0.001	0.010	0.010	< 0.001	< 0.001	0.039

因素	β	SE	Wald χ²值	P值	OR	95%CI
NIHSS评分	1.654	0.402	16.929	< 0.001	5.228	2.151 ~ 12.705
CBF	1.329	0.317	17.576	< 0.001	3.777	1.554 ~ 9.180
CBV	1.308	0.296	19.527	< 0.001	3.699	1.522 ~ 9.989
平均CT值	1.417	0.245	33.451	< 0.001	4.125	1.697 ~ 10.024

[1]	Yanyan JIA,Yijia SUN,Di ZHAO,Yan. SONG. Elderly acute progressive cerebral infarction patients′ serum PLA2， called S100B levels and the relationship between carotid plaques [J]. The Journal of Practical Medicine, 2025, 41(2): 244-249.
[2]	Chunyan LI,Ting XIAO,Bangcui WU,Yong CHEN,Mei TIAN. PKCβ inhibitor modulates macrophage phenotype and affects kidney ischemia-reperfusion injury during transplantation [J]. The Journal of Practical Medicine, 2025, 41(1): 23-29.
[3]	Song LI,Xingyou HE,Dian HE,Bo WANG,Yu ZHAN,Jingjing. SUN. The joint efficacy of NBP and LIPost C in treatment of elderly patients with large atherosclerotic cerebral infarction [J]. The Journal of Practical Medicine, 2024, 40(9): 1286-1292.
[4]	Rui ZHANG,Ying ZHOU,Wenji NI,Ya HUANG,Dandan LI,Tao JIN,Yong. ZHONG. Application value of artificial intelligence⁃basedretinal microvascular analysis in diagnosis of diabetes complications [J]. The Journal of Practical Medicine, 2024, 40(8): 1142-1147.
[5]	Liangjiang HUANG,Dewen MAO,Jinghui ZHENG,Minggang WANG,Chun YAO. Application of artificial intelligence in HE risk prediction modelling and research advances [J]. The Journal of Practical Medicine, 2024, 40(3): 289-294.
[6]	Xiangzhi XIAO,Guanxiong CHEN,Zhiwen HU. The relationship between EGCG targeted regulation of Nrf2⁃Keap1 signaling pathway and neuroprotective effect in cerebral infarction [J]. The Journal of Practical Medicine, 2024, 40(3): 309-315.
[7]	Zhe SHI,Xialin ZUO,Linhui PENG,Zhiwei LU,Kongping. LI. Effect of M1 microglial polarization on secondary damage in the thalamus after cerebral cortical infarction [J]. The Journal of Practical Medicine, 2024, 40(22): 3138-3145.
[8]	Yuliang HUANG,Ying TANG,Wenjuan YU,Junzhe. CHEN. The mechanism of cyclin D1 ameliorates renal ischemia⁃reperfusion⁃induced acute kidney injury by promotingglycolysis [J]. The Journal of Practical Medicine, 2024, 40(21): 3013-3022.
[9]	Lili TANG,Xinyu WANG,Jie ZHANG,Yue ZHAO,Xiaoyue LI. Research progress on the relationship between m6A methylation modification and acute kidney injury [J]. The Journal of Practical Medicine, 2024, 40(2): 278-282.
[10]	Yingjun TAO,Tengzhu REN,Feng WEI,Xintong. LIU. Effects of agatroban combined with mitochondrial transplantation on endothelial function and hemorheology in mice with cerebral ischemia⁃reperfusion injury [J]. The Journal of Practical Medicine, 2024, 40(19): 2665-2671.
[11]	Jingfang WAN,Kehong. CHEN. Advances in research on optimal dialysate bicarbonate concentration for maintenance hemodialysis patients [J]. The Journal of Practical Medicine, 2024, 40(18): 2520-2524.
[12]	Guidi ZOU,Xiaokai CHEN,Huihong TAN,Yi LI,Nan LI,Yefan CAO,Hewei. WANG. The clinical efficacy of closed⁃loop rehabilitation therapy by brain⁃computer interface combined with exoskeleton robotic hand for patients with hand dysfunction after cerebral infarction [J]. The Journal of Practical Medicine, 2024, 40(17): 2395-2400.
[13]	Qingdong YAO,Chengbing ZHANG,Jun FU,Peng WANG,Bin LONG,Haifeng. LIU. The correlation analysis of coronary artery plaque AI quantitative parameter with FFR-CT in coronary CT angiography [J]. The Journal of Practical Medicine, 2024, 40(17): 2489-2494.
[14]	Zhu LI,Yan WANG,Wenjing ZHOU,Haiying. WANG. Advances in the role of mitochondrial respiratory chain enzyme complexes in myocardial ischemia-reperfusion injury [J]. The Journal of Practical Medicine, 2024, 40(15): 2172-2176.
[15]	Juan DU,Xiaoyu ZHOU,Miao ZHANG,Xueling ZHANG,Wenya. LAN. Impact of mechanical thrombectomy using Solitaire stent combined with dual antiplatelet therapy on limb function and vascular reocclusion in patients with acute cerebral infarction [J]. The Journal of Practical Medicine, 2024, 40(14): 1952-1956.

Clinical significance of CT perfusion imaging combined with artificial intelligence in evaluating reperfusion injury after cerebral infarction

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 25

Related Articles 15

Recommended Articles

Metrics

Comments

指标	最佳截断点	灵敏度/%	特异度/%	AUC	95%CI
CBF/［mL/（100g·min）］	13.16	67.74	70.67	0.665	0.561 ~ 0.768
CBV/（mL/100 g）	1.14	70.97	74.67	0.667	0.555 ~ 0.778
平均CT值	9.95	77.42	77.33	0.744	0.639 ~ 0.849
联合	-	87.10	90.67	0.908	0.845 ~ 0.971