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Li Y, Zhang H, Sun Y, Fan Q, Wang L, Ji C, HuiGu, Chen B, Zhao S, Wang D, Yu P, Li J, Yang S, Zhang C, Wang X. Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study. Int J Med Inform 2024; 188:105487. [PMID: 38761459 DOI: 10.1016/j.ijmedinf.2024.105487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/06/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). METHOD This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. RESULTS 510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter<3mm), small (3 mm ≤ diameter<5mm), medium (5 mm ≤ diameter<7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. CONCLUSIONS The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.
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Affiliation(s)
- Yuanyuan Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Huiling Zhang
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Yun Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Qianrui Fan
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Long Wang
- Department of Cardiovascular Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Congshan Ji
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - HuiGu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Baojin Chen
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Shuo Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Dawei Wang
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Pengxin Yu
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Junchen Li
- Department of Radiology, Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China.
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China.
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Ma Y, Deng X, Chen Z, Yuan Y, Guan S, Guo X. Safety and efficacy analysis of the off-label use of pipeline embolization devices for intracranial aneurysms: a propensity score matching study. Front Neurol 2024; 14:1278366. [PMID: 38239324 PMCID: PMC10794508 DOI: 10.3389/fneur.2023.1278366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/05/2023] [Indexed: 01/22/2024] Open
Abstract
Background and objective The safety and efficacy of on-label use of pipeline embolization devices (PEDs) are well established; however, there is much controversy over their off-label use. This study aimed to investigate the safety and efficacy of the off-label use of PEDs for treating intracranial aneurysms. Methods This single-center study retrospectively included patients with digital subtraction angiography, computed tomographic angiography, or magnetic resonance angiography confirmed intracranial aneurysms treated with PEDs who were admitted to our institution between 1 January 2018 and 1 July 2022. Patients were divided into on- and off-label groups according to the Food and Drug Administration criteria published in 2021. Propensity score matching (PSM) was used to balance disparities in baseline information between the two groups. Safety outcomes included postoperative mortality and complication rates, whereas effectiveness outcomes included aneurysm occlusion rate (O'Kelly-Marotta grading system C + D grades), retreatment rate within 12 months, and postoperative functional score [modified Rankin scale (mRS) score]. The study was approved by the Ethics Committee of Scientific Research and Clinical Trial of the First Affiliated Hospital of Zhengzhou University (Ethics number: KY 2018-098-02). All patients provided informed consent. Results A total of 242 patients with 261 aneurysms (160 on-label and 101 off-label aneurysms) were included in this study. PSM yielded 81 pairs of patients matched for baseline information. Postoperative hemorrhagic, ischemic, and procedure-related complication rates did not reach statistical significance. In addition, no statistically significant differences in the aneurysm occlusion rate, retreatment rate within 12 months, postoperative functional score (mRS score), or mRS score deterioration rate were observed between the two groups. A higher incidence of in-stent stenosis was observed in the off-label (4.9% vs. 21%, p = 0.002) group than in the on-label group; however, all patients were asymptomatic. Conclusion Compared with on-label use, off-label use of PEDs for treating intracranial aneurysms did not increase the risk of complications, and the occlusion rates were comparable. Therefore, decisions regarding clinical management should not rely solely on on- or off-label indications.
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Affiliation(s)
- Yajing Ma
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Neurointervention Engineering Research Center of Henan Province, Zhengzhou, Henan, China
| | - Xin Deng
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Neurointervention Engineering Research Center of Henan Province, Zhengzhou, Henan, China
| | - Zhen Chen
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Neurointervention Engineering Research Center of Henan Province, Zhengzhou, Henan, China
| | - Yongjie Yuan
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Neurointervention Engineering Research Center of Henan Province, Zhengzhou, Henan, China
| | - Sheng Guan
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Neurointervention Engineering Research Center of Henan Province, Zhengzhou, Henan, China
| | - Xinbin Guo
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Neurointervention Engineering Research Center of Henan Province, Zhengzhou, Henan, China
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Nariai Y, Takigawa T, Kawamura Y, Hyodo A, Suzuki K. Inflow Angle and Height-Width Ratio are Predictors of Incomplete Occlusion at One and Two Years After Flow Diverter Treatment for Small- and Medium-Sized Internal Carotid Artery Aneurysms. World Neurosurg 2023; 180:e716-e728. [PMID: 37821031 DOI: 10.1016/j.wneu.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVE We investigated the association between the inflow angle of aneurysms and their occlusion status at 1 and 2 years after flow diverter (FD) treatment. METHODS We retrospectively analyzed 42 consecutive patients from a single center with 43 untreated, unruptured internal carotid artery (ICA) proximal to communicating segment, saccular aneurysms sized <12 mm. RESULTS At 1 year posttreatment, the complete occlusion (CO) rate was 58.1%. On univariate analyses, the proportion of inflow angle >90° was significantly lower in the CO group than in the incomplete occlusion group (20.0% VS. 83.3%; P < 0.001). The CO incidence decreased with a height-width (H/W) ratio of <1.2 (P = 0.059). On multivariate analysis, an H/W ratio of <1.2 (odds ratio [OR], 0.076; P = 0.027) and an inflow angle of >90° (OR, 0.020; P = 0.0011) significantly influenced CO at 1 year post FD. At 2 years posttreatment, the CO rate was 76.3% (29/38 cases with available follow-up data). On univariate analyses, in the CO group compared to the incomplete occlusion group, the proportion of H/W ratio <1.2 was significantly lower (P = 0.005) and the proportion of inflow angle >90° was significantly lower (P = 0.021); aneurysm dome size tended to be larger (8.5 mm vs. 7.1 mm; P = 0.080). On multivariate analysis, an H/W ratio <1.2 (OR, 0.042; P = 0.015) and an inflow angle >90° (OR: 0.088; P = 0.031) significantly influenced CO at 2 years post FD. CONCLUSIONS The inflow angle of >90° and H/W ratio <1.2 may significantly influence the CO rate in small- or medium-sized internal carotid artery aneurysms 1 and 2 years post FD.
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Affiliation(s)
- Yasuhiko Nariai
- Department of Neurosurgery, Dokkyo Medical University Saitama Medical Center, Saitama, Japan.
| | - Tomoji Takigawa
- Department of Neurosurgery, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
| | - Yosuke Kawamura
- Department of Neurosurgery, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
| | - Akio Hyodo
- Department of Neurosurgery, Kamagaya General Hospital, Chiba, Japan
| | - Kensuke Suzuki
- Department of Neurosurgery, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
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Zhang Y, Zhang F, Turhon M, Huang J, Li M, Peng Q, Zheng Z, Liu J, Zhang Y, Liu J, Zhang H, Li T, Song D, Zhao Y, Aisha M, Wang Y, Feng W, Wang Y, Wan J, Mao G, Shi H, Guan S. Treatment of Intracranial Vertebral Artery Dissecting Aneurysms Using Pipeline Embolization Devices : A Multicenter Cohort Study. Clin Neuroradiol 2023; 33:1105-1114. [PMID: 37380901 DOI: 10.1007/s00062-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/24/2023] [Indexed: 06/30/2023]
Abstract
PURPOSE Intracranial vertebral artery dissecting aneurysm (IVADA) is a rare type of aneurysm with high morbidity and mortality. Recently, the application of pipeline embolization devices (PEDs) has been extended to IVADAs. Here, we aim to investigate the safety and effectiveness of PEDs for IVADAs. METHOD We retrospectively reviewed the PLUS database to identify patients who had IVADAs and were treated with PEDs from 2014 to 2019 at 14 centers across China. Data including patient and aneurysm characteristics, procedure details, angiographic and clinical results, relationship with the ipsilateral posterior inferior cerebellar artery (PICA), and patency of the PICA following PED coverage were analyzed. RESULTS In this study 52 consecutive patients with 52 IVADAs were included. The mean age was 52.33 years and 82.7% were male. With a median follow-up of 10.5 months, the complete occlusion rate was 93.8% (45/48) and no recurrence or in-stent stenosis was detected. The total postoperative complication rate and mortality were 11.5% and 1.9%, respectively. Complications occurred in 9.6% (5/52) of patients within 30 days after the operation, including ischemic stroke in 3 and hemorrhagic stroke in 2. Another patient suffered an ischemic stroke at follow-up, 78.8% (41/52) PICAs were covered by PEDs, 1 case (2.4%) had a functional disability due to PICA occlusion, while 39.0% (16/41) had reduced flow during follow-up but hardly caused any obvious neurological deficits. Patients with IVADA involving PICA had a trend towards more complications (66.7% vs. 51.1%; P = 1). CONCLUSION Treating IVADAs with PEDs may be a safe and effective option, with favorable clinical and angiographic outcomes; however, complications associated with this treatment should not be ignored. REGISTRATION http://www. CLINICALTRIALS gov . Unique identifier: NCT03831672.
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Affiliation(s)
- Ying Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Fujunhui Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mirzat Turhon
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiliang Huang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mengxing Li
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qichen Peng
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhaoxu Zheng
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jian Liu
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yisen Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianmin Liu
- Changhai Hospital Affiliated to Naval Medical University, Shanghai, China
| | - Hongqi Zhang
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tianxiao Li
- Zhengzhou University People's Hospital, Zhengzhou, China
| | - Donglei Song
- Shanghai Donglei Brain Hospital, Shanghai, China
| | - Yuanli Zhao
- Peking University International Hospital, Beijing, China
| | - Maimaitili Aisha
- First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Yunyan Wang
- Qilu Hospital of Shandong University, Jinan, China
| | - Wenfeng Feng
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yang Wang
- Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Jieqing Wan
- Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guohua Mao
- Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Huaizhang Shi
- First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Sheng Guan
- First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Guo ZY, Zhong ZA, Peng P, Liu Y, Chen F. A scoring system categorizing risk factors to evaluate the need for ventriculoperitoneal shunt in pediatric patients after brain tumor resection. Front Oncol 2023; 13:1248553. [PMID: 37916175 PMCID: PMC10616891 DOI: 10.3389/fonc.2023.1248553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/02/2023] [Indexed: 11/03/2023] Open
Abstract
Objectives To develop a scoring system based on independent predictors of the need for ventriculoperitoneal (VP) shunt after brain tumor resection in pediatric patients. Methods A total of 416 pediatric patients (≤ 14 years old) with brain tumors who underwent surgery were randomly assigned to the training (n = 333) and validation cohorts (n = 83). Based on the implementation of VP shunt, the training cohort was divided into the VP shunt group (n = 35) and the non-VP shunt group (n = 298). Univariate and multivariate logistic analyses were performed. A scoring system was developed based on clinical characteristics and operative data, and scores and corresponding risks were calculated. Results Age < 3 (p = 0.010, odds ratio [OR] = 3.162), blood loss (BL) (p = 0.005, OR = 1.300), midline tumor location (p < 0.001, OR = 5.750), preoperative hydrocephalus (p = 0.001, OR = 7.044), and total resection (p = 0.025, OR = 0.284) were identified as independent predictors. The area under the curve (AUC) of the scoring system was higher than those of age < 3, BL, midline tumor location, preoperative hydrocephalus, and total resection (0.859 vs. 0.598, 0.717, 0.725, 0.705, and 0.555, respectively; p < 0.001). Furthermore, the scoring system showed good performance in the validation cohort (AUC = 0.971). The cutoff value for predictive scores was 5.5 points, which categorized patients into low risk (0-5 points) and high risk (6-14 points) groups. Conclusions Our scoring system, integrating age < 3, BL, midline tumor location, preoperative hydrocephalus, and total resection, provides a practical evaluation. Scores ranging from 6 to 14 points indicate high risk.
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Affiliation(s)
- Zhong-Yin Guo
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zi-An Zhong
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Peng Peng
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yang Liu
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Feng Chen
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
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Chen Y, Chen P, Duan G, Li R, Li Z, Guo G. Extracranial-intracranial bypass surgery for intracranial aneurysm of the anterior cerebral circulation: A systematic review and meta-analysis. Front Neurol 2023; 14:1174088. [PMID: 37064185 PMCID: PMC10102499 DOI: 10.3389/fneur.2023.1174088] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
BackgroundThe safety of extracranial–intracranial (EC–IC) bypass in the management of anterior circulation intracranial aneurysms (IAs) remains to be determined. This systematic review aims to summarize the existing evidence and provide guidance for the precise management of IAs.Data sourceWe constructed search strategies and comprehensively searched Pubmed, Medline, Embase, Web of science, and Cochrane library.MethodsThis systematic review was actualized according to the PRISMA statement. We evaluated study quality using the methodological index for non-randomized study (MINORS). Effect sizes were pooled using a random-effects model. Heterogeneity between studies was assessed using the I2 test. Publication bias was assessed using the Egger's test. The registration number for this systematic review is CRD42023396730.ResultThis systematic review included a total of 21 articles, involving 915 patients. Postoperative bypass patency rate was 99% (95% CI 0.98–1.00); short-term follow-up was 98% (95% CI 0.94–1.00); long-term follow-up was 95% (95% CI 0.93–0.97). The long-term follow-up occlusion rate of saphenous vein was higher than that of radial artery (OR 6.10 95% CI 1.04–35.59). Short-term surgery-related mortality was 0.3% (95% CI 0.000–0.012); long-term follow-up was 0.4% (95% CI 0.000–0.013); The proportion of patients with a score of 0–2 on the modified Rankin Scale (mRS) during long-term follow-up was 92% (95% CI 0.86–0.98). The incidence rates of long-term follow-up complications were: ischemic 3% (95% CI 0.01–0.06); hemorrhagic 1% (95% CI 0.00–0.03); neurological deficit 1% (95% CI 0.00–0.03); other 3% (95% CI 0.01–0.06).LimitationMost of the included studies were retrospective studies. Studies reporting preoperative status were not sufficient to demonstrate postoperative improvement. Lack of sufficient subgroup information such as aneurysm rupture status.ConclusionEC–IC therapy for anterior circulation IAs has a high safety profile. Higher level of evidence is still needed to support clinical decision.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023396730, identifier: CRD42023396730.
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Affiliation(s)
- Yang Chen
- Department of Neurosurgery, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Pengyu Chen
- Department of Neurosurgery, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Guosheng Duan
- Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, China
| | - Ren Li
- Department of Neurosurgery, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ziao Li
- Department of Neurosurgery, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Geng Guo
- Department of Emergency, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- *Correspondence: Geng Guo
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Turhon M, Kang H, Liu J, Zhang Y, Zhang Y, Huang J, Wang K, Li M, Liu J, Zhang H, Li T, Song D, Zhao Y, Luo B, Maimaiti A, Aisha M, Wang Y, Feng W, Wang Y, Wan J, Mao G, Shi H, Yang X, Guan S. In-Stent Stenosis After Pipeline Embolization Device in Intracranial Aneurysms: Incidence, Predictors, and Clinical Outcomes. Neurosurgery 2022; 91:943-951. [PMID: 36129281 DOI: 10.1227/neu.0000000000002142] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/04/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In-stent stenosis (ISS) is a delayed complication that can occur after pipeline embolization device use when treating intracranial aneurysms (IAs). OBJECTIVE To assess the incidence, predictors, and outcomes of ISS. METHODS This was a retrospective, multicenter, observational study. All patient data were collected from a PLUS registry study. We collected data from patients with IA who completed digital subtraction angiography at follow-up and divided patients into "non-ISS," "mild ISS," or "severe ISS" groups. Multivariate logistic regression analysis was conducted to determine predictors of ISS. RESULTS A total of 1171 consecutive patients with 1322 IAs participated in this study. Angiographic follow-up was available for 662 patients with 728 IAs, and the mean follow-up time was 9 months. ISS was detected in 73 cases (10.03%), including 61 mild ISS cases and 12 severe ISS cases. Univariate and multivariable analysis demonstrated that current smoking history (mild ISS: OR 2.15, 95% CI 1.122-4.118, P = .021; severe ISS: OR 5.858, 95% CI 1.186-28.93, P = .030) and cerebral atherosclerosis (mild ISS: OR 5.694, 95% CI 3.193-10.15, P = .001; severe ISS: OR 6.103, 95% CI 1.384-26.91, P = .017) were independent predictors of ISS. Compared with the other groups, the severe ISS group had higher rate of ischemic stroke (33.3%). CONCLUSION ISS occurs in approximately 10.03% of cases at a mean follow-up of 9 months. Statistically, current smoking history and cerebral atherosclerosis are the main predictors of ISS. Severe ISS may be associated with higher risk of neurological ischemic events in patients with IA after pipeline embolization device implantation.
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Affiliation(s)
- Mirzat Turhon
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Huibin Kang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jian Liu
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yisen Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Ying Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jiliang Huang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Kun Wang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Mengxing Li
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jianmin Liu
- Department of Neurosurgery, Changhai Hospital, Shanghai, People's Republic of China
| | - Hongqi Zhang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Tianxiao Li
- Department of Neurosurgery, Zhengzhou University People's Hospital, Zhengzhou, People's Republic of China
| | - Donglei Song
- Department of Neurosurgery, Shanghai Donglei Brain Hospital, Shanghai, People's Republic of China
| | - Yuanli Zhao
- Department of Neurosurgery, Peking University International Hospital, Beijing, People's Republic of China
| | - Bin Luo
- Department of Neurosurgery, Peking University International Hospital, Beijing, People's Republic of China
| | - Aierpati Maimaiti
- Department of Neurosurgery, Xinjiang Medical University Affiliated First Hospital, Urumqi, People's Republic of China
| | - Maimaitili Aisha
- Department of Neurosurgery, Xinjiang Medical University Affiliated First Hospital, Urumqi, People's Republic of China
| | - Yunyan Wang
- Department of Neurosurgery, Qilu Hospital, Shandong University, Jinan, People's Republic of China
| | - Wenfeng Feng
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Yang Wang
- Department of Neurosurgery, First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.,Department of Neurosurgery, Beijing ChaoYang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jieqing Wan
- Department of Neurosurgery, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, People's Republic of China
| | - Guohua Mao
- Department of Neurosurgery, Nanchang University Second Affiliated Hospital, Nanchang, People's Republic of China
| | - Huaizhang Shi
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China
| | - Xinjian Yang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.,Department of Interventional Neuroradiology, Beijing TianTan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Sheng Guan
- Department of Intervention Neuroradiology, Zhengzhou University First Affiliated Hospital, Zhengzhou, People's Republic of China
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8
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Turhon M, Maimaiti A, Gheyret D, Axier A, Rexiati N, Kadeer K, Su R, Wang Z, Chen X, Cheng X, Zhang Y, Aisha M. An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning. Front Immunol 2022; 13:1001320. [PMID: 36248807 PMCID: PMC9556730 DOI: 10.3389/fimmu.2022.1001320] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Background Immunogenic Cell Death (ICD) is a novel way to regulate cell death and can sufficiently activate adaptive immune responses. Its role in immunity is still emerging. However, the involvement of ICD in Intracranial Aneurysms (IA) remains unclear. This study aimed to identify biomarkers associated with ICDs and determine the relationship between them and the immune microenvironment during the onset and progression of IA Methods The IA gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IA were identified and the effects of the ICD on immune microenvironment signatures were studied. Techniques like Lasso, Bayes, DT, FDA, GBM, NNET, RG, SVM, LR, and multivariate analysis were used to identify the ICD gene signatures in IA. A consensus clustering algorithm was used for conducting the unsupervised cluster analysis of the ICD patterns in IA. Furthermore, enrichment analysis was carried out for investigating the various immune responses and other functional pathways. Along with functional annotation, the weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network and module construction, identification of the hub gene, and co-expression analysis were also carried out. Results The above techniques were used for establishing the ICD gene signatures of HMGB1, HMGN1, IL33, BCL2, HSPA4, PANX1, TLR9, CLEC7A, and NLRP3 that could easily distinguish IA from normal samples. The unsupervised cluster analysis helped in identifying three ICD gene patterns in different datasets. Gene enrichment analysis revealed that the IA samples showed many differences in pathways such as the cytokine-cytokine receptor interaction, regulation of actin cytoskeleton, chemokine signaling pathway, NOD-like receptor signaling pathway, viral protein interaction with the cytokines and cytokine receptors, and a few other signaling pathways compared to normal samples. In addition, the three ICD modification modes showed obvious differences in their immune microenvironment and the biological function pathways. Eight ICD-regulators were identified and showed meaningful associations with IA, suggesting they could severe as potential prognostic biomarkers. Conclusions A new gene signature for IA based on ICD features was created. This signature shows that the ICD pattern and the immune microenvironment are closely related to IA and provide a basis for optimizing risk monitoring, clinical decision-making, and developing novel treatment strategies for patients with IA.
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Affiliation(s)
- Mirzat Turhon
- Department of Neurointerventional Surgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurointerventional Surgery, Beijing Tiantan hospital, Capital Medical University, Beijing, China
| | - Aierpati Maimaiti
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Dilmurat Gheyret
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Aximujiang Axier
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Nizamidingjiang Rexiati
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Kaheerman Kadeer
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Riqing Su
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Zengliang Wang
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiaohong Chen
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiaojiang Cheng
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- *Correspondence: Maimaitili Aisha, ; Yisen Zhang, ; Xiaojiang Cheng,
| | - Yisen Zhang
- Department of Neurointerventional Surgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurointerventional Surgery, Beijing Tiantan hospital, Capital Medical University, Beijing, China
- *Correspondence: Maimaitili Aisha, ; Yisen Zhang, ; Xiaojiang Cheng,
| | - Maimaitili Aisha
- Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- *Correspondence: Maimaitili Aisha, ; Yisen Zhang, ; Xiaojiang Cheng,
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9
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Peng F, Fu M, Xia J, Niu H, Liu L, Feng X, Xu P, Bai X, Li Z, Chen J, Tong X, He X, Xu B, Chen X, Liu H, Sui B, Duan Y, Li R, Liu A. Quantification of aneurysm wall enhancement in intracranial fusiform aneurysms and related predictors based on high-resolution magnetic resonance imaging: a validation study. Ther Adv Neurol Disord 2022; 15:17562864221105342. [PMID: 35847373 PMCID: PMC9280813 DOI: 10.1177/17562864221105342] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/18/2022] [Indexed: 11/18/2022] Open
Abstract
Background: Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) has emerged as a new imaging biomarker of intracranial aneurysm instability. Objective: To determine a standard method of AWE quantification for predicting fusiform intracranial aneurysms (FIAs) stability by comparing the sensitivity of each parameter in identifying symptomatic FIAs. The predictors of AWE and FIA types were also identified. Methods: We retrospectively analyzed consecutive fusiform aneurysm patients who underwent HR-MRI from two centers. The aneurysm-to-pituitary stalk contrast ratio (CRstalk), aneurysm enhancement ratio, and aneurysm enhancement index were extracted, and their sensitivities in discriminating aneurysm symptoms were compared using the receiver-operating characteristic curve. Morphological parameters of fusiform aneurysm were extracted based on 3D vessel model. Uni- and multivariate analyses of related predictors for AWE, CRstalk, and FIA types were performed, respectively. Results: Overall, 117 patients (mean age, 53.3 ± 11.7 years; male, 75.2%) with 117 FIAs underwent HR-MRI were included. CRstalk with the maximum signal intensity (CRstalk-max) had the highest sensitivity in identifying symptomatic FIAs with an area under the curve value (0.697) and a cut-off value of 0.90. The independent predictors of AWE were aneurysm symptoms [(odds ratio) OR = 3.754, p = 0.003], aspirin use (OR = 0.248, p = 0.037), and the maximum diameter of the cross-section (OR = 1.171, p = 0.043). The independent predictors of CRstalk-max were aneurysm symptoms (OR = 1.289, p = 0.003) and posterior circulation aneurysm (OR = 1.314, p = 0.001). Transitional-type showed higher rates of hypertension and mural thrombus over both dolichoectatic- and fusiform-type FIAs. Conclusion: CRstalk-max may be the most reliable parameter to quantify AWE to distinguish symptomatic FIAs. It also has the potential to identify unstable FIAs. Several factors contribute to the complex pathophysiology of FIAs and need further validation in a larger cohort.
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Affiliation(s)
- Fei Peng
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Mingzhu Fu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Jiaxiang Xia
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Hao Niu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lang Liu
- Department of Neurosurgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xin Feng
- Neurosurgery Center, Department of Cerebrovascular Surgery, Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Xu
- Department of Neurosurgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Xiaoyan Bai
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhiye Li
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jigang Chen
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xin Tong
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiaoxin He
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Boya Xu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xuge Chen
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Hongyi Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Binbin Sui
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yonghong Duan
- Department of Neurosurgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Rui Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Aihua Liu
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, No. 119, South 4th Ring West Road, Fengtai District, Beijing 100070, China
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