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Huang P, Zhou Y, Ruan F, Sun J, Shen J, Chen H. Optimization of Extraction of Four Components from Radix Scrophulariae with Natural Deep Eutectic Solvents and Evaluation of Extract's Antioxidant Activity. J Chromatogr Sci 2024:bmae037. [PMID: 38851208 DOI: 10.1093/chromsci/bmae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/08/2024] [Indexed: 06/10/2024]
Abstract
In this research, eight natural deep eutectic solvents (NaDESs) consisting of food-grade ingredients were screened for the extraction of four bioactive compounds (acteoside, cinnamic acid, angoroside C and harpagoside) from radix scrophulariae (RS). Among these NaDESs, Proline-Glycerol NaDES with higher comprehensive score was selected. The Criteria Importance Through Intercriteria Correlation (CRITIC) was applied to calculate the information entropy and the weight of indexes, and figured out a comprehensive score. The weights of acteoside, cinnamic acid, angoroside C and harpagoside were 0.369, 0.172, 0.241 and 0.218, respectively. Response surface methodology (RSM) mathematical model was used to optimize the extraction parameters. The optimal extraction parameters were as follows: extraction time with 42.21 min, NaDES concentration with 52.89%, solid-to-liquid ratio with 1 : 37.05 g/mL and the predicted value of comprehensive score was 0.885. Under the optimal condition, the comprehensive score was 0.903 ± 0.005. Finally, the antioxidant activity experiment revealed that the 1,1-Diphenyl-2-picrylhydrazyl · radical scavenging activity and hydroxyl radical scavenging activity of the extract at 2.0 mg/mL and 1.5 mg/mL were approximately equal to those of ascorbic acid, respectively. The results showed that the extraction condition optimized by RSM combined with CRITIC was reasonable and dependable, and the extract of radix scrophulariae exhibited good antioxidant activity.
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Affiliation(s)
- Ping Huang
- Clinical Pharmacy Department, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 453 Tiyu Road, Xihu District, Hangzhou 31007, China
| | - Yanxia Zhou
- College of Pharmacy, Jiangxi Medical College, 399 Zhimin Avenue, Xinzhou District, Shangrao 334000, China
| | - Fei Ruan
- Clinical Pharmacy Department, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 453 Tiyu Road, Xihu District, Hangzhou 31007, China
| | - Jianyu Sun
- Clinical Pharmacy Department, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 453 Tiyu Road, Xihu District, Hangzhou 31007, China
| | - Jinglin Shen
- College of Pharmacy, Jiangxi Medical College, 399 Zhimin Avenue, Xinzhou District, Shangrao 334000, China
| | - Hongmei Chen
- Clinical Pharmacy Department, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 453 Tiyu Road, Xihu District, Hangzhou 31007, China
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Li X, Cheng Y, Han X, Cui B, Li J, Yang H, Xu G, Lin Q, Xiao X, Tang J, Lu J. Exploring the association of glioma tumor residuals from incongruent [ 18F]FET PET/MR imaging with tumor proliferation using a multiparametric MRI radiomics nomogram. Eur J Nucl Med Mol Imaging 2024; 51:779-796. [PMID: 37864593 DOI: 10.1007/s00259-023-06468-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/28/2023] [Indexed: 10/23/2023]
Abstract
PURPOSE The study aimed to using multiparametric MRI radiomics to predict glioma tumor residuals (TRFET over MR) derived from incongruent [18F]fluoroethyl-L-tyrosine ([18F]FET) PET/MR imaging. METHODS One hundred ten patients with gliomas who underwent [18F]FET PET/MR scanning were retrospectively analyzed. The TRFET over MR was identified by the discrepancy-PET that the extent of resection (EOR) based on MRI subtracted the biological tumor volume on PET images. The MRI parameters and radiomics features were extracted based on EOR and selected by the least absolute shrinkage and selection operator to construct radiomics score (Rad-score). The correlation network analysis of all features was analyzed by Spearman's correlation tests. The methods for evaluating the clinical usefulness consisted of the receiver operating characteristic curve, the calibration curve, and decision curve analysis. RESULTS The Rad-score of the patients with the TRFET over MR was significantly higher than those with the non TRFET over MR (p < 0.001). The Rad-score was significantly correlated with the discrepancy-PET (r = 0.72, p < 0.001), Ki-67 level (r = 0.76, p < 0.001), and epidermal growth factor receptor (EGFR) of gliomas (r = 0.75, p < 0.001), respectively. Moreover, there was a difference of the correlation network analysis between the TRPET over MR group and non TRFET over MR group. The nomogram combing Rad-score and clinical features had the greatest performance in predicting TRFET over MR (AUC = 0.90/0.87, training/testing). There was a significant difference in prognosis (median OS, 17 m vs. 43 m) between patients with TRFET over MR and non TRFET over MR based on nomogram prediction (p < 0.001). CONCLUSION The nomogram based on MRI radiomics would predict gliomas tumor residuals caused by the absence of 18F-PET PET examination and adjust EOR to improve prognosis.
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Affiliation(s)
- Xiaoran Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Ye Cheng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xin Han
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Bixiao Cui
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Jing Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Hongwei Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Geng Xu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Qingtang Lin
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xinru Xiao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Tang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China.
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Chen L, Chen R, Li T, Huang L, Tang C, Li Y, Zeng Z. MRI radiomics model for predicting TERT promoter mutation status in glioblastoma. Brain Behav 2023; 13:e3324. [PMID: 38054695 PMCID: PMC10726789 DOI: 10.1002/brb3.3324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/05/2023] [Accepted: 10/31/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND AND PURPOSE The presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients. METHODS A total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10-fold cross validation was applied to the training dataset. RESULTS A model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations. CONCLUSIONS Radiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.
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Affiliation(s)
- Ling Chen
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
- Department of RadiologyLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Runrong Chen
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
| | - Tao Li
- Department of RadiologyLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Lizhao Huang
- Department of RadiologyLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Chuyun Tang
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
| | - Yao Li
- Department of NeurosurgeryLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Zisan Zeng
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
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Martucci M, Russo R, Schimperna F, D’Apolito G, Panfili M, Grimaldi A, Perna A, Ferranti AM, Varcasia G, Giordano C, Gaudino S. Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives. Biomedicines 2023; 11:biomedicines11020364. [PMID: 36830900 PMCID: PMC9953338 DOI: 10.3390/biomedicines11020364] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/20/2023] [Accepted: 01/22/2023] [Indexed: 01/28/2023] Open
Abstract
MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.
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Affiliation(s)
- Matia Martucci
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
- Correspondence:
| | - Rosellina Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | | | - Gabriella D’Apolito
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | - Marco Panfili
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | - Alessandro Grimaldi
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Alessandro Perna
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | | | - Giuseppe Varcasia
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Carolina Giordano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | - Simona Gaudino
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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Morelli L, Palombo M, Buizza G, Riva G, Pella A, Fontana G, Imparato S, Iannalfi A, Orlandi E, Paganelli C, Baroni G. Microstructural parameters from DW-MRI for tumour characterization and local recurrence prediction in particle therapy of skull-base chordoma. Med Phys 2023; 50:2900-2913. [PMID: 36602230 DOI: 10.1002/mp.16202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/21/2022] [Accepted: 12/15/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Quantitative imaging such as Diffusion-Weighted MRI (DW-MRI) can be exploited to non-invasively derive patient-specific tumor microstructure information for tumor characterization and local recurrence risk prediction in radiotherapy. PURPOSE To characterize tumor microstructure according to proliferative capacity and predict local recurrence through microstructural markers derived from pre-treatment conventional DW-MRI, in skull-base chordoma (SBC) patients treated with proton (PT) and carbon ion (CIRT) radiotherapy. METHODS Forty-eight patients affected by SBC, who underwent conventional DW-MRI before treatment and were enrolled for CIRT (n = 25) or PT (n = 23), were retrospectively selected. Clinically verified local recurrence information (LR) and histological information (Ki-67, proliferation index) were collected. Apparent diffusion coefficient (ADC) maps were calculated from pre-treatment DW-MRI and, from these, a set of microstructural parameters (cellular radius R, volume fraction vf, diffusion D) were derived by applying a fine-tuning procedure to a framework employing Monte Carlo simulations on synthetic cell substrates. In addition, apparent cellularity (ρapp ) was estimated from vf and R for an easier clinical interpretation. Histogram-based metrics (mean, median, variance, entropy) from estimated parameters were considered to investigate differences (Mann-Whitney U-test, α = 0.05) in estimated tumor microstructure in SBCs characterized by low or high cell proliferation (Ki-67). Recurrence-free survival analyses were also performed to assess the ability of the microstructural parameters to stratify patients according to the risk of local recurrence (Kaplan-Meier curves, log-rank test α = 0.05). RESULTS Refined microstructural markers revealed optimal capabilities in discriminating patients according to cell proliferation, achieving best results with mean values (p-values were 0.0383, 0.0284, 0.0284, 0.0468, and 0.0088 for ADC, R, vf, D, and ρapp, respectively). Recurrence-free survival analyses showed significant differences between populations at high and low risk of local recurrence as stratified by entropy values of estimated microstructural parameters (p = 0.0110). CONCLUSION Patient-specific microstructural information was non-invasively derived providing potentially useful tools for SBC treatment personalization and optimization in particle therapy.
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Affiliation(s)
- Letizia Morelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Giulia Riva
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Andrea Pella
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Giulia Fontana
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Sara Imparato
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Alberto Iannalfi
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Ester Orlandi
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
- National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
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Wang J, Zhang H, Dang X, Rui W, Cheng H, Wang J, Zhang Y, Qiu T, Yao Z, Liu H, Pang H, Ren Y. Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study. Front Oncol 2023; 13:1104610. [PMID: 37182187 PMCID: PMC10171458 DOI: 10.3389/fonc.2023.1104610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 04/13/2023] [Indexed: 05/16/2023] Open
Abstract
Background To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas. Methods Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC1500, α1500 fitted by 15 b-values (0-1,500 sec/mm2)and DDC5000 and α5000 fitted by 22 b-values (0-5,000 sec/mm2) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters. Results MDWI-derived α1500 negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC1500 and DDC5000 negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α1500(r=-0.485; P=0.005) and α5000(r=-0.395; P=0.025). Conclusions SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.
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Affiliation(s)
- Junlong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hua Zhang
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Xuefei Dang
- Department of Oncology, Minhang Branch of Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wenting Rui
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Haixia Cheng
- Department of Neuropathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yong Zhang
- Department of Magnetic Resonance Research, General Electric Healthcare, Shanghai, China
| | - Tianming Qiu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hanqiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
| | - Haopeng Pang
- Minimally Invasive Therapy Center, Shanghai Cancer Center, Fudan University, Shanghai, China
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
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Ma Z, Huo M, Xie J, Liu G, Li G, Liu Q, Mao L, Huang W, Liu B, Liu X. Wall characteristics of atherosclerotic middle cerebral arteries in patients with single or multiple infarcts: A high-resolution MRI Study. Front Neurol 2022; 13:934926. [DOI: 10.3389/fneur.2022.934926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Background and purposeUnderstanding the stroke mechanism of middle cerebral artery (MCA) atherosclerosis may inform secondary prevention. The aim of this study was to explore the relationship between vascular wall characteristics and infarction patterns using high-resolution magnetic resonance imaging (HRMRI) and diffusion-weighted imaging (DWI).MethodsFrom November 2018 to March 2021, patients with acute ischemic stroke due to MCA atherosclerotic disease were retrospectively analyzed. The wall characteristics of atherosclerotic MCA, including conventional characteristics and histogram-defined characteristics, were evaluated using HRMRI. Patients were divided into single-infarction and multiple-infarction groups based on DWI, and wall characteristics were compared between the two groups.ResultsOf 92 patients with MCA plaques, 59 patients (64.1%) had multiple infarcts, and 33 (35.9%) had single infarcts. The histogram-defined characteristics showed no differences between the single-infarction and multiple-infarction groups (P>0.05). Plaque burden, degree of stenosis, and prevalence of intraplaque hemorrhage (IPH) were significantly greater in the multiple-infarction group than in the single-infarction group (plaque burden: P = 0.001; degree of stenosis: P = 0.010; IPH: P = 0.019). Multivariate analysis showed that plaque burden (odds ratio: 1.136; 95% confidence interval: 1.054–1.224, P = 0.001) and IPH (odds ratio: 5.248; 95% confidence interval: 1.573–17.512, P = 0.007) were independent predictors for multiple infarction.ConclusionIPH and plaque burden are independently associated with multiple infarcts. HRMRI may provide new insight into the mechanisms underlying the different MCA infarction patterns.
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