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Lv K, Zhang C, Liu B, Yang A, Luan J, Hu P, Yao Z, Liu J, Ma G. White matter structural changes before and after microvascular decompression for hemifacial spasm. Brain Struct Funct 2024; 229:959-970. [PMID: 38502329 DOI: 10.1007/s00429-023-02741-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 11/22/2023] [Indexed: 03/21/2024] [Imported: 03/21/2024]
Abstract
Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction. This enables us to model the human brain as a complex network using graph theory. In our study, we recruited 32 patients with HFS and 32 healthy controls to analyze and compare the topological organization of whole-brain white matter networks between the groups. We also explored the potential relationships between altered topological properties and clinical outcomes. Compared to the HC group, the white matter network was disrupted in both preoperative and postoperative groups of HFS patients, mainly located in the somatomotor network, limbic network, and default network (All P < 0.05, FDR corrected). There was no significant difference between the preoperative and postoperative groups (P > 0.05, FDR corrected). There was a correlation between the altered topological properties and clinical outcomes in the postoperative group of patients (All P < 0.05, FDR corrected). Our findings indicate that in HFS, the white matter structural network was disrupted before and after MVD, and that these alterations in the postoperative group were correlated with the clinical outcomes. White matter alteration here described may subserve as potential biomarkers for HFS and may help us identify patients with HFS who can benefit from MVD and thus can help us make a proper surgical patient selection.
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Affiliation(s)
- Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Chuanpeng Zhang
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
- Department of Neurosurgery, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Zeshan Yao
- Jingjinji National Center of Technology Innovation, Beijing, China
| | - Jiang Liu
- Department of Neurosurgery, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China.
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China.
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
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Chen H, Yang A, Huang W, Du L, Liu B, Lv K, Luan J, Hu P, Shmuel A, Shu N, Ma G. Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study. Neuroimage 2024; 290:120555. [PMID: 38447683 DOI: 10.1016/j.neuroimage.2024.120555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/07/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024] [Imported: 03/23/2024] Open
Abstract
Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.
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Affiliation(s)
- Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Lei Du
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Neurology and Neurosurgery, Physiology, and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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Luan J, Zhang D, Liu B, Yang A, Lv K, Hu P, Yu H, Shmuel A, Zhang C, Ma G. Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme. J Transl Med 2024; 22:107. [PMID: 38279111 PMCID: PMC10821572 DOI: 10.1186/s12967-023-04823-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 12/22/2023] [Indexed: 01/28/2024] [Imported: 03/23/2024] Open
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients. METHODS We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model. RESULTS We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination. CONCLUSIONS We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.
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Affiliation(s)
- Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Di Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China.
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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Guo H, Jian S, Zhou Y, Chen X, Chen J, Zhou J, Huang Y, Ma G, Li X, Ning Y, Wu F, Wu K. Discriminative analysis of schizophrenia patients using an integrated model combining 3D CNN with 2D CNN: A multimodal MR image and connectomics analysis. Brain Res Bull 2024; 206:110846. [PMID: 38104672 DOI: 10.1016/j.brainresbull.2023.110846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 11/20/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023] [Imported: 03/23/2024]
Abstract
OBJECTIVE Few studies have applied deep learning to the discriminative analysis of schizophrenia (SZ) patients using the fusional features of multimodal MRI data. Here, we proposed an integrated model combining a 3D convolutional neural network (CNN) with a 2D CNN to classify SZ patients. METHOD Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data were acquired for 140 SZ patients and 205 normal controls. We computed structural connectivity (SC) from the sMRI data as well as functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) from the rs-fMRI data. The 3D images of T1, ReHo, and ALFF were used as the inputs for the 3D CNN model, while the SC and FC matrices were used as the inputs for the 2D CNN model. Moreover, we added squeeze and excitation blocks (SE-blocks) to each layer of the integrated model and used a support vector machine (SVM) to replace the softmax classifier. RESULTS The integrated model proposed in this study, using the fusional features of the T1 images, and the matrices of FC, showed the best performance. The use of the SE-blocks and SVM classifiers significantly improved the performance of the integrated model, in which the accuracy, sensitivity, specificity, area under the curve, and F1-score were 89.86%, 86.21%, 92.50%, 89.35%, and 87.72%, respectively. CONCLUSIONS Our findings indicated that an integrated model combining 3D CNN with 2D CNN is a promising method to improve the classification performance of SZ patients and has potential for the clinical diagnosis of psychiatric diseases.
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Affiliation(s)
- Haiman Guo
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Shuyi Jian
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Yubin Zhou
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Xiaoyi Chen
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Jinbiao Chen
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Jing Zhou
- School of Material Sciences and Engineering, South China University of Technology, Guangzhou 510610, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China
| | - Yuanyuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan.
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Zhang D, Luan J, Liu B, Yang A, Lv K, Hu P, Han X, Yu H, Shmuel A, Ma G, Zhang C. Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. Front Med (Lausanne) 2023; 10:1271687. [PMID: 38098850 PMCID: PMC10720716 DOI: 10.3389/fmed.2023.1271687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023] [Imported: 03/23/2024] Open
Abstract
Objective To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. Methods 131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index. Results After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560). Conclusion This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.
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Affiliation(s)
- Di Zhang
- Department of Radiology, Liaocheng People’s Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
| | - Jixin Luan
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Aocai Yang
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Kuan Lv
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pianpian Hu
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People’s Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
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Yang A, Zhuang H, Du L, Liu B, Lv K, Luan J, Hu P, Chen F, Wu K, Shu N, Shmuel A, Ma G, Wang Y. Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD. Neuroimage 2023; 282:120381. [PMID: 37734476 DOI: 10.1016/j.neuroimage.2023.120381] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 08/14/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023] [Imported: 03/23/2024] Open
Abstract
OBJECTIVE The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. METHODS Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. RESULTS Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score. CONCLUSION CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.
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Affiliation(s)
- Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Hangwei Zhuang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA; Department of Radiology, Weill Cornell Medical College, New York, New York 10065, USA
| | - Lei Du
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing 100142, PR China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, PR China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, PR China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, Hainan, PR China
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangdong 510006, Guangzhou, PR China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Neurology and Neurosurgery, Physiology, and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China.
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA; Department of Radiology, Weill Cornell Medical College, New York, New York 10065, USA
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Du W, Wang T, Hu S, Luan J, Tian F, Ma G, Xue J. Engineering of electrospun nanofiber scaffolds for repairing brain injury. Engineered Regeneration 2023; 4:289-303. [DOI: 10.1016/j.engreg.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/14/2023] [Imported: 08/14/2023] Open
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Li HM, Han XW, Sang CY, Sui Y, Ma GL. [Low-frequency fluctuation amplitude changes in resting-state brain functional magnetic resonance imaging and its correlation with clinical hearing levels in patients with unilateral hearing impairment]. Zhonghua Yi Xue Za Zhi 2023; 103:1911-1917. [PMID: 37402672 DOI: 10.3760/cma.j.cn112137-20221107-02337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/06/2023] [Imported: 08/14/2023]
Abstract
Objective: To investigate low-frequency fluctuation amplitude changes in resting-state brain fMRI and its correlation with clinical hearing levels in patients with clinical hearing level in patients with unilateral hearing impairment. Methods: Forty-five patients with unilateral hearing impairment[12 males and 33 females, aged 36-67 (46.0±9.7) years], and 31 controls with normal hearing[9 males and 22 females, aged 36-67 (46.0±10.1) years], were retrospectively included. All subjects underwent blood oxygen level-dependent (BOLD) resting-state functional magnetic resonance imaging and high-resolution T1-weighted imaging. The patients were divided into the left-sided hearing impaired group(24 cases), and the right-sided hearing impaired group(21 cases). After data being preprocessed, differences in low frequency amplitude (ALFF) metrics between the evaluated patients and controls were calculated and analyzed, and the statistics were corrected for Gaussian random field (GFR). Results: Overall comparative analysis of patients with hearing impairment showed that one-way ANOVA among the three groups showed abnormal ALFF values only in the right anterior cuneiform lobe (GRF adjusted P=0.002). The ALFF value of the hearing impaired group was higher than that of the control group in one cluster (peak coordinates: X=9, Y=-72, Z=48, T=5.82), involving the left occipital gyrus, right anterior cuneiform lobe, left superior cuneiform lobe, left superior parietal gyrus, and left angular gyrus (GRF adjusted P=0.031). The ALFF value of the hearing impaired group was lower than that of the control group in three clusters (peak coordinates: X=57, Y=-48, Z=-24; T=-4.99; X=45, Y=-66, Z=0, T=-4.06; X=42, Y=-12, Z=36, T=-4.03), involving the right inferior temporal gyrus, the right middle temporal gyrus, and the right precentral gyrus (GRF adjusted P=0.009). Compared with the control group, the ALFF value of the left hearing impairment group was significantly higher than that of the control group in one cluster (peak coordinates: X=-12, Y=-75, Z=45, T=5.78), involving the left anterior cuneiform lobe, right anterior cuneiform lobe, left middle occipital gyrus, left superior parietal gyrus, left superior occipital gyrus, left cuneiform lobe, and right cuneiform lobe (P=0.023 after GRF correction). Compared with the control group, the right hearing impairment group had a significantly higher ALFF value in one cluster (peak coordinates: X=9, Y=-46, Z=22, T=6.06), involving the left middle occipital gyrus, right anterior cuneiform lobe, left cuneiform lobe, right cuneiform lobe, left superior occipital gyrus, and right superior occipital gyrus (GRF adjusted P=0.022); The brain area with reduced ALFF values is located in the right inferior temporal gyrus (GRF adjusted P=0.029). Spearman's two-tailed correlation analysis between ALFF values and pure tone average in the abnormal brain regions showed that ALFF values in the abnormal brain regions correlated to some extent with the pure tone average (PTA) only in the left-sided hearing impaired group(PTA=2 000 Hz, r=0.318,P=0.033;PTA=4 000 Hz,r=0.386,P=0.009). Conclusion: The abnormal neural activity within the brain are different in patients with left-sided and right-sided hearing impairment, and the severity of hearing impairment is related to the difference in functional integration of brain regions.
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Affiliation(s)
- H M Li
- Department of Radiology, Fu Xing Hospital, Capital Medical University, Beijing 100038, China
| | - X W Han
- The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - C Y Sang
- Department of Radiology, Fu Xing Hospital, Capital Medical University, Beijing 100038, China
| | - Y Sui
- Department of Radiology, Fu Xing Hospital, Capital Medical University, Beijing 100038, China
| | - G L Ma
- China-Japan Friendship Clinical Medical College of Peking University, Beijing 100029, China
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Wang Y, Yang A, Song Z, Liu B, Chen Y, Lv K, Ma G, Tang X. Investigation of functional connectivity in Bell's palsy using functional magnetic resonance imaging: prospective cross-sectional study. Quant Imaging Med Surg 2023; 13:4676-4686. [PMID: 37456292 PMCID: PMC10347328 DOI: 10.21037/qims-22-911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 03/03/2023] [Indexed: 07/18/2023]
Abstract
Background The most common cause of lower motor neuron facial palsy is Bell's palsy (BP). BP results in partial or complete inability to automatically move the facial muscles on the affected side and, in some cases, to close the eyelids, which can cause permanent eye damage. This study investigated changes in brain function and connectivity abnormalities in patients with BP. Methods This study included 46 patients with unilateral BP and 34 healthy controls (HCs). Resting-state brain functional magnetic resonance imaging (fMRI) images were acquired, and Toronto Facial Grading System (TFGS) scores were obtained for all participants. The fractional amplitude of low-frequency fluctuation (fALFF) was estimated, and the relationship between the TFGS and fALFF was determined using correlation analysis for brain regions with changes in fALFF in those with BP versus HCs. Brain regions associated with TFGS were used as seeds for further functional connectivity (FC) analysis; relationships between FC values of abnormal areas and TFGS scores were also analyzed. Results Activation of the right precuneus, right angular gyrus, left supramarginal gyrus, and left middle occipital gyrus was significantly decreased in the BP group. fALFF was significantly higher in the right thalamus, vermis, and cerebellum of the BP group compared with that in the HC group (P<0.05). The FC between the left middle occipital gyrus and right angular gyrus, left precuneus, and right middle frontal gyrus increased sharply, but decreased in the left angular gyrus, left posterior cingulate gyrus, left middle frontal gyrus, inferior cerebellum, and left middle temporal gyrus. Furthermore, the fALFF in the left middle occipital gyrus was negatively correlated with TFGS score (R=0.144; P=0.008). Conclusions The pathogenesis of BP is closely related to functional reorganization of the cerebral cortex. Patients with BP have altered fALFF activity in cortical regions associated with facial motion feedback monitoring.
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Affiliation(s)
- Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, Beijing, China
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
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Han X, Zhu Z, Luan J, Lv P, Xin X, Zhang X, Shmuel A, Yao Z, Ma G, Zhang B. Effects of repetitive transcranial magnetic stimulation and their underlying neural mechanisms evaluated with magnetic resonance imaging-based brain connectivity network analyses. Eur J Radiol Open 2023; 10:100495. [PMID: 37396489 PMCID: PMC10311181 DOI: 10.1016/j.ejro.2023.100495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 07/04/2023] [Imported: 03/23/2024] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain modulation and rehabilitation technique used in patients with neuropsychiatric diseases. rTMS can structurally remodel or functionally induce activities of specific cortical regions and has developed to an important therapeutic method in such patients. Magnetic resonance imaging (MRI) provides brain data that can be used as an explanation tool for the neural mechanisms underlying rTMS effects; brain alterations related to different functions or structures may be reflected in changes in the interaction and influence of brain connections within intrinsic specific networks. In this review, we discuss the technical details of rTMS and the biological interpretation of brain networks identified with MRI analyses, comprehensively summarize the neurobiological effects in rTMS-modulated individuals, and elaborate on changes in the brain network in patients with various neuropsychiatric diseases receiving rehabilitation treatment with rTMS. We conclude that brain connectivity network analysis based on MRI can reflect alterations in functional and structural connectivity networks comprising adjacent and separated brain regions related to stimulation sites, thus reflecting the occurrence of intrinsic functional integration and neuroplasticity. Therefore, MRI is a valuable tool for understanding the neural mechanisms of rTMS and practically tailoring treatment plans for patients with neuropsychiatric diseases.
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Affiliation(s)
- Xiaowei Han
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, China
- Nanjing University Institute of Medical Imaging and Artificial Intelligence, Nanjing University, China
| | - Zhengyang Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, China
- Nanjing University Institute of Medical Imaging and Artificial Intelligence, Nanjing University, China
| | - Jixin Luan
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, China
| | - Pin Lv
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, China
- Nanjing University Institute of Medical Imaging and Artificial Intelligence, Nanjing University, China
| | - Xiaoyan Xin
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, China
- Nanjing University Institute of Medical Imaging and Artificial Intelligence, Nanjing University, China
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, China
- Nanjing University Institute of Medical Imaging and Artificial Intelligence, Nanjing University, China
| | - Amir Shmuel
- Montreal Neurological Institute, McGill University, Canada
| | - Zeshan Yao
- Biomedical Engineering Institute, Jingjinji National Center of Technology Innovation, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, China
- Nanjing University Institute of Medical Imaging and Artificial Intelligence, Nanjing University, China
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Cui W, Ma Y, Ren J, Liu J, Ma G, Liu H, Li Y. Personalized Functional Connectivity based Spatio-Temporal Aggregated Attention Network for MCI Identification. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2257-2267. [PMID: 37104108 DOI: 10.1109/tnsre.2023.3271062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023] [Imported: 08/14/2023]
Abstract
Functional connectivity (FC) networks derived from resting-state magnetic resonance image (rs-fMRI) are effective biomarkers for identifying mild cognitive impairment (MCI) patients. However, most FC identification methods simply extract features from group-averaged brain templates, and neglect inter-subject functional variations. Furthermore, the existing methods generally concentrate on spatial correlation among brain regions, resulting in the inefficient capture of the fMRI temporal features. To address these limitations, we propose a novel personalized functional connectivity based dual-branch graph neural network with spatio-temporal aggregated attention (PFC-DBGNN-STAA) for MCI identification. Specifically, a personalized functional connectivity (PFC) template is firstly constructed to align 213 functional regions across samples and generate discriminative individualized FC features. Secondly, a dual-branch graph neural network (DBGNN) is conducted by aggregating features from the individual- and group-level templates with the cross-template FC, which is beneficial to improve the feature discrimination by considering dependency between templates. Finally, a spatio-temporal aggregated attention (STAA) module is investigated to capture the spatial and dynamic relationships between functional regions, which solves the limitation of insufficient temporal information utilization. We evaluate our proposed method on 442 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieve the accuracies of 90.1%, 90.3%, 83.3% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification tasks, respectively, indicating that our method boosts MCI identification performance and outperforms state-of-the-art methods.
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Chen X, Ke P, Huang Y, Zhou J, Li H, Peng R, Huang J, Liang L, Ma G, Li X, Ning Y, Wu F, Wu K. Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis. Front Neurosci 2023; 17:1140801. [PMID: 37090813 PMCID: PMC10117439 DOI: 10.3389/fnins.2023.1140801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/10/2023] [Indexed: 03/31/2023] [Imported: 08/14/2023] Open
Abstract
IntroductionRecent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks.MethodsWe applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph).ResultsThe GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing.DiscussionOur findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git.
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Affiliation(s)
- Xiaoyi Chen
- Department of Biomedical Engineering, School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Yuanyuan Huang
- Department of Emotional Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Jing Zhou
- School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China
| | - Hehua Li
- Department of Emotional Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Runlin Peng
- Department of Biomedical Engineering, School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Jiayuan Huang
- Department of Biomedical Engineering, School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - LiQing Liang
- Department of Biomedical Engineering, School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Yuping Ning
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- Department of Psychosomatic, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Fengchun Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- Department of Biomedical Engineering, School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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Chen Q, Chen F, Zhu Y, Long C, Lu J, Zhang X, Nedelska Z, Hort J, Chen J, Ma G, Zhang B. Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline. Neurobiol Aging 2023; 127:82-93. [PMID: 37116409 DOI: 10.1016/j.neurobiolaging.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/12/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] [Imported: 08/14/2023]
Abstract
Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.
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Chen X, Zhou J, Ke P, Huang J, Xiong D, Huang Y, Ma G, Ning Y, Wu F, Wu K. Classification of schizophrenia patients using a graph convolutional network: A combined functional MRI and connectomics analysis. Biomed Signal Process Control 2023; 80:104293. [DOI: 10.1016/j.bspc.2022.104293] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] [Imported: 08/14/2023]
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Cui LB, Wang DJJ, Ma G. Editorial: Multi-parametric perfusion MRI by arterial spin labeling. Front Neurosci 2023; 16:1132835. [PMID: 36711152 PMCID: PMC9875590 DOI: 10.3389/fnins.2022.1132835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 12/30/2022] [Indexed: 01/13/2023] [Imported: 08/14/2023] Open
Affiliation(s)
- Long-Biao Cui
- Schizophrenia Imaging Lab, Fourth Military Medical University, Xi'an, China
| | - Danny J. J. Wang
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China,*Correspondence: Guolin Ma ✉
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Yang A, Liu B, Lv K, Luan J, Hu P, Yu H, Shmuel A, Li S, Tian H, Ma G, Zhang B. Altered coupling of resting-state cerebral blood flow and functional connectivity in Meige syndrome. Front Neurosci 2023; 17:1152161. [PMID: 37207180 PMCID: PMC10188939 DOI: 10.3389/fnins.2023.1152161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/22/2023] [Indexed: 05/21/2023] Open
Abstract
Introduction Meige syndrome (MS) is an adult-onset segmental dystonia disease, mainly manifested as blepharospasm and involuntary movement caused by dystonic dysfunction of the oromandibular muscles. The changes of brain activity, perfusion and neurovascular coupling in patients with Meige syndrome are hitherto unknown. Methods Twenty-five MS patients and thirty age- and sex-matched healthy controls (HC) were prospectively recruited in this study. All the participants underwent resting-state arterial spin labeling and blood oxygen level-dependent examinations on a 3.0 T MR scanner. The measurement of neurovascular coupling was calculated using cerebral blood flow (CBF)-functional connectivity strength (FCS) correlations across the voxels of whole gray matter. Also, voxel-wised analyses of CBF, FCS, and CBF/FCS ratio images between MS and HC were conducted. Additionally, CBF and FCS values were compared between these two groups in selected motion-related brain regions. Results MS patients showed increased whole gray matter CBF-FCS coupling relative to HC (t = 2.262, p = 0.028). In addition, MS patients showed significantly increased CBF value in middle frontal gyrus and bilateral precentral gyrus. Conclusion The abnormal elevated neurovascular coupling of MS may indicate a compensated blood perfusion in motor-related brain regions and reorganized the balance between neuronal activity and brain blood supply. Our results provide a new insight into the neural mechanism underlying MS from the perspective of neurovascular coupling and cerebral perfusion.
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Affiliation(s)
- Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Departments of Neurology and Neurosurgery, Physiology, and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Shijun Li
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hong Tian
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
- Hong Tian,
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Guolin Ma,
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Beijing, China
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Meng X, Fan J, Yu H, Mu J, Li Z, Yang A, Liu B, Lv K, Ai D, Lin Y, Song H, Fu T, Xiao D, Ma G, Yang J, Gu Y. Volume-awareness and outlier-suppression co-training for weakly-supervised MRI breast mass segmentation with partial annotations. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] [Imported: 08/14/2023]
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Lu C, Yang A, Xia F, Liu G, Zhao H, Zhang W, Li Y, Liu J, Ma G, Xia H. Liquid metal injected from interstitial channels for inhibiting subcutaneous hepatoma growth and improving MRI/MAT image contrast. Front Oncol 2022; 12:1019592. [PMID: 36479081 PMCID: PMC9720740 DOI: 10.3389/fonc.2022.1019592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/30/2022] [Indexed: 08/14/2023] [Imported: 08/14/2023] Open
Abstract
Objective Liquid metal (LM) nowadays is considered a new biomedical material for medical treatment. The most common application of LM in medical therapy is taking LM as a carrier for oncology therapeutics. However, the feasibility and direct effect of LM in tumor treatment are still unknown, and how to delineate the negative resection margin (NRM) of the tumor is also a crucial problem in surgery. We aimed to inject LM into interstitial channels of extremities of mice to overlay the surface of the primary tumor to investigate the effect of LM on inhibiting tumor growth and highlight the NRM of the tumor. Methods In this study, all 50 BALB/c-nude female mice were used to construct the transplanted HepG2-type hepatocellular carcinoma model. One week after the establishment of the model, the mice were divided into three groups, named LM group, PBS group and Control group by injecting different liquid materials into the forelimb interstitial channel of the mice. T2WI image on MRI and Magneto-acoustic tomography (MAT) were used to show the distribution of LM and PBS in vivo. The group comparisons of tumor growth and blood tests were evaluated by one-way ANOVA and post-hoc analysis. And the biocompatibility of LM to BALB/c nude mice was evaluated by histopathological analysis of LM group and control group. Results The volume change ratio of tumor was significantly lower in LM group than in PBS and Control group after 10 days of grouping. Compared with PBS and Control group, the main indexes of blood tests in LM group were significantly lower and close to normal level. In addition, the distribution of LM in vivo could be clearly observed under T2WI anatomic images and the crossprofile of the tumor in MAT. LM also has a obvious contrast in MRI T2WI and enhanced the amplitude of imaging signal in MAT. Conclusion LM may inhibit the growth of transplanted hepatoma tumor through tumor encapsulation. In vivo, tumor imaging and LM distribution imaging were achieved by MRI T2WI, which verified that LM injected with interstitial injection made the NRM of tumor more prominent and had the potential of being MRI contrast agent. At the same time, LM could also be a new conductive medium to improve the imaging quality of MAT. Moreover, LM performed mild biocompatibility.
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Affiliation(s)
- Chaosen Lu
- Department of Engineering Electromagnetic Field and Its Application, Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China
- College of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei Xia
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Guoqiang Liu
- Department of Engineering Electromagnetic Field and Its Application, Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China
- Department of Electronic and Electrical Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Hongliang Zhao
- College of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Wenwei Zhang
- Department of Engineering Electromagnetic Field and Its Application, Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China
- Department of Electronic and Electrical Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Yuanyuan Li
- Department of Engineering Electromagnetic Field and Its Application, Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China
- Department of Electronic and Electrical Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Jian Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Xia
- Department of Engineering Electromagnetic Field and Its Application, Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China
- Department of Electronic and Electrical Engineering, University of Chinese Academy of Sciences, Beijing, China
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Ye F, Du L, Liu B, Gao X, Yang A, Liu D, Chen Y, Lv K, Xu P, Chen Y, Liu J, Zhang L, Li S, Shmuel A, Zhang Q, Ma G. Application of pseudocontinuous arterial spin labeling perfusion imaging in children with autism spectrum disorders. Front Neurosci 2022; 16:1045585. [PMID: 36425476 PMCID: PMC9680558 DOI: 10.3389/fnins.2022.1045585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/17/2022] [Indexed: 11/11/2022] [Imported: 08/14/2023] Open
Abstract
Introduction Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging. Materials and methods This cross-sectional study enrolled 17 individuals with ASD and 13 TD children. All participants underwent pCASL examination on a 3.0 T MRI scanner. Children in two groups were assessed for clinical characteristics and developmental profiles using Autism Behavior Checklist (ABC) and Gesell development diagnosis scale (GDDS), respectively. We compared CBF in different cerebral regions of ASD and TD children. We also assessed the association between CBF and clinical characteristics/developmental profile. Results Compared with TD children, individuals with ASD demonstrated a reduction in CBF in the left frontal lobe, the bilateral parietal lobes, and the bilateral temporal lobes. Within the ASD group, CBF was significantly higher in the right parietal lobe than in the left side. Correlation analysis of behavior characteristics and CBF in different regions showed a positive correlation between body and object domain scores on the ABC and CBF of the bilateral occipital lobes, and separately, between language domain scores and CBF of the left frontal lobe. The score of the social and self-help domain was negatively correlated with the CBF of the left frontal lobe, the left parietal lobe, and the left temporal lobe. Conclusion Cerebral blood flow was found to be negatively correlated with scores in the social and self-help domain, and positively correlated with those in the body and object domain, indicating that CBF values are a potential MRI-based biomarker of disease severity in ASD patients. The findings may provide novel insight into the pathophysiological mechanisms of ASD.
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Affiliation(s)
- Fang Ye
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Department of Radiology, Peking University, Cancer Hospital and Institute, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xinying Gao
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Die Liu
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pengfei Xu
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Yuanmei Chen
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Jing Liu
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Lipeng Zhang
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shijun Li
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Qi Zhang
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
- Qi Zhang,
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- *Correspondence: Guolin Ma,
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Zhang H, Chen H, Qin J, Wang B, Ma G, Wang P, Zhong D, Liu J. MC-ViT: Multi-path cross-scale vision transformer for thymoma histopathology whole slide image typing. Front Oncol 2022; 12:925903. [PMID: 36387248 PMCID: PMC9659861 DOI: 10.3389/fonc.2022.925903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/11/2022] [Indexed: 08/14/2023] [Imported: 08/14/2023] Open
Abstract
OBJECTIVES Accurate histological typing plays an important role in diagnosing thymoma or thymic carcinoma (TC) and predicting the corresponding prognosis. In this paper, we develop and validate a deep learning-based thymoma typing method for hematoxylin & eosin (H&E)-stained whole slide images (WSIs), which provides useful histopathology information from patients to assist doctors for better diagnosing thymoma or TC. METHODS We propose a multi-path cross-scale vision transformer (MC-ViT), which first uses the cross attentive scale-aware transformer (CAST) to classify the pathological information related to thymoma, and then uses such pathological information priors to assist the WSIs transformer (WT) for thymoma typing. To make full use of the multi-scale (10×, 20×, and 40×) information inherent in a WSI, CAST not only employs parallel multi-path to capture different receptive field features from multi-scale WSI inputs, but also introduces the cross-correlation attention module (CAM) to aggregate multi-scale features to achieve cross-scale spatial information complementarity. After that, WT can effectively convert full-scale WSIs into 1D feature matrices with pathological information labels to improve the efficiency and accuracy of thymoma typing. RESULTS We construct a large-scale thymoma histopathology WSI (THW) dataset and annotate corresponding pathological information and thymoma typing labels. The proposed MC-ViT achieves the Top-1 accuracy of 0.939 and 0.951 in pathological information classification and thymoma typing, respectively. Moreover, the quantitative and statistical experiments on the THW dataset also demonstrate that our pipeline performs favorably against the existing classical convolutional neural networks, vision transformers, and deep learning-based medical image classification methods. CONCLUSION This paper demonstrates that comprehensively utilizing the pathological information contained in multi-scale WSIs is feasible for thymoma typing and achieves clinically acceptable performance. Specifically, the proposed MC-ViT can well predict pathological information classes as well as thymoma types, which show the application potential to the diagnosis of thymoma and TC and may assist doctors in improving diagnosis efficiency and accuracy.
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Affiliation(s)
- Huaqi Zhang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Huang Chen
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Jin Qin
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Bei Wang
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Pengyu Wang
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Dingrong Zhong
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
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21
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Liu B, Yang A, Gao W, Chen Y, Wang Y, Liu X, Lv K, Zhang L, Ma G. Altered cerebral blood flow in patients with spinocerebellar degeneration. Front Neurosci 2022; 16:977145. [PMID: 36177360 PMCID: PMC9513175 DOI: 10.3389/fnins.2022.977145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/24/2022] [Indexed: 12/03/2022] [Imported: 08/14/2023] Open
Abstract
Objectives Spinocerebellar degeneration (SCD) comprises a multitude of disorders with sporadic and hereditary forms, including spinocerebellar ataxia (SCA). Except for progressive cerebellar ataxia and structural atrophy, hemodynamic changes have also been observed in SCD. This study aimed to explore the whole-brain patterns of altered cerebral blood flow (CBF) and its correlations with disease severity and psychological abnormalities in SCD via arterial spin labeling (ASL). Methods Thirty SCD patients and 30 age- and sex-matched healthy controls (HC) were prospectively recruited and underwent ASL examination on a 3.0T MR scanner. The Scale for Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS) scores were used to evaluate the disease severity in SCD patients. Additionally, the status of anxiety, depression and sleep among all patients were, respectively, evaluated by the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS) and Self-Rating Scale of Sleep (SRSS). We compared the whole-brain CBF value between SCD group and HC group at the voxel level. Then, the correlation analyses between CBF and disease severity, and psychological abnormalities were performed on SCD group. Results Compared with HC, SCD patients demonstrated decreased CBF value in two clusters (FWE corrected P < 0.05), covering bilateral dentate and fastigial nuclei, bilateral cerebellar lobules I-IV, V and IX, left lobule VI, right lobule VIIIb, lobules IX and X of the vermis in the cerebellar Cluster 1 and the dorsal part of raphe nucleus in the midbrain Cluster 2. The CBF of cerebellar Cluster 1 was negatively correlated with SARA scores (Spearman’s rho = –0.374, P = 0.042) and SDS standard scores (Spearman’s rho = –0.388, P = 0.034), respectively. And, the CBF of midbrain Cluster 2 also had negative correlations with SARA scores (Spearman’s rho = –0.370, P = 0.044) and ICARS scores (Pearson r = –0.464, P = 0.010). Conclusion The SCD-related whole-brain CBF changes mainly involved in the cerebellum and the midbrain of brainstem, which are partially overlapped with the related function cerebellar areas of hand, foot and tongue movement. Decreased CBF was related to disease severity and depression status in SCD. Therefore, CBF may be a promising neuroimaging biomarker to reflect the severity of SCD and suggest mental changes.
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Affiliation(s)
- Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Linwei Zhang
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
- Linwei Zhang,
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Guolin Ma,
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22
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Yang A, Du L, Gao W, Liu B, Chen Y, Wang Y, Liu X, Lv K, Zhang W, Xia H, Wu K, Ma G. Associations of cortical iron accumulation with cognition and cerebral atrophy in Alzheimer’s disease. Quant Imaging Med Surg 2022; 12:4570-4586. [PMID: 36060596 PMCID: PMC9403583 DOI: 10.21037/qims-22-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 06/10/2022] [Indexed: 11/06/2022] [Imported: 08/14/2023]
Affiliation(s)
- Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lei Du
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Wenwei Zhang
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
| | - Hui Xia
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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23
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Lv P, Ma G, Chen W, Liu R, Xin X, Lu J, Su S, Li M, Yang S, Ma Y, Rong P, Dong N, Chen Q, Zhang X, Han X, Zhang B. Brain morphological alterations and their correlation to tumor differentiation and duration in patients with lung cancer after platinum chemotherapy. Front Oncol 2022; 12:903249. [PMID: 36016623 PMCID: PMC9396961 DOI: 10.3389/fonc.2022.903249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] [Imported: 08/14/2023] Open
Abstract
ObjectiveChemotherapy-related brain impairments and changes can occur in patients with lung cancer after platinum chemotherapy and have a substantial impact on survivors’ quality of life. Therefore, it is necessary to understand the brain neuropathological alterations and response mechanisms to provide a theoretical basis for rehabilitation strategies. This study aimed to investigate the related brain morphological changes and clarified their correlation with clinical and pathological indicators in patients with lung cancer after platinum chemotherapy.MethodsOverall, 28 patients with chemotherapy, 56 patients without chemotherapy, and 41 healthy controls were categorized in three groups, matched for age, sex, and years of education, and included in the cross-sectional comparison of brain volume and cortical thickness. 14 matched patients before and after chemotherapy were subjected to paired comparison for longitudinal observation of brain morphological changes. Three-dimensional T1-weighted images were acquired from all participants, and quantitative parameters were calculated using the formula of the change from baseline. Correlation analysis was performed to evaluate the relationship between abnormal morphological indices and clinical information of patients.ResultsBrain regions with volume differences among the three groups were mainly distributed in frontal lobe and limbic cortex. Additionally, significant differences in cerebrospinal fluid were observed in most ventricles, and the main brain regions with cortical thickness differences were the gyrus rectus and medial frontal cortex of the frontal lobe, transverse temporal gyrus of the temporal lobe, insular cortex, anterior insula, and posterior insula of the insular cortex. According to the paired comparison, decreased brain volumes in the patients after chemotherapy appeared in some regions of the frontal, parietal, temporal, and occipital lobes; limbic cortex; insular cortex; and lobules VI-X and decreased cortical thickness in the patients after chemotherapy was found in the frontal, temporal, limbic, and insular cortexes. In the correlation analysis, only the differentiation degree of the tumor and duration after chemotherapy were significantly correlated with imaging indices in the abnormal brain regions.ConclusionsOur findings illustrate the platinum-related brain reactivity morphological alterations which provide more insights into the neuropathological mechanisms of patients with lung cancer after platinum chemotherapy and empirical support for the details of brain injury related to cancer and chemotherapy.
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Affiliation(s)
- Pin Lv
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenqian Chen
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Renyuan Liu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaoyan Xin
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Shu Su
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University and Clinical Cancer Institute of Nanjing University, Nanjing, China
| | - Ming Li
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - ShangWen Yang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yiming Ma
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Ping Rong
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Ningyu Dong
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Qian Chen
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
- *Correspondence: Xiaowei Han, ; Bing Zhang,
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China
- Institute of Brain Science, Nanjing University, Nanjing, China
- *Correspondence: Xiaowei Han, ; Bing Zhang,
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Feng G, Wang Y, Huang W, Chen H, Dai Z, Ma G, Li X, Zhang Z, Shu N. Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome. Hum Brain Mapp 2022; 43:3775-3791. [PMID: 35475571 PMCID: PMC9294303 DOI: 10.1002/hbm.25883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/22/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022] [Imported: 08/14/2023] Open
Abstract
An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Yiwen Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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Zhang Y, Yuan N, Zhang Z, Du J, Wang T, Liu B, Yang A, Lv K, Ma G, Lei B. Unsupervised Domain Selective Graph Convolutional Network for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer. Med Image Anal 2022; 79:102467. [PMID: 35537338 DOI: 10.1016/j.media.2022.102467] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 12/24/2022] [Imported: 08/14/2023]
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Li W, Zhang H, Ren L, Zou Y, Tian F, Ji X, Li Q, Wang W, Ma G, Xia S. Radiomics of dual-energy computed tomography for predicting progression-free survival in patients with early glottic cancer. Future Oncol 2022; 18:1873-1884. [PMID: 35293227 DOI: 10.2217/fon-2021-1125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] [Imported: 08/14/2023] Open
Abstract
Aim: This study aimed to predict progression-free survival (PFS) in patients with early glottic cancer using radiomic features on dual-energy computed tomography iodine maps. Methods: Radiomic features were extracted from arterial and venous phase iodine maps, and radiomic risk scores were determined by univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator regression with tenfold cross-validation. The Kaplan-Meier method was used to evaluate the association between radiomic risk scores and PFS. Results: Patients were stratified into low-risk and high-risk groups using radiomics, the PFS corresponding rates with statistical significance between the two groups. The high-risk group showed better survival, benefiting from laryngectomy. Conclusion: Radiomics could provide a promising biomarker for predicting the PFS of early glottic cancer patients.
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Affiliation(s)
- Wenfei Li
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China.,Department of Radiology, The First Hospital of Qinhuangdao, No. 258 Wenhua Road, Haigang District, Qinhuangdao, 066000, China
| | - Huanlei Zhang
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Lei Ren
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China.,Department of Medical Imaging, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, No. 88 Changling Road, Xiqing District, Tianjin, 300381, China
| | - Ying Zou
- Department of Medical Imaging, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, No. 88 Changling Road, Xiqing District, Tianjin, 300381, China
| | - Fengyue Tian
- Department of Radiology, Affiliated Hospital of Nankai University (Tianjin No. 4 Hospital), No. 4 Weishan Road, Hexi District, Tianjin, 300222, China
| | - Xiaodong Ji
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Qing Li
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Wei Wang
- Department of Otolaryngology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Shuang Xia
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
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Chen Y, Li H, Liu B, Gao W, Yang A, Lv K, Xia H, Zhang W, Yu H, Liu J, Liu X, Wang Y, Han H, Ma G. Cerebral Blood Flow Pattern Changes in Unilateral Sudden Sensorineural Hearing Loss. Front Neurosci 2022; 16:856710. [PMID: 35356053 PMCID: PMC8959761 DOI: 10.3389/fnins.2022.856710] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] [Imported: 08/14/2023] Open
Abstract
ObjectiveThis study analyzed the differences in the cerebral blood flow (CBF) between unilateral Sudden Sensorineural Hearing Loss (SSNHL) patients and healthy controls (HCs). We also investigated CBF differences in auditory-related areas in patients with left- and right-sided SSNHL (lSSNHL and rSSNHL) and HCs. We further explore the correlation between unilateral SSNHL characteristics and changes in the CBF.Methods36 patients with unilateral SSNHL (15 males and 21 females, 40.39 ± 13.42 years) and 36 HCs (15 males and 21 females, 40.39 ± 14.11 years) were recruited. CBF images were collected and analyzed using arterial spin labeling (ASL). CereFlow software was used for the post-processing of the ASL data to obtain the CBF value of 246 subregions within brainnetome atlas (BNA). The Two-sample t-test was used to compare CBF differences between SSNHL patients and HCs. One-way ANOVA or Kruskal-Wallis test was used to compare the CBF difference of auditory-related areas among the three groups (lSSNHL, rSSNHL, and HCs). Then, the correlation between CBF changes and specific clinical characteristics were calculated.ResultsThe SSNHL patients exhibited decreased CBF in the bilateral middle frontal gyrus (MFG, MFG_7_1 and MFG_7_3), the contralateral precentral gyrus (PrG, PrG_6_3) and the bilateral superior parietal lobule (SPL, bilateral SPL_5_1, SPL_5_2, and ipsilateral SPL_5_4), p < 0.0002. Compared with HCs, unilateral SSNHL patients exhibited increased rCBF in the bilateral orbital gyrus (OrG, OrG_6_5), the bilateral inferior temporal gyrus (ITG, contralateral ITG_7_1 and bilateral ITG_7_7), p < 0.0002. lSSNHL showed abnormal CBF in left BA21 caudal (p = 0.02) and left BA37 dorsolateral (p = 0.047). We found that the CBF in ipsilateral MFG_7_1 of SSNHL patients was positively correlated with tinnitus Visual Analog Scale (VAS) score (r = 0.485, p = 0.008).ConclusionOur preliminary study explored CBF pattern changes in unilateral SSNHL patients in auditory-related areas and non-auditory areas, suggesting that there may exist reduced attention and some sensory compensation in patients with SSNHL. These findings could advance our understanding of the potential pathophysiology of unilateral SSNHL.
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Affiliation(s)
- Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Haimei Li
- Department of Radiology, Fuxing Hospital, Capital Medical University, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Hui Xia
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
| | - Wenwei Zhang
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Honglei Han
- Department of Otolaryngology, China-Japan Friendship Hospital, Beijing, China
- Honglei Han,
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- *Correspondence: Guolin Ma,
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Abstract
Liquid metals (LMs) not only retain the basic properties of metallic biomaterials, such as high thermal conductivity and high electrical conductivity, but also possess flexibility, flowability, deformability, plasticity, good adhesion, and so on. Therefore, they open many possibilities of extending soft metals into biomedical sciences including biomedical imaging. One of the special properties of LMs is that they can provide a controllable material system in which the electrical, thermal, mechanical, and chemical properties can be controlled on a large scale. This paper reviews the preparation and characteristics of LM-based biomaterials classified into four categories: LM micro/nanoparticles, surface modified LM droplets, LM composites with inorganic substances, and LM composites with organic polymers. Besides, considering the most important requirement for biomaterials is biocompatibility, the paper also analyzes the toxicity results of various LM biomaterials when used in the biomedical area, from different levels including body weight measurement, histology evaluation, and blood biochemistry tests. Next, the applications of LMs in X-ray, CT, MRI, photoacoustic imaging, and molecular imaging are introduced in detail. And finally, the challenges and opportunities of their application in medical imaging are also discussed.
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Affiliation(s)
- Wenwen Gao
- Beijing Key Lab of CryoBiomedical Engineering and Key Lab of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China. .,Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China. .,Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, China
| | - Yige Wang
- Beijing Key Lab of CryoBiomedical Engineering and Key Lab of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China. .,Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China. .,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Wang
- Beijing Key Lab of CryoBiomedical Engineering and Key Lab of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China. .,School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China.
| | - Jing Liu
- Beijing Key Lab of CryoBiomedical Engineering and Key Lab of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China. .,School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.,Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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Huang Z, Lei H, Chen G, Li H, Li C, Gao W, Chen Y, Wang Y, Xu H, Ma G, Lei B. Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis. Appl Soft Comput 2021; 115:108088. [PMID: 34840541 PMCID: PMC8611958 DOI: 10.1016/j.asoc.2021.108088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 10/18/2021] [Accepted: 11/07/2021] [Indexed: 12/30/2022] [Imported: 08/14/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.
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Affiliation(s)
- Zhongwei Huang
- Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Guangdong Province Engineering Center of China-made High Performance Data Computing System, Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Haijun Lei
- Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Guangdong Province Engineering Center of China-made High Performance Data Computing System, Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Guoliang Chen
- Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Guangdong Province Engineering Center of China-made High Performance Data Computing System, Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Haimei Li
- Department of Radiology, Fu Xing Hospital, Capital Medical University, Beijing, China
| | - Chuandong Li
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yaofa Wang
- Minfound Medical Systems Co., Ltd., Hangzhou, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Baiying Lei
- National- Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
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Gao W, Yang D, Zhang Z, Du L, Liu B, Liu J, Chen Y, Wang Y, Liu X, Yang A, Lv K, Xue J, Ma G. Altered Cortical-Striatal Network in Patients With Hemifacial Spasm. Front Hum Neurosci 2021; 15:770107. [PMID: 34744670 PMCID: PMC8569140 DOI: 10.3389/fnhum.2021.770107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022] [Imported: 08/14/2023] Open
Abstract
Objective: Hemifacial spasm (HFS) is a kind of motor disorder, and the striatum plays a significant role in motor function. The purpose of this study was to explore the alterations of the cortical-striatal network in HFS using resting-state functional magnetic resonance imaging (fMRI). Methods: The fMRI data of 30 adult patients with primary unilateral HFS (15 left-side and 15 right-side) and 30 healthy controls were collected. Six subregions of the striatum in each hemisphere were selected for functional connectivity (FC) analysis. One-sample t-test was used to analyze the intragroup FC of the HFS group and the control group. Two-sample t-test was used to compare the difference of FC between the two groups. The correlation between the abnormal FC and severity of HFS was evaluated by using the Spearman correlation analysis. Results: Compared with the controls, the striatal subregions had altered FC with motor and orbitofrontal cortex in patients with HFS. The altered FC between striatal subregions and motor cortex was correlated with the spasm severity in patients with HFS. Conclusion: The FC of the cortical-striatal network was altered in primary HFS, and these alterations were correlated with the severity of HFS. This study indicated that the cortical-striatal network may play different roles in the underlying pathological mechanism of HFS.
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Affiliation(s)
- Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Dong Yang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Zhe Zhang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jiajia Xue
- Beijing Laboratory of Biomedical Materials, Beijing University of Chemical Technology, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
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Han X, Luo S, Liu B, Chen Y, Gao W, Wang Y, Liu X, Yu H, Zhang L, Ma G. Acute Angle of Multilobulated Contours Improves the Risk Classification of Thymomas. Front Med (Lausanne) 2021; 8:744587. [PMID: 34660649 PMCID: PMC8513789 DOI: 10.3389/fmed.2021.744587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/30/2021] [Indexed: 11/29/2022] [Imported: 08/14/2023] Open
Abstract
Background: Computed tomography plays an important role in the identification and characterization of thymomas. It has been mainly used during preoperative evaluation for clinical staging. However, the reliable prediction of histological risk types of thymomas based on CT imaging features requires further study. In this study, we developed and validated a nomogram based on CT imaging and included new indices for individualized preoperative prediction of the risk classification of thymomas. Methods: We conducted a retrospective, multicenter study that included 229 patients from two Chinese medical centers. All the patients underwent cross-sectional CT imaging within 2 weeks before surgery. The results of pathological assessments were retrieved from existing reports of the excised lesions. The tumor perimeter that contacted the lung (TPCL) was evaluated and a new quantitative indicator, the acute angle (AA) formed by adjacent lobulations, was measured. Two predictive models of risk classification were created using the least absolute shrinkage and selection operator (LASSO) method in a training cohort for features selection. The model with a smaller Akaike information criterion was then used to create an individualized imaging nomogram, which we evaluated regarding its prediction ability and clinical utility. Results: A new CT imaging-based model incorporating AA was developed and validated, which had improved predictive performance during risk classification of thymomas when compared with a model using traditional imaging predictors. The new imaging nomogram with AA demonstrated its clinical utility by decision curve analysis. Conclusions: Acute angle can improve the performance of a CT-based predictive model during the preoperative risk classification of thymomas and should be considered a new imaging marker for the evaluation and treatment of patients with thymomas. On the contrary, TPCL is not useful as a predictor for the risk classification of thymomas in this study.
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Affiliation(s)
- Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Song Luo
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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32
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Chen X, Liu J, Li P, Wang JM, Zhao LX, Han XW, Chen Y, Yu HW, Ma GL. [The application of artificial intelligence on the classification of benign and malignant breast tumors based on dynamic enhanced MR images]. Zhonghua Yi Xue Za Zhi 2021; 101:3029-3032. [PMID: 34638196 DOI: 10.3760/cma.j.cn112137-20210128-00269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] [Imported: 08/14/2023]
Abstract
This retrospective analysis was conducted on clinical obtained DCE-MR images of 198 patients, age from 21 to 79 years(45.5±13.7). The CBAM-ResNet model was developed to perform the classification automatically at the image-level based on deep learning method using the pathological examination as the reference standard,then the classification result of each individual patient was obtained by ensemble learning. The proposed method can have an accuracy of 82.69% for correctly distinguishing between benign and malignant breast tumors at the slice-level based on CBAM-ResNet model and with a sensitivity of 85.67%.. After the voting mechanism is applied, the classification accuracy can reach up to 88.24% at the patient-level with a sensitivity of 87.50%. Our experimental results demonstrated the proposed approach have a high classification accuracy.
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Affiliation(s)
- X Chen
- School of Biomedical Engineering, University of Science and Technology of China, Hefei 230000, China
| | - J Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - P Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215000, China
| | - J M Wang
- School of Biomedical Engineering, University of Science and Technology of China, Hefei 230000, China
| | - L X Zhao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215000, China
| | - X W Han
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Y Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - H W Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - G L Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
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Song X, Li H, Gao W, Chen Y, Wang T, Ma G, Lei B. Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis. IEEE Trans Industr Inform 2021; 17:6499-6509. [PMID: 37981914 PMCID: PMC8545009 DOI: 10.1109/tii.2021.3056686] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 12/05/2020] [Accepted: 01/24/2021] [Indexed: 08/14/2023] [Imported: 08/14/2023]
Abstract
Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly.1.
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Affiliation(s)
- Xuegang Song
- Health Science Center, School of Biomedical EngineeringShenzhen UniversityShenzhen518060China
| | - Haimei Li
- Department of RadiologyFuxing Hospital affiliated to Capital Medical UniversityBeijing100038China
| | - Wenwen Gao
- Department of RadiologyChina-Japan Friendship HospitalBeijing100029China
| | - Yue Chen
- Department of RadiologyChina-Japan Friendship HospitalBeijing100029China
| | - Tianfu Wang
- Health Science Center, School of Biomedical EngineeringShenzhen UniversityShenzhen518060China
| | - Guolin Ma
- Department of RadiologyChina-Japan Friendship HospitalBeijing100029China
| | - Baiying Lei
- Health Science Center, School of Biomedical EngineeringShenzhen UniversityShenzhen518060China
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Shi SM, Li HM, Zhang ZY, Gao WW, Chen Y, Ma GL. [Analysis of cerebral perfusion of leptomeningeal branch and perforating branch of unilateral middle cerebral artery with severe stenosis or occlusion based on multi-delay arterial spin labeling]. Zhonghua Yi Xue Za Zhi 2021; 101:1784-1790. [PMID: 34167278 DOI: 10.3760/cma.j.cn112137-20210207-00381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] [Imported: 08/14/2023]
Abstract
Objective: To compare the cerebral perfusion differences between the symptomatic patients and the asymptomatic patients with unilateral middle cerebral artery (MCA) severe stenosis or occlusion by using three post labeling delays (PLD) of the three-dimensional pseudo-continuous arterial spin labeling (pCASL) technique. Methods: The clinical characteristics and ASL data of the 27 patients with severe stenosis or occlusion of unilateral MCA (18 symptomatic, 9 asymptomatic) were prospectively enrolled from April 2018 to November 2019 in the Department of Radiology of China-Japan Friendship Hospital. There were 16 males and 11 females, age range from 29 to 85 (55±13) years. According to the symptoms, they were divided into symptomatic group (18 cases) and asymptomatic group (9 cases). The parameters of cerebral blood flow (CBF), mean cerebral blood flow (mCBF), arterial transit time (ATT) and arterial cerebral blood volume (aCBV) were obtained using the Cereflow software. One-way multivariate analysis of variance (one-way MANOVA) was used to compare the differences of cerebral perfusion parameters between symptomatic group and asymptomatic group, and between the affected side and the control side in the two groups. Two-way ANOVA was used to evaluate the effects of symptoms, hemisphere (affected and control side), PLD times (1.5, 2.0 and 2.5 s) and the interaction between the two factors. Results: The CBF of the affected leptomeningeal branch and perforating branch of MCA in symptomatic group was lower than that in asymptomatic group [(36.8±10.2) ml·100 g-1·min-1 versus (46.6±13.9) ml·100 g-1·min-1, F(1, 75)=13.279, P=0.000 49; (32.3±8.3) ml·100 g-1·min-1 versus (36.2±7.5) ml·100 g-1·min-1, F (1, 75)=4.065, P=0.047], and there was no interaction between the symptom and PLD [F(2, 75) =0.061, P=0.940]. In the symptomatic group, the CBF of the leptomeningeal branch and perforating branch of MCA in affected side was lower than that in control side [(36.8±10.2) ml·100 g-1·min-1 versus (43.7±10.0) ml·100 g-1·min-1, F(1, 102)=12.559, P=0.000 59; (32.3±8.3) ml·100 g-1·min-1 versus (36.4±8.0) ml·100 g-1·min-1, F(1, 102)=6.493, P=0.012]. In the symptomatic group, the CBF of leptomeningeal branch of MCA when PLD was 2.5 s was 7.34 ml·100 g-1·min-1, which were higher than that when PLD of 1.5 s (95%CI: 0.72-13.9, P=0.03). There was no interaction between PLD and hemisphere [F(2, 102) =0.307, P=0.736]. Conclusions: The collateral circulation in the blood supply area of MCA in asymptomatic patients with severe unilateral MCA stenosis or occlusion is more abundant than that in symptomatic patients. ASL can be an effective technique for evaluating the cerebral perfusion of collateral circulation in patients with severe stenosis or occlusion of MCA.
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Affiliation(s)
- S M Shi
- Department of Radilolgy, China-Japan Friendship Clinical Medical College of Peking University, Beijing 100029, China
| | - H M Li
- Department of Radilogy, Fu Xing Hospital, Capital Medical University, Beijing 100045, China
| | - Z Y Zhang
- Department of Newrology, China-Japan Friendship Hospital, Beijing 100029, China
| | - W W Gao
- Department of Radilolgy, China-Japan Friendship Clinical Medical College of Peking University, Beijing 100029, China
| | - Y Chen
- Department of Radilolgy, China-Japan Friendship Clinical Medical College of Peking University, Beijing 100029, China
| | - G L Ma
- Department of Radilolgy, China-Japan Friendship Clinical Medical College of Peking University, Beijing 100029, China
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Zhang Y, Li H, Du J, Qin J, Wang T, Chen Y, Liu B, Gao W, Ma G, Lei B. 3D Multi-Attention Guided Multi-Task Learning Network for Automatic Gastric Tumor Segmentation and Lymph Node Classification. IEEE Trans Med Imaging 2021; 40:1618-1631. [PMID: 33646948 DOI: 10.1109/tmi.2021.3062902] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] [Imported: 08/14/2023]
Abstract
Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist radiologists in reading images, but also provide image-guided clinical diagnosis and improve diagnosis accuracy. However, due to the inhomogeneous intensity distribution of gastric tumor and LN in CT scans, the ambiguous/missing boundaries, and highly variable shapes of gastric tumor, it is quite challenging to develop an automatic solution. To comprehensively address these challenges, we propose a novel 3D multi-attention guided multi-task learning network for simultaneous gastric tumor segmentation and LN classification, which makes full use of the complementary information extracted from different dimensions, scales, and tasks. Specifically, we tackle task correlation and heterogeneity with the convolutional neural network consisting of scale-aware attention-guided shared feature learning for refined and universal multi-scale features, and task-aware attention-guided feature learning for task-specific discriminative features. This shared feature learning is equipped with two types of scale-aware attention (visual attention and adaptive spatial attention) and two stage-wise deep supervision paths. The task-aware attention-guided feature learning comprises a segmentation-aware attention module and a classification-aware attention module. The proposed 3D multi-task learning network can balance all tasks by combining segmentation and classification loss functions with weight uncertainty. We evaluate our model on an in-house CT images dataset collected from three medical centers. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, and obtains promising performance for tumor segmentation and LN classification. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge. Our implementation is released at https://github.com/infinite-tao/MA-MTLN.
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Li H, Liu B, Zhang Y, Fu C, Han X, Du L, Gao W, Chen Y, Liu X, Wang Y, Wang T, Ma G, Lei B. 3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor. Front Oncol 2021; 11:618496. [PMID: 34094903 PMCID: PMC8173118 DOI: 10.3389/fonc.2021.618496] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 04/21/2021] [Indexed: 11/24/2022] [Imported: 08/14/2023] Open
Abstract
Automatic segmentation of gastric tumor not only provides image-guided clinical diagnosis but also assists radiologists to read images and improve the diagnostic accuracy. However, due to the inhomogeneous intensity distribution of gastric tumors in CT scans, the ambiguous/missing boundaries, and the highly variable shapes of gastric tumors, it is quite challenging to develop an automatic solution. This study designs a novel 3D improved feature pyramidal network (3D IFPN) to automatically segment gastric tumors in computed tomography (CT) images. To meet the challenges of this extremely difficult task, the proposed 3D IFPN makes full use of the complementary information within the low and high layers of deep convolutional neural networks, which is equipped with three types of feature enhancement modules: 3D adaptive spatial feature fusion (ASFF) module, single-level feature refinement (SLFR) module, and multi-level feature refinement (MLFR) module. The 3D ASFF module adaptively suppresses the feature inconsistency in different levels and hence obtains the multi-level features with high feature invariance. Then, the SLFR module combines the adaptive features and previous multi-level features at each level to generate the multi-level refined features by skip connection and attention mechanism. The MLFR module adaptively recalibrates the channel-wise and spatial-wise responses by adding the attention operation, which improves the prediction capability of the network. Furthermore, a stage-wise deep supervision (SDS) mechanism and a hybrid loss function are also embedded to enhance the feature learning ability of the network. CT volumes dataset collected in three Chinese medical centers was used to evaluate the segmentation performance of the proposed 3D IFPN model. Experimental results indicate that our method outperforms state-of-the-art segmentation networks in gastric tumor segmentation. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge.
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Affiliation(s)
- Haimei Li
- Department of Radiology, Fuxing Hospital, Capital Medical University, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongtao Zhang
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Chao Fu
- Department of Radiology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Tianfu Wang
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Baiying Lei
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
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Zou Y, Zheng M, Qi Z, Guo Y, Ji X, Huang L, Gong Y, Lu X, Ma G, Xia S. Dual-energy computed tomography could reliably differentiate metastatic from non-metastatic lymph nodes of less than 0.5 cm in patients with papillary thyroid carcinoma. Quant Imaging Med Surg 2021; 11:1354-1367. [PMID: 33816174 DOI: 10.21037/qims-20-846] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] [Imported: 08/14/2023]
Abstract
Background Dual-energy computed tomography (DECT) has been widely applied to detect lymph node (LN) and lymph node metastasis (LNM) in various cancers, including papillary thyroid carcinoma (PTC). This study aimed to quantitatively evaluate metastatic cervical lymph nodes (LNs) <0.5 cm in patients with PTC using DECT, which has not been done in previous studies. Methods Preoperative DECT data of patients with pathologically confirmed PTC were retrospectively collected and analyzed between May 2016 and June 2018. A total of 359 LNs from 52 patients were included. Diameter, iodine concentration (IC), normalized iodine concentration (NIC), and the slope of the energy spectrum curve (λHU) of LNs in the arterial and the venous phases were compared between metastatic and non-metastatic LNs. The optimal parameters were obtained from the receiver operating characteristic (ROC) curves. The generalized estimation equation (GEE) model was used to evaluate independent diagnostic factors for LNM. Results A total of 139 metastatic and 220 non-metastatic LNs were analyzed. There were statistical differences of quantitative parameters between the two groups (P value 0.000-0.007). The optimal parameter for diagnosing LNM was IC in the arterial phase, and its area under the curve (AUC), sensitivity, and specificity were 0.775, 71.9%, and 73.6%, respectively. When the three parameters of diameter, IC in the arterial phase, and NIC in the venous phase were combined, the prediction efficiency was better, and the AUC was 0.819. The GEE results showed that LNs located in level VIa [odds ratio (OR) 2.030, 95% confidence interval (CI): 1.134-3.634, P=0.017], VIb (OR 2.836, 95% CI: 1.597-5.038, P=0.000), diameter (OR 2.023, 95% CI: 1.158-3.532, P=0.013), IC in the arterial phase (OR 4.444, 95% CI: 2.808-7.035, P=0.000), and IC in the venous phase (OR 5.387, 95% CI: 3.449-8.413, P=0.000) were independent risk factors for LNM in patients with PTC. Conclusions DECT had good diagnostic performance in the differentiation of cervical metastatic LNs <0.5 cm in patients with PTC.
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Affiliation(s)
- Ying Zou
- Radiological Department, First Central Clinical College, Tianjin Medical University, Tianjin, China.,Radiological Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Meizhu Zheng
- Radiological Department, Third Central Hospital of Tianjin, Tianjin, China
| | - Ziyu Qi
- Radiological Department, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Yu Guo
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Xiaodong Ji
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Lixiang Huang
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Yan Gong
- Radiological Department, First Central Clinical College, Tianjin Medical University, Tianjin, China.,Radiological Department, Tianjin Hospital of ITCWM Nankai Hospital, Tianjin, China
| | - Xiudi Lu
- Radiological Department, First Central Clinical College, Tianjin Medical University, Tianjin, China.,Radiological Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Guolin Ma
- Radiological Department, China-Japan Friendship Hospital, Beijing, China
| | - Shuang Xia
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
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Gao W, Li G, Han X, Song Z, Zhao S, Sun F, Ma H, Cui A, Wang Y, Liu X, Chen Y, Zhang L, Ma G, Tang X. Regional brain network and behavioral alterations in EGR3 gene transfected rat model of schizophrenia. Brain Imaging Behav 2021; 15:2606-2615. [PMID: 33723811 DOI: 10.1007/s11682-021-00462-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/31/2020] [Accepted: 06/10/2020] [Indexed: 12/25/2022] [Imported: 08/14/2023]
Abstract
Schizophrenia is a severe psychiatric disease while its etiology and effective treatment are not completely clear. A rat model of schizophrenia was previously established by transfecting EGR3 gene into the hippocampus of rats. This study aimed to investigate the behavioral and cerebral alterations of the schizophrenic model rats and the risperidone effects. Twenty-six rats were divided into 3 groups: schizophrenia model group (E group), risperidone treatment group (T group), and healthy control group (H group). Morris water maze and open field test were used as behavioral tests, resting-state functional magnetic resonance imaging (fMRI) was performed after EGR3 gene transfection and risperidone therapy. Graph analyses were used for examining cerebral alterations of the rats. Behavioral tests demonstrated reduced spatial working memory and exploring unfamiliar space ability in schizophrenic model rats. Graph analyses revealed reduced regional architectures in the olfactory bulb, nucleus accumbens, and pineal gland in group E compared to group H (p < 0.05), while group T showed increased regional architecture in pineal gland compared to group E (p < 0.05). Besides, the regional architectures in the olfactory bulb, nucleus accumbens were lower in group T than group H, while the hippocampus showed increased regional architecture in group T compared to group H (p < 0.05). Schizophrenia induced several regional alterations in the cerebrum while risperidone can reverse part of these alterations. This study lends support for future research on the pathology of schizophrenia and provides new insights on the role of risperidone in schizophrenia.
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Affiliation(s)
- Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
| | - Guangfei Li
- School of Life Science, Beijing Institute of Technology, No.5 Zhongguancun South Street, Haidian District, Beijing, 100081, China
| | - Xiaowei Han
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.,Graduate School of Peking Union Medical College, Beijing, 100006, China
| | - Zeyu Song
- School of Life Science, Beijing Institute of Technology, No.5 Zhongguancun South Street, Haidian District, Beijing, 100081, China
| | - Shuai Zhao
- Changzhi Medical College, Shanxi, 046000, China
| | - Feiyi Sun
- School of Life Science, Beijing Institute of Technology, No.5 Zhongguancun South Street, Haidian District, Beijing, 100081, China
| | - Hong Ma
- School of Life Science, Beijing Institute of Technology, No.5 Zhongguancun South Street, Haidian District, Beijing, 100081, China
| | - Ailing Cui
- Anatomy Department of Changzhi Medical College, Shanxi, 046000, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
| | - Lu Zhang
- Department of Science and Education, Shangluo Central Hospital, Shangluo, 726000, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, No.5 Zhongguancun South Street, Haidian District, Beijing, 100081, China.
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Yu H, Meng X, Chen H, Liu J, Gao W, Du L, Chen Y, Wang Y, Liu X, Liu B, Fan J, Ma G. Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features. Front Oncol 2021; 11:628577. [PMID: 33777776 PMCID: PMC7991288 DOI: 10.3389/fonc.2021.628577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 01/29/2021] [Indexed: 12/26/2022] [Imported: 08/14/2023] Open
Abstract
Objectives This study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer. Methods Data from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann–Whitney U test to evaluate the level of TILs in the low and high groups. Results Among the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738–0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615–0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively). Conclusion Radiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.
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Affiliation(s)
- Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xianqi Meng
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Huang Chen
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound medicine, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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Wang Y, Ma G, Zhang Y, Sheng L. Simulation and verification electrical properties of liquid metal flexible bioelectrodes. Microsyst Technol 2021; 27:673-679. [DOI: 10.1007/s00542-020-05044-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 09/22/2020] [Indexed: 08/14/2023] [Imported: 08/14/2023]
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41
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Liu X, Du L, Zhang B, Zhao Z, Gao W, Liu B, Liu J, Chen Y, Wang Y, Yu H, Ma G. Alterations and Associations Between Magnetic Susceptibility of the Basal Ganglia and Diffusion Properties in Alzheimer's Disease. Front Neurosci 2021; 15:616163. [PMID: 33664645 PMCID: PMC7921325 DOI: 10.3389/fnins.2021.616163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 01/12/2021] [Indexed: 11/28/2022] [Imported: 08/14/2023] Open
Abstract
This study adopted diffusion tensor imaging to detect alterations in the diffusion parameters of the white matter fiber in Alzheimer’s disease (AD) and used quantitative susceptibility mapping to detect changes in magnetic susceptibility. However, whether the changes of susceptibility values due to excessive iron in the basal ganglia have correlations with the alterations of the diffusion properties of the white matter in patients with AD are still unknown. We aim to investigate the correlations among magnetic susceptibility values of the basal ganglia, diffusion indexes of the white matter, and cognitive function in patients with AD. Thirty patients with AD and nineteen healthy controls (HCs) were recruited. Diffusion indexes of the whole brain were detected using tract-based spatial statistics. The caudate nucleus, putamen, and globus pallidus were selected as regions of interest, and their magnetic susceptibility values were measured. Compared with HCs, patients with AD showed that there were significantly increased axial diffusivity (AxD) in the internal capsule, superior corona radiata (SCR), and right anterior corona radiata (ACR); increased radial diffusivity (RD) in the right anterior limb of the internal capsule, ACR, and genu of the corpus callosum (GCC); and decreased fractional anisotropy (FA) in the right ACR and GCC. The alterations of RD values, FA values, and susceptibility values of the right caudate nucleus in patients with AD were correlated with cognitive scores. Besides, AxD values in the right internal capsule, ACR, and SCR were positively correlated with the magnetic susceptibility values of the right caudate nucleus in patients with AD. Our findings revealed that the magnetic susceptibility of the caudate nucleus may be an MRI-based biomarker of the cognitive dysfunction of AD and abnormal excessive iron distribution in the basal ganglia had adverse effects on the diffusion properties of the white matter.
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Affiliation(s)
- Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
| | - Zifang Zhao
- Department of Anesthesiology, Peking University First Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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Huang S, Han X, Fan J, Chen J, Du L, Gao W, Liu B, Chen Y, Liu X, Wang Y, Ai D, Ma G, Yang J. Anterior Mediastinal Lesion Segmentation Based on Two-Stage 3D ResUNet With Attention Gates and Lung Segmentation. Front Oncol 2021; 10:618357. [PMID: 33634027 PMCID: PMC7901488 DOI: 10.3389/fonc.2020.618357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 12/15/2020] [Indexed: 01/13/2023] [Imported: 08/14/2023] Open
Abstract
OBJECTIVES Anterior mediastinal disease is a common disease in the chest. Computed tomography (CT), as an important imaging technology, is widely used in the diagnosis of mediastinal diseases. Doctors find it difficult to distinguish lesions in CT images because of image artifact, intensity inhomogeneity, and their similarity with other tissues. Direct segmentation of lesions can provide doctors a method to better subtract the features of the lesions, thereby improving the accuracy of diagnosis. METHOD As the trend of image processing technology, deep learning is more accurate in image segmentation than traditional methods. We employ a two-stage 3D ResUNet network combined with lung segmentation to segment CT images. Given that the mediastinum is between the two lungs, the original image is clipped through the lung mask to remove some noises that may affect the segmentation of the lesion. To capture the feature of the lesions, we design a two-stage network structure. In the first stage, the features of the lesion are learned from the low-resolution downsampled image, and the segmentation results under a rough scale are obtained. The results are concatenated with the original image and encoded into the second stage to capture more accurate segmentation information from the image. In addition, attention gates are introduced in the upsampling of the network, and these gates can focus on the lesion and play a role in filtering the features. The proposed method has achieved good results in the segmentation of the anterior mediastinal. RESULTS The proposed method was verified on 230 patients, and the anterior mediastinal lesions were well segmented. The average Dice coefficient reached 87.73%. Compared with the model without lung segmentation, the model with lung segmentation greatly improved the accuracy of lesion segmentation by approximately 9%. The addition of attention gates slightly improved the segmentation accuracy. CONCLUSION The proposed automatic segmentation method has achieved good results in clinical data. In clinical application, automatic segmentation of lesions can assist doctors in the diagnosis of diseases and may facilitate the automated diagnosis of illnesses in the future.
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Affiliation(s)
- Su Huang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Jing Chen
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Danni Ai
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
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Du L, Zhao Z, Liu X, Chen Y, Gao W, Wang Y, Liu J, Liu B, Ma G. Alterations of Iron Level in the Bilateral Basal Ganglia Region in Patients With Middle Cerebral Artery Occlusion. Front Neurosci 2021; 14:608058. [PMID: 33551726 PMCID: PMC7859276 DOI: 10.3389/fnins.2020.608058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/17/2020] [Indexed: 11/27/2022] [Imported: 08/14/2023] Open
Abstract
Background and Purpose: The purpose of this study was to explore the changes of iron level using quantitative susceptibility mapping (QSM) in the bilateral basal ganglia region in middle cerebral artery occlusion (MCAO) patients with long-term ischemia. Methods: Twenty-seven healthy controls and nine patients with MCAO were recruited, and their QSM images were obtained. The bilateral caudate nucleus (Cd), putamen (Pt), and globus pallidus (Gp) were selected as the regions of interest (ROIs). Susceptibility values of bilateral ROIs were calculated and compared between the affected side and unaffected side in patients with MCAO and between patients with MCAO and healthy controls. In addition, receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic capability of susceptibility values in differentiating healthy controls and patients with MCAO by the area under the curve (AUC). Results: The susceptibility values of bilateral Cd were asymmetric in healthy controls; however, this asymmetry disappeared in patients with MCAO. In addition, compared with healthy controls, the average susceptibility values of the bilateral Pt in patients with MCAO were increased (P < 0.05), and the average susceptibility value of the bilateral Gp was decreased (P < 0.05). ROC curves showed that the susceptibility values of the Pt and Gp had a larger AUC (AUC = 0.700 and 0.889, respectively). Conclusion: As measured by QSM, the iron levels of the bilateral basal ganglia region were significantly changed in patients with MCAO. Iron dyshomeostasis in the basal ganglia region might be involved in the pathophysiological process of middle cerebral artery stenosis and occlusion. These findings may provide a novel insight to profoundly address the pathophysiological mechanisms of MCAO.
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Affiliation(s)
- Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zifang Zhao
- Department of Anesthesiology, Peking University First Hospital, Peking University, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Sheng L, Rao W, Zhou Z, Wu S, Ma G. Microwave-Induced Thermal Lesion Detection via Ultrasonic Scatterer Center Frequency Analysis with Autoregressive Cepstrum. Crit Rev Biomed Eng 2021; 48:85-93. [PMID: 33389897 DOI: 10.1615/critrevbiomedeng.2020033670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] [Imported: 08/14/2023]
Abstract
We proposed a new method for microwave-induced thermal lesion detection using the autoregressive spectrum analysis of ultrasonic backscattered signals in this paper. Eighteen cases of microwave ablation experiments and twenty cases of water bath heating experiments were conducted. Ultrasonic radiofrequency data of normal and coagulated porcine liver tissues were collected through these two experiments. Then, autoregressive spectrum analysis was performed; the mean frequency of the dominant peak in the autoregressive spectrum was computed based on water bath experiments; and a method for recognizing normal and solidified tissues was obtained by comparing the difference of the dominant peak in the autoregressive spectrum. Two bandpass finite impulse response filters, whose passbands corresponded respectively to the dominant peak in the autoregressive spectrum of normal and coagulated tissues, were used to compute the power spectral integration for the microwave-induced experiments. Microwave-induced thermal lesions were detected based on the differences between the power spectral integrations from the two filters. Compared to the caliper-measured area, the power spectral integration detected area had an error of (10.25 ± 3.59). Experimental results indicated that the proposed method may be used in preliminary detection of microwave-induced thermal lesions.
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Affiliation(s)
- Lei Sheng
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, China
| | - Wei Rao
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 29 Zhongguancun East Road, Haidian District, Beijing, 100190, PR China
| | - Zhuhuang Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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45
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Du L, Zhao Z, Xu B, Gao W, Liu X, Chen Y, Wang Y, Liu J, Liu B, Sun S, Ma G, Gao J. Anisotropy of Anomalous Diffusion Improves the Accuracy of Differentiating and Grading Alzheimer's Disease Using Novel Fractional Motion Model. Front Aging Neurosci 2020; 12:602510. [PMID: 33328977 PMCID: PMC7710869 DOI: 10.3389/fnagi.2020.602510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/19/2020] [Indexed: 11/13/2022] [Imported: 08/14/2023] Open
Abstract
Background and Purpose: Recent evidence shows that the fractional motion (FM) model may be a more appropriate model for describing the complex diffusion process of water in brain tissue and has shown to be beneficial in clinical applications of Alzheimer's disease (AD). However, the FM model averaged the anomalous diffusion parameter values, which omitted the impacts of anisotropy. This study aimed to investigate the potential feasibility of anisotropy of anomalous diffusion using the FM model for distinguishing and grading AD patients. Methods: Twenty-four patients with AD and 11 matched healthy controls were recruited, diffusion MRI was obtained from all participants and analyzed using the FM model. Generalized fractional anisotropy (gFA), an anisotropy metric, was introduced and the gFA values of FM-related parameters, Noah exponent (α) and the Hurst exponent (H), were calculated and compared between the healthy group and AD group and between the mild AD group and moderate AD group. The receiver-operating characteristic (ROC) analysis and the multivariate logistic regression analysis were used to assess the diagnostic performances of the anisotropy values and the directionally averaged values. Results: The gFA(α) and gFA(H) values of the moderate AD group were higher than those of the mild AD group in left hippocampus. The gFA(α) value of the moderate AD group was significantly higher than that of the healthy control group in both the left and right hippocampus. The gFA(ADC) values of the moderate AD group were significantly lower than those of the mild AD group and healthy control group in the right hippocampus. Compared with the gFA(α), gFA(H), α, and H, the ROC analysis showed larger areas under the curves for combination of α + gFA(α) and the combination of H + gFA(H) in differentiating the mild AD and moderate AD groups, and larger area under the curves for combination of α + gFA(α) in differentiating the healthy controls and AD groups. Conclusion: The anisotropy of anomalous diffusion could significantly differentiate and grade patients with AD, and the diagnostic performance was improved when the anisotropy metric was combined with commonly used directionally averaged values. The utility of anisotropic anomalous diffusion may provide novel insights to profoundly understand the neuropathology of AD.
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Affiliation(s)
- Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zifang Zhao
- Department of Anesthesiology, Peking University First Hospital, Peking University, Beijing, China
| | - Boyan Xu
- Beijing Intelligent Brain Cloud Inc., Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Shilong Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiahong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
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46
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Yang Q, Xu H, Tang X, Hu C, Wang P, Wáng YXJ, Wang Y, Ma G, Zhang B. Medical Imaging Engineering and Technology Branch of the Chinese Society of Biomedical Engineering expert consensus on the application of Emergency Mobile Cabin CT. Quant Imaging Med Surg 2020; 10:2191-2207. [PMID: 33139998 DOI: 10.21037/qims-20-980] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] [Imported: 08/14/2023]
Abstract
Started during December 2019, following the emergence of several COVID-19 cases in Wuhan City, Hubei Province, there was a rapid surge and spread of new COVID-19 cases throughout China. The disease has since been included in the Class B infectious diseases category, as stipulated in the Law of the People's Republic of China on the Prevention and Treatment of Infectious Diseases and shall be managed according to Class A infectious diseases. During the early phases of COVID-19 infection, no specific pulmonary imaging features may be evident, or features overlapping with other pneumonia may be observed. Although CT is not the gold standard for the diagnosis of COVID-19, it nonetheless is a convenient and fast method, and its application can be deployed in community hospitals. Furthermore, CT can be used to render a suggestive diagnosis and evaluate the severity as well as the effects of therapeutic interventions for typical cases of COVID-19. The mobile emergency special CT device described in this document (also known as Emergency Mobile Cabin CT) has several unique characteristics, including its mobility, flexibility, and networking capabilities. Furthermore, it adopts a fully independent isolation design to avoid cross-infection between patients and medical staff. It can play an important role in screening suspected cases presenting with imaging features of COVID-19 in hospitals of various levels that provide care to suspected or confirmed COVID-19 patients as part of the first line procedures of epidemic prevention and control.
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Affiliation(s)
- Qi Yang
- Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Haibo Xu
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Chunhong Hu
- The First Affiliated Hospital of Soochow University, Soochow, China
| | - Peijun Wang
- Tongji Hospital of Tongji University, Shanghai, China
| | - Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Yaofa Wang
- Minfound Medical Systems Co. Ltd, Shaoxing, China
| | - Guolin Ma
- China-Japan Friendship Hospital, Beijing, China
| | - Bing Zhang
- The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
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47
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Du L, Xu B, Zhao Z, Han X, Gao W, Shi S, Liu X, Chen Y, Wang Y, Sun S, Zhang L, Gao J, Ma G. Identification and Classification of Alzheimer's Disease Patients Using Novel Fractional Motion Model. Front Neurosci 2020; 14:767. [PMID: 33071719 PMCID: PMC7533574 DOI: 10.3389/fnins.2020.00767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 06/30/2020] [Indexed: 01/06/2023] [Imported: 08/14/2023] Open
Abstract
Most diffusion magnetic resonance imaging (dMRI) techniques use the mono-exponential model to describe the diffusion process of water in the brain. However, the observed dMRI signal decay curve deviates from the mono-exponential form. To solve this problem, the fractional motion (FM) model has been developed, which is regarded as a more appropriate model for describing the complex diffusion process in brain tissue. It is still unclear in the identification and classification of Alzheimer's disease (AD) patients using the FM model. The purpose of this study was to investigate the potential feasibility of FM model for differentiating AD patients from healthy controls and grading patients with AD. Twenty-four patients with AD and 11 healthy controls were included. The left and right hippocampus were selected as regions of interest (ROIs). The apparent diffusion coefficient (ADC) values and FM-related parameters, including the Noah exponent (α), the Hurst exponent (H), and the memory parameter (μ=H-1/α), were calculated and compared between AD patients and healthy controls and between mild AD and moderate AD patients using a two-sample t-test. The correlations between FM-related parameters α, H, μ, and ADC values and the cognitive functions assessed by mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scales were investigated using Pearson partial correlation analysis in patients with AD. The receiver-operating characteristic analysis was used to assess the differential performance. We found that the FM-related parameter α could be used to distinguish AD patients from healthy controls (P < 0.05) with greater sensitivity and specificity (left ROI, 0.917 and 0.636; right ROI, 0.917 and 0.727) and grade AD patients (P < 0.05) showed higher sensitivity and specificity (right ROI, 0.917, 0.75). The α was found to be positively correlated with MMSE (P < 0.05) and MoCA (P < 0.05) scores in patients with AD, indicating that the α values in the bilateral hippocampus were a potential MRI-based biomarker of disease severity in AD patients. This novel diffusion model may be useful for further understanding neuropathologic changes in patients with AD.
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Affiliation(s)
- Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Boyan Xu
- Beijing Intelligent Brain Cloud Inc., Beijing, China
| | - Zifang Zhao
- Department of Anesthesiology, Peking University First Hospital, Peking University, Beijing, China
| | - Xiaowei Han
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Sumin Shi
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Shilong Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Lu Zhang
- Department of Science and Education, Shangluo Central Hospital, Shangluo, China
| | - Jiahong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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48
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Gao Y, Sun R, Zhao M, Ding J, Wang A, Ye S, Zhang Y, Mao Q, Xie W, Ma G, Shi H. Sulfenic Acid-Mediated on-Site-Specific Immobilization of Mitochondrial-Targeted NIR Fluorescent Probe for Prolonged Tumor Imaging. Anal Chem 2020; 92:6977-6983. [PMID: 32314575 DOI: 10.1021/acs.analchem.9b05855] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] [Imported: 08/14/2023]
Abstract
Mitochondria plays pivotal roles in energy production and apoptotic pathways. Mitochondria-targeting strategy has been recognized as a promising way for cancer theranostics. Thus, spatiotemporally manipulating the prolonged retention of theranostic agents within mitochondria is considerably significant in cancer diagnosis and therapy. Herein, as a proof-of concept, we for the first time report a sulfenic acid-responsive platform on controlled immobilization of probes within mitochondria for prolonged tumor imaging. A novel near-infrared (NIR) probe DATC constructed with a NIR dye (Cy5) as signal unit, a cationic triphenylphosphonium (TPP) for mitochondria targeting, and a sulfenic acid-reactive group (1,3-cyclohexanedione) for mitochondrial fixation was rationally designed and synthesized. This probe displayed good target ability to mitochondria and could act as a promising fluorescent probe for specific visualization of endogenous protein sulfenic acids expressed in the mitochondria. Moreover, the probe could be spontaneously fixed on site through the specific reaction and covalent binding to the sulfenic acids of oxidized proteins under oxidative stress, resulting in enhanced intracellular uptake and prolonged retention. We thus believe that this mitochondria-targeted and locational immobilization strategy may offer a new insight for long-term tumor imaging and effective therapy.
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Affiliation(s)
- Yinjia Gao
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Rui Sun
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Meng Zhao
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Jianan Ding
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Anna Wang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Shuyue Ye
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Yuqi Zhang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Qiulian Mao
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
| | - Wei Xie
- Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, People's Republic of China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Haibin Shi
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, People's Republic of China
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49
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Yu H, Meng X, Chen H, Han X, Fan J, Gao W, Du L, Chen Y, Wang Y, Liu X, Zhang L, Ma G, Yang J. Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer. Front Oncol 2020; 10:412. [PMID: 32351879 PMCID: PMC7174560 DOI: 10.3389/fonc.2020.00412] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 03/09/2020] [Indexed: 12/16/2022] [Imported: 08/14/2023] Open
Abstract
Objectives: Tumor-infiltrating lymphocytes (TILs) have been identified as a significant prognostic indicator of response to neoadjuvant therapy and immunotherapy for triple-negative breast cancer (TNBC) patients. Herein, we aim to assess the association between TIL levels and mammographic features in TNBC patients. Methods: Forty-three patients with surgically proven TNBC who underwent preoperative mammography from January 2018 to December 2018 were recruited. Pyradiomics software was used to extract 204 quantitative radiomics features, including morphologic, grayscale, and textural features, from the segmented lesion areas. The correlation between radiological characteristics and TIL levels was evaluated by screening the most statistically significant radiological features using Mann–Whitney U-test and Pearson correlation coefficient. The patients were divided into two groups based on tumor TIL levels: patients with TIL levels <50% and those with TIL levels ≥50%. The correlation between TIL levels and clinicopathological characteristics was assessed using the chi-square test or Fisher's exact test. Mann–Whitney U-test and Pearson correlation coefficient were used to analyze the statistical significance and Pearson correlation coefficient of clinical pathological features, age, and radiological features. Results: Of 43 patients, 32 (74.4%) had low TIL levels and 11 (25.6%) had high TIL levels. The histological grade of the low TIL group was higher than that of the high TIL group (p = 0.043). The high TIL group had a more negative threshold Ki-67 level (<14%) than the low TIL group (p = 0.017). The six most important radiomics features [uniformity, variance, grayscale symbiosis matrix (GLCM) correlation, GLCM autocorrelation, gray level difference matrix (GLDM) low gray level emphasis, and neighborhood gray-tone difference matrix (NGTDM) contrast], representing qualitative mammographic image characteristics, were statistically different (p < 0.05) among the low and high TIL groups. Tumors in the high TIL group had a more non-uniform density and a smoother gradient of the tumor pattern than the low TIL group. The changes in Ki-67, age, epidermal growth factor receptor, radiomic characteristics, and Pearson correlation coefficient were statistically significant (p < 0.05). Conclusion: Mammography features not only distinguish high and low TIL levels in TNBC patients but also can act as imaging biomarkers to enhance diagnosis and the response of patients to neoadjuvant therapies and immunotherapies.
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Affiliation(s)
- Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xianqi Meng
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Huang Chen
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Xiaowei Han
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Lu Zhang
- Department of Science and Education, Shangluo Central Hospital, Shangluo, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
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50
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Li G, Han X, Gao W, Song Z, Zhao S, Sun F, Ma H, Cui A, Tang X, Ma G. Influence of EGR3 Transfection on Imaging and Behavior in Rats and Therapeutic Effect of Risperidone in Schizophrenia Model. Front Psychiatry 2020; 11:00787. [PMID: 33192626 PMCID: PMC7542223 DOI: 10.3389/fpsyt.2020.00787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/22/2020] [Indexed: 01/02/2023] [Imported: 08/14/2023] Open
Abstract
Schizophrenia is a type of neurodevelopmental psychiatric disorder. However, to date, scientists have not discovered the etiology and effective treatment of this condition. We injected the early growth response gene (EGR3) into the bilateral hippocampus to build a schizophrenia rat model. Behavioral phenotyping and resting-state functional magnetic resonance imaging (rs-fMRI) were used to analyze the behavioral and cerebral alterations in the schizophrenia rat model. The efficacy of risperidone therapy was also evaluated. We divided 34 rats into four groups: schizophrenia model group (E group), sham-operation group (FE group), healthy control group (H group), and risperidone therapy group (T group). Open field test and Morris water maze were conducted as behavioral experiments. Next, we performed rs-fMRI after four weeks of EGR3 transfection and risperidone treatment and analyzed imaging data using regional homogeneity (ReHo), the amplitude of low-frequency fluctuations (ALFF), and functional connectivity (FC). We examined the difference in behavioral and neural activation among the four groups and considered the correlations between behavior and imaging results. EGR3 gene transfection decreased the total moved distance in the open field test and the duration in the Q5 zone of the Morris water maze. Risperidone treatment reversed the trend and improved the performance of rats in these behavioral tests. Schizophrenia induced several neural alterations in ALFF and ReHo metrics of the rat brain, and risperidone could partly reverse these alterations. The results suggest that similar research is required for schizophrenia and that risperidone may be a novel treatment for dysregulated neural activation in schizophrenia.
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Affiliation(s)
- Guangfei Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Xiaowei Han
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School, Peking Union Medical College, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Zeyu Song
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Shuai Zhao
- Changzhi Medical College, Changzhi, China
| | - Feiyi Sun
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Hong Ma
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Ailing Cui
- Anatomy Department, Changzhi Medical College, Changzhi, China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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