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Vieites V, Ralph Y, Reeb-Sutherland B, Dick AS, Mattfeld AT, Pruden SM. Neurite density of the hippocampus is associated with trace eyeblink conditioning latency in 4- to 6-year-olds. Eur J Neurosci 2024; 59:358-369. [PMID: 38092417 PMCID: PMC10872972 DOI: 10.1111/ejn.16217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 02/06/2024]
Abstract
Limited options exist to evaluate the development of hippocampal function in young children. Research has established that trace eyeblink conditioning (EBC) relies on a functional hippocampus. Hence, we set out to investigate whether trace EBC is linked to hippocampal structure, potentially serving as a valuable indicator of hippocampal development. Our study explored potential associations between individual differences in hippocampal volume and neurite density with trace EBC performance in young children. We used onset latency of conditioned responses (CR) and percentage of conditioned responses (% CR) as measures of hippocampal-dependent associative learning. Using a sample of typically developing children aged 4 to 6 years (N = 30; 14 girls; M = 5.70 years), participants underwent T1- and diffusion-weighted MRI scans and completed a 15-min trace eyeblink conditioning task conducted outside the MRI. % CR and CR onset latency were calculated based on all trials involving tone-puff presentations and tone-alone trials. Findings revealed a connection between greater left hippocampal neurite density and delayed CR onset latency. Children with higher neurite density in the left hippocampus tended to blink closer to the onset of the unconditioned stimulus, indicating that structural variations in the hippocampus were associated with more precise timing of conditioned responses. No other relationships were observed between hippocampal volume, cerebellum volume or neurite density, hippocampal white matter connectivity and any EBC measures. Preliminary results suggest that trace EBC may serve as a straightforward yet innovative approach for studying hippocampal development in young children and populations with atypical development.
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Affiliation(s)
- Vanessa Vieites
- Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Yvonne Ralph
- Department of Psychology, University of Texas at Tyler, Tyler, Texas, USA
| | | | - Anthony Steven Dick
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Aaron T Mattfeld
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Shannon M Pruden
- Department of Psychology, Florida International University, Miami, Florida, USA
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Zhao X, Hu X, Guo Z, Hu W, Zhang C, Mo J, Zhang K. Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex. J Clin Med 2024; 13:680. [PMID: 38337374 PMCID: PMC10856546 DOI: 10.3390/jcm13030680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/22/2023] [Accepted: 01/17/2024] [Indexed: 02/12/2024] Open
Abstract
The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation (p = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.
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Affiliation(s)
- Xuemin Zhao
- Department of Neurophysiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100071, China;
| | - Xu Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
- Department of Neurosurgery, Wuxi Taihu Hospital, Wuxi Clinical College of Anhui Medical University, Wuxi 214000, China
| | - Zhihao Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
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Wang Z, Li Y, He Z, Li S, Huang K, Shi X, Sun X, Ruan R, Cui C, Wang R, Wang L, Lv S, Zhang C, Liu Z, Yang H, Yang X, Liu S. Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study. BMC Med 2023; 21:500. [PMID: 38110931 PMCID: PMC10729377 DOI: 10.1186/s12916-023-03121-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 10/19/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images. METHODS Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis. RESULTS PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70-0.83) than the LDA (AUC = 0.7506; 95% CI 0.68-0.82) and ANN models (AUC = 0.7425; 95% CI 0.67-0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71-0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71-0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74-0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75-0.87). CONCLUSIONS The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients.
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Affiliation(s)
- Zhongke Wang
- Department of Neurosurgery, Armed Police Hospital of Chongqing, Chongqing, China
| | - Yang Li
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Zeng He
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Shujing Li
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Kaixuan Huang
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Xianjun Shi
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Xiaoqin Sun
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Ruotong Ruan
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Chun Cui
- Department of Radiology, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Ruodan Wang
- Department of Neurology, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Li Wang
- Department of Neurology, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Shengqing Lv
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Chunqing Zhang
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China
- Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing, China
| | - Zhonghong Liu
- Department of Neurosurgery, Armed Police Hospital of Chongqing, Chongqing, China
| | - Hui Yang
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China.
- Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing, China.
| | - Xiaolin Yang
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China.
| | - Shiyong Liu
- Department of Neurosurgery, Comprehensive Epilepsy Center, Xinqiao Hospital, Army Medical University, Chongqing, China.
- Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing, China.
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DiPiero M, Rodrigues PG, Gromala A, Dean DC. Applications of advanced diffusion MRI in early brain development: a comprehensive review. Brain Struct Funct 2023; 228:367-392. [PMID: 36585970 PMCID: PMC9974794 DOI: 10.1007/s00429-022-02605-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023]
Abstract
Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.
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Affiliation(s)
- Marissa DiPiero
- Department of Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Alyssa Gromala
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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5
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Nie JZ, Karras CL, Trybula SJ, Texakalidis P, Alden TD. The role of neurosurgery in the management of tuberous sclerosis complex–associated epilepsy: a systematic review. Neurosurg Focus 2022; 52:E6. [DOI: 10.3171/2022.2.focus21789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/21/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE
Tuberous sclerosis complex (TSC) is an autosomal dominant, multisystem neurocutaneous disorder associated with cortical tubers, brain lesions seen in nearly all patients with TSC, which are frequently epileptogenic. Seizures are often the earliest clinical manifestation of TSC, leading to epilepsy in over 70% of patients. Medical management with antiepileptic drugs constitutes early therapy, but over 50% develop medically refractory epilepsy, necessitating surgical evaluation and treatment. The objective of this study was to summarize the literature and report seizure outcomes following surgical treatment for TSC-associated epilepsy.
METHODS
A systematic literature review was performed in accordance with the PRISMA guidelines. The PubMed and Embase databases were searched for journal articles reporting seizure outcomes following epilepsy surgery in TSC patients. Included studies were placed into one of two groups based on the surgical technique used. Excellent and worthwhile seizure reductions were defined for each group as outcomes and extracted from each study.
RESULTS
A total of 46 studies were included. Forty of these studies reported seizure outcomes following any combination of resection, disconnection, and ablation on a collective 1157 patients. Excellent and worthwhile seizure reductions were achieved in 59% (683/1157) and 85% (450/528) of patients, respectively. Six of these studies reported seizure outcomes following treatment with neuromodulation. Excellent and worthwhile seizure reductions were achieved in 34% (24/70) and 76% (53/70) of patients, respectively.
CONCLUSIONS
Surgery effectively controls seizures in select patients with TSC-associated epilepsy, but outcomes vary. Further understanding of TSC-associated epilepsy, improving localization strategies, and emerging surgical techniques represent promising avenues for improving surgical outcomes.
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Affiliation(s)
- Jeffrey Z. Nie
- Southern Illinois University School of Medicine, Springfield, Illinois
| | - Constantine L. Karras
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois; and
| | - S. Joy Trybula
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois; and
| | - Pavlos Texakalidis
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois; and
| | - Tord D. Alden
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois; and
- Division of Pediatric Neurosurgery, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
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6
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Hare MM, Dick AS, Graziano PA. Adverse childhood experiences predict neurite density differences in young children with and without attention deficit hyperactivity disorder. Dev Psychobiol 2022; 64:e22234. [PMID: 35050509 PMCID: PMC8827844 DOI: 10.1002/dev.22234] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 11/09/2021] [Accepted: 11/28/2021] [Indexed: 01/03/2023]
Abstract
Adverse childhood experiences (ACEs) put millions of children at risk for later health problems. As childhood represents a critical developmental period, it is important to understand how ACEs impact brain development in young children. In addition, children with attention-deficit/hyperactivity disorder (ADHD) are more likely than typically developing (TD) peers to experience ACEs. Therefore, the current study examined the impact of ACEs on early brain development, using a cumulative risk approach, in a large sample of children with and without ADHD. We examined 198 young children (Mage = 5.45, 82.3% Hispanic/Latino; 52.5% ADHD) across measures of brain volume, cortical thickness, neurite density index (NDI), and orientation dispersion index (ODI). For the NDI measure, there was a significant interaction between group and cumulative risk (ß = .18, p = .048), such that for children with ADHD, but not TD children, greater cumulate risk was associated with increased NDI in corpus callosum. No other interactions were detected. Additionally, when examining across groups, greater cumulative risk was associated with reduced ODI and volume in the cerebellum, although these findings did not survive a correction for multiple comparisons. Our results highlight the role early cumulative ACEs play in brain development across TD and children with ADHD.
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Affiliation(s)
- Megan M. Hare
- Center for Children and Families, Department of Psychology, Florida International University, Miami, FL 33199, USA
| | - Anthony Steven Dick
- Center for Children and Families, Department of Psychology, Florida International University, Miami, FL 33199, USA
| | - Paulo A. Graziano
- Center for Children and Families, Department of Psychology, Florida International University, Miami, FL 33199, USA
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7
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Si X, Zhang X, Zhou Y, Chao Y, Lim SN, Sun Y, Yin S, Jin W, Zhao X, Li Q, Ming D. White matter structural connectivity as a biomarker for detecting juvenile myoclonic epilepsy by transferred deep convolutional neural networks with varying transfer rates. J Neural Eng 2021; 18. [PMID: 34507303 DOI: 10.1088/1741-2552/ac25d8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/10/2021] [Indexed: 11/12/2022]
Abstract
Objective. By detecting abnormal white matter changes, diffusion magnetic resonance imaging (MRI) contributes to the detection of juvenile myoclonic epilepsy (JME). In addition, deep learning has greatly improved the detection performance of various brain disorders. However, there is almost no previous study effectively detecting JME by a deep learning approach with diffusion MRI.Approach. In this study, the white matter structural connectivity was generated by tracking the white matter fibers in detail based on Q-ball imaging and neurite orientation dispersion and density imaging. Four advanced deep convolutional neural networks (CNNs) were deployed by using the transfer learning approach, in which the transfer rate searching strategy was proposed to achieve the best detection performance.Main results. Our results showed: (a) Compared to normal control, the white matter' neurite density of JME was significantly decreased. The most significantly abnormal fiber tracts between the two groups were found to be cortico-cortical connection tracts. (b) The proposed transfer rate searching approach contributed to find each CNN's best performance, in which the best JME detection accuracy of 92.2% was achieved by using the Inception_resnet_v2 network with a 16% transfer rate.Significance. The results revealed: (a) Through detection of the abnormal white matter changes, the white matter structural connectivity can be used as a useful biomarker for detecting JME, which helps to characterize the pathophysiology of epilepsy. (b) The proposed transfer rate, as a new hyperparameter, promotes the CNNs transfer learning performance in detecting JME.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, People's Republic of China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yiping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Siew-Na Lim
- Department of Neurology, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Shaoya Yin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weipeng Jin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xin Zhao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
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8
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Li Y, Liu H, Li T, Feng J, He Y, Chen L, Li C, Qiu X. Choroid Plexus Carcinomas With TP53 Germline Mutations: Management and Outcome. Front Oncol 2021; 11:751784. [PMID: 34660315 PMCID: PMC8514937 DOI: 10.3389/fonc.2021.751784] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 08/30/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Choroid plexus carcinomas (CPCs) are rare pediatric tumors commonly associated with Li-Fraumeni syndrome (LFS), which involves a germline mutation of the tumor suppressor gene TP53. MATERIALS AND METHODS We retrospectively analyzed the corresponding information of 12 cases, including the effects of surgery and radiotherapy and TP53 germline mutations, to analyse the management strategies. Kaplan-Meier curves and the log-rank test were used to evaluate the progression-free survival (PFS). RESULTS Twelve CPC patients were included, of which TP53 germline mutations were found in eight cases. All patients underwent surgical resection, and six patients received radiotherapy following with operation after initial diagnosis, one patient received radiotherapy following relapse. It was significantly different (P=0.012 and 0.028) that patients with TP53 germline mutation receiving the gross total resection (GTR) without radiotherapy showed survival advantages. Without TP53 germline mutations also showed survival advantages, but there is no statistical significance (P=0.063). CONCLUSIONS These findings provide evidence for the therapeutic strategy that radiotherapy should not be considered for patients with TP53 germline mutations.
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Affiliation(s)
- Yanong Li
- Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hailong Liu
- Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tandy Li
- Departments of Pharmacy, New York Presbyterian Lower Manhattan Hospital, New York, NY, United States
| | - Jin Feng
- Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yanjiao He
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Li Chen
- Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunde Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoguang Qiu
- Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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9
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Sone D. Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy. Front Neurosci 2021; 15:699176. [PMID: 34385902 PMCID: PMC8353251 DOI: 10.3389/fnins.2021.699176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/28/2021] [Indexed: 12/30/2022] Open
Abstract
It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
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