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Leung IHK, Strudwick MW. A systematic review of the challenges, emerging solutions and applications, and future directions of PET/MRI in Parkinson's disease. EJNMMI REPORTS 2024; 8:3. [PMID: 38748251 PMCID: PMC10962627 DOI: 10.1186/s41824-024-00194-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/26/2023] [Indexed: 05/19/2024]
Abstract
PET/MRI is a hybrid imaging modality that boasts the simultaneous acquisition of high-resolution anatomical data and metabolic information. Having these exceptional capabilities, it is often implicated in clinical research for diagnosing and grading, as well as tracking disease progression and response to interventions. Despite this, its low level of clinical widespread use is questioned. This is especially the case with Parkinson's disease (PD), the fastest progressively disabling and neurodegenerative cause of death. To optimise the clinical applicability of PET/MRI for diagnosing, differentiating, and tracking PD progression, the emerging novel uses, and current challenges must be identified. This systematic review aimed to present the specific challenges of PET/MRI use in PD. Further, this review aimed to highlight the possible resolution of these challenges, the emerging applications and future direction of PET/MRI use in PD. EBSCOHost (indexing CINAHL Plus, PsycINFO) Ovid (Medline, EMBASE) PubMed, Web of Science, and Scopus from 2006 (the year of first integrated PET/MRI hybrid system) to 30 September 2022 were used to search for relevant primary articles. A total of 933 studies were retrieved and following the screening procedure, 18 peer-reviewed articles were included in this review. This present study is of great clinical relevance and significance, as it informs the reasoning behind hindered widespread clinical use of PET/MRI for PD. Despite this, the emerging applications of image reconstruction developed by PET/MRI research data to the use of fully automated systems show promising and desirable utility. Furthermore, many of the current challenges and limitations can be resolved by using much larger-sampled and longitudinal studies. Meanwhile, the development of new fast-binding tracers that have specific affinity to PD pathological processes is warranted.
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Sun J, Cong C, Li X, Zhou W, Xia R, Liu H, Wang Y, Xu Z, Chen X. Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics. Eur Radiol 2024; 34:662-672. [PMID: 37535155 DOI: 10.1007/s00330-023-10003-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 05/08/2023] [Accepted: 06/06/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics. METHODS One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models. RESULTS The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model. CONCLUSIONS Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics. CLINICAL RELEVANCE STATEMENT This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance. KEY POINTS •Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.
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Affiliation(s)
- Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Chao Cong
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
- School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China
| | - Xinpeng Li
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | | | - Yi Wang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Zhiqiang Xu
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China.
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
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Savoie FA, Arpin DJ, Vaillancourt DE. Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here? Curr Neuropharmacol 2024; 22:1583-1605. [PMID: 37533246 DOI: 10.2174/1570159x21666230801140648] [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: 08/10/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 08/04/2023] Open
Abstract
Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.
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Affiliation(s)
- Félix-Antoine Savoie
- Department of Applied Physiology and Kinesiology, Laboratory for Rehabilitation Neuroscience, University of Florida, Gainesville, FL, USA
| | - David J Arpin
- Department of Applied Physiology and Kinesiology, Laboratory for Rehabilitation Neuroscience, University of Florida, Gainesville, FL, USA
| | - David E Vaillancourt
- Department of Applied Physiology and Kinesiology, Laboratory for Rehabilitation Neuroscience, University of Florida, Gainesville, FL, USA
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
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Strobel J, Müller HP, Ludolph AC, Beer AJ, Sollmann N, Kassubek J. New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review. Cells 2023; 12:2776. [PMID: 38132096 PMCID: PMC10742083 DOI: 10.3390/cells12242776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.
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Affiliation(s)
- Joachim Strobel
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Hans-Peter Müller
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
| | - Albert C. Ludolph
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
| | - Ambros J. Beer
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, 89081 Ulm, Germany;
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Jan Kassubek
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
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Andersen AM, Kaalund SS, Marner L, Salvesen L, Pakkenberg B, Olesen MV. Quantitative cellular changes in multiple system atrophy brains. Neuropathol Appl Neurobiol 2023; 49:e12941. [PMID: 37812040 DOI: 10.1111/nan.12941] [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: 03/31/2023] [Revised: 09/21/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
Multiple system atrophy (MSA) is a neurodegenerative disorder characterised by a combined symptomatology of parkinsonism, cerebellar ataxia, autonomic failure and corticospinal dysfunction. In brains of MSA patients, the hallmark lesion is the aggregation of misfolded alpha-synuclein in oligodendrocytes. Even though the underlying pathological mechanisms remain poorly understood, the evidence suggests that alpha-synuclein aggregation in oligodendrocytes may contribute to the neurodegeneration seen in MSA. The primary aim of this review is to summarise the published stereological data on the total number of neurons and glial cell subtypes (oligodendrocytes, astrocytes and microglia) and volumes in brains from MSA patients. Thus, we include in this review exclusively the reports of unbiased quantitative data from brain regions including the neocortex, nuclei of the cerebrum, the brainstem and the cerebellum. Furthermore, we compare and discuss the stereological results in the context of imaging findings and MSA symptomatology. In general, the stereological results agree with the common neuropathological findings of neurodegeneration and gliosis in brains from MSA patients and support a major loss of nigrostriatal neurons in MSA patients with predominant parkinsonism (MSA-P), as well as olivopontocerebellar atrophy in MSA patients with predominant cerebellar ataxia (MSA-C). Surprisingly, the reports indicate only a minor loss of oligodendrocytes in sub-cortical regions of the cerebrum (glial cells not studied in the cerebellum) and negligible changes in brain volumes. In the past decades, the use of stereological methods has provided a vast amount of accurate information on cell numbers and volumes in the brains of MSA patients. Combining different techniques such as stereology and diagnostic imaging (e.g. MRI, PET and SPECT) with clinical data allows for a more detailed interdisciplinary understanding of the disease and illuminates the relationship between neuropathological changes and MSA symptomatology.
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Affiliation(s)
- Alberte M Andersen
- Centre for Neuroscience and Stereology, Department of Neurology, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
| | - Sanne S Kaalund
- Centre for Neuroscience and Stereology, Department of Neurology, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
| | - Lisbeth Marner
- Department of Clinical Physiology and Nuclear Medicine, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lisette Salvesen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Neurology, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
| | - Bente Pakkenberg
- Centre for Neuroscience and Stereology, Department of Neurology, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mikkel V Olesen
- Centre for Neuroscience and Stereology, Department of Neurology, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
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6
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Buchert R, Wegner F, Huppertz HJ, Berding G, Brendel M, Apostolova I, Buhmann C, Dierks A, Katzdobler S, Klietz M, Levin J, Mahmoudi N, Rinscheid A, Rogozinski S, Rumpf JJ, Schneider C, Stöcklein S, Spetsieris PG, Eidelberg D, Wattjes MP, Sabri O, Barthel H, Höglinger G. Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy. Mov Disord 2023; 38:1901-1913. [PMID: 37655363 DOI: 10.1002/mds.29581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS). OBJECTIVES To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice. METHODS This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern. RESULTS Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample. CONCLUSIONS Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florian Wegner
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | | | - Georg Berding
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexander Dierks
- Department of Nuclear Medicine, University Hospital Augsburg, Augsburg, Germany
| | - Sabrina Katzdobler
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Martin Klietz
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Nima Mahmoudi
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Andreas Rinscheid
- Medical Physics and Radiation Protection, University Hospital Augsburg, Augsburg, Germany
| | | | | | - Christine Schneider
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany
| | - Sophia Stöcklein
- Department of Radiology, University Hospital of Munich, LMU, Munich, Germany
| | - Phoebe G Spetsieris
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - David Eidelberg
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Günter Höglinger
- Department of Neurology, Hannover Medical School, Hannover, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
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Wan L, Zhu S, Chen Z, Qiu R, Tang B, Jiang H. Multidimensional biomarkers for multiple system atrophy: an update and future directions. Transl Neurodegener 2023; 12:38. [PMID: 37501056 PMCID: PMC10375766 DOI: 10.1186/s40035-023-00370-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/11/2023] [Indexed: 07/29/2023] Open
Abstract
Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.
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Affiliation(s)
- Linlin Wan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National International Collaborative Research Center for Medical Metabolomics, Central South University, Changsha, 410008, China
| | - Sudan Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Zhao Chen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, 410008, China
| | - Rong Qiu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, 410008, China
| | - Hong Jiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China.
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, 410008, China.
- National International Collaborative Research Center for Medical Metabolomics, Central South University, Changsha, 410008, China.
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Carli G, Meles SK, Reesink FE, de Jong BM, Pilotto A, Padovani A, Galbiati A, Ferini-Strambi L, Leenders KL, Perani D. Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies. Neuroimage Clin 2023; 39:103475. [PMID: 37494757 PMCID: PMC10394024 DOI: 10.1016/j.nicl.2023.103475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/18/2023] [Accepted: 07/09/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies. METHODS We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA). RESULTS The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941). CONCLUSIONS SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.
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Affiliation(s)
- Giulia Carli
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sanne K Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Fransje E Reesink
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bauke M de Jong
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Andrea Galbiati
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy
| | - Luigi Ferini-Strambi
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy
| | - Klaus L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Daniela Perani
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan; Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.
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Bian J, Wang X, Hao W, Zhang G, Wang Y. The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis. Front Aging Neurosci 2023; 15:1199826. [PMID: 37484694 PMCID: PMC10357514 DOI: 10.3389/fnagi.2023.1199826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Background In recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD. Methods We systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson's disease and various atypical parkinsonism syndromes (APS). Results Twenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833-0.891), 0.91 (95% CI: 0.86-0.94), and 0.93 (95% CI: 0.87-0.96) in the training set, and 0.871 (95% CI: 0.853-0.890), 0.86 (95% CI: 0.81-0.89), and 0.87 (95% CI: 0.83-0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843-0.889), 0.86 (95% CI: 0.84-0.88), and 0.80 (95% CI: 0.75-0.84) in the training set, and 0.879 (95% CI: 0.854-0.903), 0.87 (95% CI: 0.85-0.89), and 0.82 (95% CI: 0.77-0.86) in the validation set, respectively. Conclusion Radiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson's disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson's disease and related fields. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197.
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Affiliation(s)
- Jiaxiang Bian
- School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Xiaoyang Wang
- School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Wei Hao
- School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Guangjian Zhang
- Department of Neurosurgery, Weifang People’s Hospital, Weifang, China
| | - Yuting Wang
- Department of Neurosurgery, Weifang People’s Hospital, Weifang, China
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Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [ 18F]FDG PET imaging. Eur Radiol 2022; 32:8008-8018. [PMID: 35674825 DOI: 10.1007/s00330-022-08799-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/27/2022] [Accepted: 04/01/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [18F]fluorodeoxyglucose (FDG) PET images. METHODS In this two-center study, 255 normal controls (NCs) and 103 PD patients were enrolled from Huashan Hospital, China; 26 NCs and 22 PD patients were enrolled as a separate test group from Wuxi 904 Hospital, China. The proposed DLR model consisted of a convolutional neural network-based feature encoder and a support vector machine (SVM) model-based classifier. The DLR model was trained and validated in the Huashan cohort and tested in the Wuxi cohort, and accuracy, sensitivity, specificity and receiver operator characteristic (ROC) curve graphs were used to describe the model's performance. Comparative experiments were performed based on four other models including the scale model, radiomics model, standard uptake value ratio (SUVR) model and DLR model. RESULTS The DLR model demonstrated superiority in differentiating PD patients and NCs in comparison to other models, with an accuracy of 95.17% [90.35%, 98.13%] (95% confidence intervals, CI) in the Huashan cohort. Moreover, the DLR model also demonstrated greater performance in diagnosing PD early than routine methods, with an accuracy of 85.58% [78.60%, 91.57%] in the Huashan cohort. CONCLUSIONS We developed a DLR model based on [18F]FDG PET images that showed good performance in the noninvasive, individualized prediction of PD and was superior to traditional handcrafted methods. This model has the potential to guide and facilitate clinical diagnosis and contribute to the development of precision treatment. KEY POINTS The DLR method on [18F]FDG PET images helps clinicians to diagnose PD and PD subgroups from normal controls. A prospective two-center study showed that the DLR method provides greater diagnostic accuracy.
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Shin HW, Hong SW, Youn YC. Clinical Aspects of the Differential Diagnosis of Parkinson's Disease and Parkinsonism. J Clin Neurol 2022; 18:259-270. [PMID: 35589315 PMCID: PMC9163948 DOI: 10.3988/jcn.2022.18.3.259] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/14/2022] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
Parkinsonism is a clinical syndrome presenting with bradykinesia, tremor, rigidity, and postural instability. Nonmotor symptoms have recently been included in the parkinsonian syndrome, which was traditionally associated with motor symptoms only. Various pathologically distinct and unrelated diseases have the same clinical manifestations as parkinsonism or parkinsonian syndrome. The etiologies of parkinsonism are classified as neurodegenerative diseases related to the accumulation of toxic protein molecules or diseases that are not neurodegenerative. The former class includes Parkinson's disease (PD), multiple-system atrophy, progressive supranuclear palsy, and corticobasal degeneration. Over the past decade, clinical diagnostic criteria have been validated and updated to improve the accuracy of diagnosing these diseases. The latter class of disorders unrelated to neurodegenerative diseases are classified as secondary parkinsonism, and include drug-induced parkinsonism (DIP), vascular parkinsonism, and idiopathic normal-pressure hydrocephalus (iNPH). DIP and iNPH are regarded as reversible and treatable forms of parkinsonism. However, studies have suggested that the absence of protein accumulation in the nervous system as well as managing the underlying causes do not guarantee recovery. Here we review the differential diagnosis of PD and parkinsonism, mainly focusing on the clinical aspects. In addition, we describe recent updates to the clinical criteria of various disorders sharing clinical symptoms with parkinsonism.
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Affiliation(s)
- Hae-Won Shin
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Sang-Wook Hong
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea.
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Amod FH, Bhigjee AI, Nyakale N. Utility of 18F FDG-PET in Parkinsonism in an African population. eNeurologicalSci 2022; 27:100399. [PMID: 35434388 PMCID: PMC9011012 DOI: 10.1016/j.ensci.2022.100399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/21/2022] [Accepted: 03/28/2022] [Indexed: 11/18/2022] Open
Affiliation(s)
- Ferzana Hassan Amod
- Department of Neurology, Inkosi Albert Luthuli Central Hospital, Cato Manor, Durban, South Africa
- Department of Neurology, University of KwaZulu-Natal, Durban, South Africa
- Corresponding author at: Department of Neurology, Inkosi Albert Luthuli Central Hospital, Cato Manor, Durban, South Africa.
| | - Ahmed Iqbal Bhigjee
- Department of Neurology, Inkosi Albert Luthuli Central Hospital, Cato Manor, Durban, South Africa
- Department of Neurology, University of KwaZulu-Natal, Durban, South Africa
| | - Nozipho Nyakale
- Department of Nuclear Medicine, Inkosi Albert Luthuli Central Hospital, Cato Manor, Durban, South Africa, Head of Nuclear Medicine Department, Sefako Makgatho Health Sciences University, Ga-Rankuwa, South Africa
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Abstract
Positron emission tomography greatly advanced our understanding on the underlying neural mechanisms of movement disorders. PET with flurodeoxyglucose (FDG) is especially useful as it depicts regional metabolic activity level that can predict patients' symptoms. Multivariate pattern analysis has been used to determine and quantify the co-varying brain networks associated with specific clinical traits of neurodegenerative disease. The result is a biomarker, useful for diagnosis, treatments, and follow up studies. Parkinsonian traits and parkinsonisms are associated with specific spatial pattern of metabolic abnormality useful for differential diagnosis. This approach has also been used for monitoring disease progression and novel treatment responses mostly in Parkinson's disease. In this book chapter, we, illustrate and discuss the significance of the brain networks associated with disease and their modification with neuroplastic changes.
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Hu X, Sun X, Hu F, Liu F, Ruan W, Wu T, An R, Lan X. Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy. Eur J Nucl Med Mol Imaging 2021; 48:3469-3481. [PMID: 33829415 DOI: 10.1007/s00259-021-05325-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/20/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To construct multivariate radiomics models using hybrid 18F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS Ninety patients (60 with PD and 30 with MSA) were randomized to training and test sets in a 7:3 ratio. All patients underwent 18F-fluorodeoxyglucose (18F-FDG) PET/MRI to simultaneously obtain metabolic images (18F-FDG), structural MRI images (T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and T2-weighted fluid-attenuated inversion recovery (T2/FLAIR)) and functional MRI images (susceptibility-weighted imaging (SWI) and apparent diffusion coefficient). Using PET and five MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA). RESULTS The radiomics signatures showed favourable diagnostic efficacy. The optimal model comprised structural (T1WI), functional (SWI) and metabolic (18F-FDG) sequences (RadscoreFDG_T1WI_SWI) with the area under curves (AUCs) of the training and test sets of 0.971 and 0.957, respectively. The integrated model, incorporating RadscoreFDG_T1WI_SWI, three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUVmax, demonstrated satisfactory calibration and discrimination in the training and test sets (0.993 and 0.994, respectively). DCA indicated the highest clinical benefit of the clinical-radiomics integrated model. CONCLUSIONS The radiomics signature with metabolic, structural and functional information provided by hybrid 18F-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.
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Affiliation(s)
- Xuehan Hu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Xun Sun
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Fan Hu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Fang Liu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Weiwei Ruan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Tingfan Wu
- GE Healthcare, Pudong New Town, No.1, Huatuo Road, Shanghai, 200000, China
| | - Rui An
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
| | - Xiaoli Lan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
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15
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Frey KA, Bohnen NILJ. Molecular Imaging of Neurodegenerative Parkinsonism. PET Clin 2021; 16:261-272. [PMID: 33589385 DOI: 10.1016/j.cpet.2020.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Advances in molecular PET imaging of neurodegenerative parkinsonism are reviewed with focus on neuropharmacologic radiotracers depicting terminals of selectively vulnerable neurons in these conditions. Degeneration and losses of dopamine, norepinephrine, serotonin, and acetylcholine imaging markers thus far do not differentiate among the parkinsonian conditions. Recent studies performed with [18F]fluorodeoxyglucose PET are limited by the need for automated image analysis tools and by lack of routine coverage for this imaging indication in the United States. Ongoing research engages use of novel molecular modeling and in silico methods for design of imaging ligands targeting these specific proteinopathies.
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Affiliation(s)
- Kirk A Frey
- Department of Radiology (Nuclear Medicine and Molecular Imaging), University of Michigan, 1500 East Medical Center Drive, Room B1-G505 UH, Ann Arbor, MI 48109-5028, USA; Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Room B1-G505 UH, Ann Arbor, MI 48109-5028, USA.
| | - Nicolaas I L J Bohnen
- Department of Radiology (Nuclear Medicine and Molecular Imaging), University of Michigan, 24 Frank Lloyd Wright Drive, Box 362, Ann Arbor, MI 48105, USA; Department of Neurology, University of Michigan, 24 Frank Lloyd Wright Drive, Box 362, Ann Arbor, MI 48105, USA; Ann Arbor Veterans Administration Medical Center, Ann Arbor, MI, USA
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16
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Frey KA. Molecular Imaging of Extrapyramidal Movement Disorders With Dementia: The 4R Tauopathies. Semin Nucl Med 2021; 51:275-285. [PMID: 33431202 DOI: 10.1053/j.semnuclmed.2020.12.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Two pathologically distinct neurodegenerative conditions, progressive supranuclear palsy and corticobasal degeneration, share in common deposits of tau proteins that differ both molecularly and ultrastructurally from the common tau deposits diagnostic of Alzheimer disease. The proteinopathy in these disorders is characterized by fibrillary aggregates of 4R tau proteins. The clinical presentations of progressive supranuclear palsy and of corticobasal degeneration are often confused with more common disorders such as Parkinson disease or subtypes of frontotemporal lobar degeneration. Neither of these 4R tau disorders has effective therapy, and while there are emerging molecular imaging approaches to identify patients earlier in the course of disease, there are as yet no reliably sensitive and specific approaches to diagnoses in life. In this review, aspects of the clinical syndromes, neuropathology, and molecular biomarker imaging studies applicable to progressive supranuclear palsy and to corticobasal degeneration will be presented. Future development of more accurate molecular imaging approaches is proposed.
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Affiliation(s)
- Kirk A Frey
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, The University of Michigan Health System, Ann Arbor, MI.
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17
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The Use of FDG PET Parametric Imaging in the Diagnosis of Olivopontocerebellar Atrophy. Clin Nucl Med 2020; 45:e419-e421. [PMID: 32657870 DOI: 10.1097/rlu.0000000000003180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Olivopontocerebellar atrophy is a rare neurodegenerative syndrome associated with 2 distinct disorders: multiple system atrophy and spinocerebellar ataxia. We present a case involving a 66-year-old man with adult-onset progressing cerebellar signs reflective of a cerebellar syndrome with no significant family history and unremarkable genetic testing for spinocerebellar ataxia. This case was found to be most consistent with sporadic olivopontocerebellar atrophy, which falls under the multiple system atrophy category. This diagnosis can be made using F-FDG PET/CT scanning and with MRI in some cases. However, in this case, relatively new PET/CT quantification and parametric imaging software was used for analysis, CortexID Suite.
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18
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Cheng L, Wu X, Guo R, Wang Y, Wang W, He P, Lin H, Shen J. Discriminative pattern of reduced cerebral blood flow in Parkinson's disease and Parkinsonism-Plus syndrome: an ASL-MRI study. BMC Med Imaging 2020; 20:78. [PMID: 32660445 PMCID: PMC7359235 DOI: 10.1186/s12880-020-00479-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 07/06/2020] [Indexed: 11/16/2022] Open
Abstract
Background Accurate identification of Parkinson’s disease (PD) and Parkinsonism-Plus syndrome (PPS), especially in the early stage of the disease, is very important. The purpose of this study was to investigate the discriminative spatial pattern of cerebral blood flow (CBF) between patients with PD and PPS. Methods Arterial spin labeling (ASL) perfusion-weighted imaging was performed in 20 patients with PD (mean age 56.35 ± 7.56 years), 16 patients with PPS (mean age 59.62 ± 6.89 years), and 17 healthy controls (HCs, mean age 54.17 ± 6.58 years). Voxel-wise comparison of the CBF was performed among PD, PPS, and HC groups. The receiver operating characteristic (ROC) curve was used to evaluate the performance of CBF in discriminating between PD and PPS. The relationship between CBF and non-motor neuropsychological scores was assessed by correlation analysis. Results PD group showed a significantly decreased CBF in the right cerebelum_crus2, the left middle frontal gyrus (MFG), the triangle inferior frontal gyrus (IFG_Tri), the left frontal medial orbital gyrus (FG_Med_Orb) and the left caudate nucleus (CN) compared with the HC group (P < 0.05). Besides the above regions, the left supplementary motor area (SMA), the right thalamus had decreased CBF in the PPS group compared with the HC group (P < 0.05). PPS group had lower CBF value in the left MFG, the left IFG_Tri, the left CN, the left SMA, and the right thalamus compared with the PD group (P < 0.05). CBFs in left IFG_Tri, the left CN, the left SMA, and the right thalamus had moderate to high capacity in discriminating between PD and PPS patients (AUC 0.719–0.831). The CBF was positively correlated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores in PD patients, while positively correlated with the MMSE, Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD) scores in PPS patients (P < 0.05). Conclusion PD and PPS patients have certain discriminative patterns of reduced CBFs, which can be used as a surrogate marker for differential diagnosis.
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Affiliation(s)
- Lina Cheng
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Radiology, Guangdong 999 Brain Hospital, Guangzhou, 510510, China
| | - Xiaoyan Wu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Ruomi Guo
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China
| | - Yuzhou Wang
- Department of Neurology, Guangdong 999 Brain Hospital, Guangzhou, 510510, China
| | - Wensheng Wang
- Department of Radiology, Guangdong 999 Brain Hospital, Guangzhou, 510510, China
| | - Peng He
- Department of Radiology, Guangdong 999 Brain Hospital, Guangzhou, 510510, China
| | - Hanbo Lin
- Department of Radiology, Guangdong 999 Brain Hospital, Guangzhou, 510510, China
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
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Role of [18F]-FDG PET in patients with atypical parkinsonism associated with dementia. Clin Transl Imaging 2020. [DOI: 10.1007/s40336-020-00360-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Abstract
PURPOSE OF REVIEW Being a disease with heterogeneous presentations and unclear consensus on its diagnostic criteria, it is difficult to differentiate vascular parkinsonism (VaP) from other neurodegenerative parkinsonism variants. Ongoing research on structural and functional neuroimaging targeting dopaminergic pathway provides us more insight into the pathophysiology of VaP to improve diagnostic accuracy. The aim of this article is to review how the emerging imaging modalities help the diagnostic process and treatment decision in VaP. RECENT FINDINGS Dopamine transporter imaging is a promising tool in differentiating presynaptic parkinsonism and VaP. It also predicts the levodopa responders in VaP. Advanced MRI techniques including volumetry, diffusion tensor imaging and sequences visualising substantia nigra are under development, and they are complementary to each other in detecting structural and functional changes in VaP, which is crucial to ensure the quality of future therapeutic trials for VaP. Dopamine transporter imaging is recommended to patients with suspected VaP. Multimodal MRI in VaP would be an important area to be investigated in the near future.
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Affiliation(s)
- Karen K Y Ma
- Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shi Lin
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Imaging & Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- BrainNow Research Institute, Guangdong Province, Shenzhen, China
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
- BrainNow Research Institute, Guangdong Province, Shenzhen, China.
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Abnormal pattern of brain glucose metabolism in Parkinson's disease: replication in three European cohorts. Eur J Nucl Med Mol Imaging 2019; 47:437-450. [PMID: 31768600 PMCID: PMC6974499 DOI: 10.1007/s00259-019-04570-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/03/2019] [Indexed: 12/15/2022]
Abstract
Rationale In Parkinson’s disease (PD), spatial covariance analysis of 18F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP). Methods The PDRPNL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRPIT was identified in 20 controls and 20 “de-novo” PD patients from an Italian cohort. A further 24 controls and 18 “de-novo” Italian patients were used for validation. The PDRPSP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy. Results A PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in “de-novo” PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRPIT, PDRPNL, and PDRPSP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA. Conclusion The PDRP is a reproducible disease characteristic across PD populations and scanning platforms globally. Further study is needed to identify the topography of specific PD subtypes, and to identify and correct for center-specific effects. Electronic supplementary material The online version of this article (10.1007/s00259-019-04570-7) contains supplementary material, which is available to authorized users.
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Saranza GM, Whitwell JL, Kovacs GG, Lang AE. Corticobasal degeneration. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2019; 149:87-136. [PMID: 31779825 DOI: 10.1016/bs.irn.2019.10.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Corticobasal degeneration (CBD) is a rare neurodegenerative disease characterized by the predominance of pathological 4 repeat tau deposition in various cell types and anatomical regions. Corticobasal syndrome (CBS) is one of the clinical phenotypes associated with CBD pathology, manifesting as a progressive asymmetric akinetic-rigid, poorly levodopa-responsive parkinsonism, with cerebral cortical dysfunction. CBD can manifest as several clinical phenotypes, and similarly, CBS can also have a pathologic diagnosis other than CBD. This chapter discusses the clinical manifestations of pathologically confirmed CBD cases, the current diagnostic criteria, as well as the pathologic and neuroimaging findings of CBD/CBS. At present, therapeutic options for CBD remain symptomatic. Further research is needed to improve the clinical diagnosis of CBD, as well as studies on disease-modifying therapies for this relentlessly progressive neurodegenerative disorder.
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Affiliation(s)
- Gerard M Saranza
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, Ontario, Canada
| | | | - Gabor G Kovacs
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, Ontario, Canada; Tanz Centre for Research in Neurodegenerative Disease and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Anthony E Lang
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
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Gu SC, Ye Q, Yuan CX. Metabolic pattern analysis of 18F-FDG PET as a marker for Parkinson's disease: a systematic review and meta-analysis. Rev Neurosci 2019; 30:743-756. [PMID: 31050657 DOI: 10.1515/revneuro-2018-0061] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 12/28/2018] [Indexed: 12/14/2022]
Abstract
A large number of articles have assessed the diagnostic accuracy of the metabolic pattern analysis of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in Parkinson's disease (PD); however, different studies involved small samples with various controls and methods, leading to discrepant conclusions. This study aims to consolidate the available observational studies and provide a comprehensive evaluation of the clinical utility of 18F-FDG PET for PD. The methods included a systematic literature search and a hierarchical summary receiver operating characteristic approach. Sensitivity analyses according to different pattern analysis methods (statistical parametric mapping versus scaled subprofile modeling/principal component analysis) and control population [healthy controls (HCs) versus atypical parkinsonian disorder (APD) patients] were performed to verify the consistency of the main results. Additional analyses for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) were conducted. Fifteen studies comprising 1446 subjects (660 PD patients, 499 APD patients, and 287 HCs) were included. The overall diagnostic accuracy of 18F-FDG in differentiating PD from APDs and HCs was quite high, with a pooled sensitivity of 0.88 [95% confidence interval (95% CI), 0.85-0.91] and a pooled specificity of 0.92 (95% CI, 0.89-0.94), with sensitivity analyses indicating statistically consistent results. Additional analyses showed an overall sensitivity and specificity of 0.87 (95% CI, 0.76-0.94) and 0.93 (95% CI, 0.89-0.96) for MSA and 0.91 (95% CI, 0.78-0.95) and 0.96 (95% CI, 0.92-0.98) for PSP. Our study suggests that the metabolic pattern analysis of 18F-FDG PET has high diagnostic accuracy in the differential diagnosis of parkinsonian disorders.
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Affiliation(s)
- Si-Chun Gu
- Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qing Ye
- Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai 200032, China
| | - Can-Xing Yuan
- Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai 200032, China
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24
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Shen T, Jiang J, Lin W, Ge J, Wu P, Zhou Y, Zuo C, Wang J, Yan Z, Shi K. Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control. Front Neurosci 2019; 13:396. [PMID: 31110472 PMCID: PMC6501727 DOI: 10.3389/fnins.2019.00396] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 04/08/2019] [Indexed: 12/31/2022] Open
Abstract
As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease (PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80:20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (R = 0.705, p < 0.001; R = 0.697, p < 0.001 in Test 1; R = 0.592, p = 0.0018, R = 0.528, p = 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD.
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Affiliation(s)
- Ting Shen
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Anhui Medical University, Wuxi, China
| | - Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yongjin Zhou
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Jian Wang
- Department of neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhuangzhi Yan
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.,Department of Nuclear Medicine, Technische Universitat Munchen, Munich, Germany
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25
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Alster P, Madetko NK, Koziorowski DM, Królicki L, Budrewicz S, Friedman A. Accumulation of Tau Protein, Metabolism and Perfusion-Application and Efficacy of Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) Imaging in the Examination of Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS). Front Neurol 2019; 10:101. [PMID: 30837933 PMCID: PMC6383629 DOI: 10.3389/fneur.2019.00101] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 01/25/2019] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging in the context of examining atypical parkinsonian tauopathies is an evolving matter. Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) bring tools, which may be reasonable in supplementary examination, however cannot be interpreted as a gold standard for correct diagnosis. The review presents advantages and limitations of tau radiotracers in PET, metabolic PET and perfusion SPECT. The aim of this paper is to highlight the possibilities and boundaries in the supplementary examination of tauopathic parkinsonian syndromes.
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Affiliation(s)
- Piotr Alster
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
| | | | | | - Leszek Królicki
- Department of Nuclear Medicine, Medical University of Warsaw, Warsaw, Poland
| | | | - Andrzej Friedman
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
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26
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PD and DLB: Brain imaging in Parkinson's disease and dementia with Lewy bodies. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 165:167-185. [DOI: 10.1016/bs.pmbts.2019.07.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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27
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Pritchard C, Silk A. Patient’s occupation, electric & head trauma in a cohort of 88 multiple system atrophy patients compared with the general population: a hypothesis stimulating pilot study. ACTA ACUST UNITED AC 2018. [DOI: 10.15406/jnsk.2018.08.00305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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28
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Jellinger KA. Potential clinical utility of multiple system atrophy biomarkers. Expert Rev Neurother 2017; 17:1189-1208. [DOI: 10.1080/14737175.2017.1392239] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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29
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Meyer PT, Frings L, Rücker G, Hellwig S. 18F-FDG PET in Parkinsonism: Differential Diagnosis and Evaluation of Cognitive Impairment. J Nucl Med 2017; 58:1888-1898. [DOI: 10.2967/jnumed.116.186403] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 08/10/2017] [Indexed: 12/30/2022] Open
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