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Chow TK, Ma KM. Loss of Mickey Mouse Ears' Sign in Progressive Supranuclear Palsy. Clin Nucl Med 2024; 49:551-553. [PMID: 38598736 DOI: 10.1097/rlu.0000000000005229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
ABSTRACT Progressive supranuclear palsy (PSP) is the most prevalent form of degenerative atypical parkinsonism. Clinical manifestations of PSP commonly encompass deficits in vertical gaze, postural stability, akinesia, and cognitive impairment. The characteristic metabolic pattern observed in PSP through FDG PET displays hypometabolism in the midbrain, striatum, thalamus, and frontal lobe. However, visual interpretation of midbrain hypometabolism poses challenges. In this report, we aim to elucidate a novel observation termed the "loss of Mickey Mouse ears' sign," which signifies midbrain hypometabolism as detected through visual assessment of FDG PET images.
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Affiliation(s)
- Tsz-Kit Chow
- From the Nuclear Medicine Unit, Department of Radiology and Nuclear Medicine, Tuen Mun Hospital, Hong Kong
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2
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Cotta Ramusino M, Massa F, Festari C, Gandolfo F, Nicolosi V, Orini S, Nobili F, Frisoni GB, Morbelli S, Garibotto V. Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review. Eur J Nucl Med Mol Imaging 2024; 51:1876-1890. [PMID: 38355740 DOI: 10.1007/s00259-024-06631-y] [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: 11/13/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. METHODS Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. RESULTS Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. CONCLUSION Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.
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Affiliation(s)
- Matteo Cotta Ramusino
- Unit of Behavior Neurology and Dementia Research Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy.
| | - Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Festari
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
| | - Federica Gandolfo
- Department of Geriatric Care, Orthogeriatrics and Rehabilitation, E.O. Galliera Hospital, Genoa, Italy
| | - Valentina Nicolosi
- UOC Neurologia Ospedale Magalini Di Villafranca Di Verona (VR) ULSS 9, Verona, Italy
| | - Stefania Orini
- Alzheimer's Unit-Memory Clinic, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
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Cerina V, Crivellaro C, Morzenti S, Pozzi FE, Bigiogera V, Jonghi-Lavarini L, Moresco RM, Basso G, De Bernardi E. A ROI-based quantitative pipeline for 18F-FDG PET metabolism and pCASL perfusion joint analysis: Validation of the 18F-FDG PET line. Heliyon 2024; 10:e23340. [PMID: 38163125 PMCID: PMC10755331 DOI: 10.1016/j.heliyon.2023.e23340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
In Mild Cognitive Impairment (MCI), the study of brain metabolism, provided by 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) can be integrated with brain perfusion through pseudo-Continuous Arterial Spin Labeling Magnetic Resonance sequences (MR pCASL). Cortical hypometabolism identification generally relies on wide control group datasets; pCASL control groups are instead not publicly available yet, due to lack of standardization in the acquisition parameters. This study presents a quantitative pipeline to be applied to PET and pCASL data to coherently analyze metabolism and perfusion inside 16 matching cortical regions of interest (ROIs) derived from the AAL3 atlas. The PET line is tuned on 36 MCI patients and 107 healthy control subjects, to agree in identifying hypometabolic regions with clinical reference methods (visual analysis supported by a vendor tool and Statistical Parametric Mapping, SPM, with two parametrizations here identified as SPM-A and SPM-B). The analysis was conducted for each ROI separately. The proposed PET analysis pipeline obtained accuracy 78 % and Cohen's к 60 % vs visual analysis, accuracy 79 % and Cohen's к 58 % vs SPM-A, accuracy 77 % and Cohen's к 54 % vs SPM-B. Cohen's к resulted not significantly different from SPM-A and SPM-B Cohen's к when assuming visual analysis as reference method (p-value 0.61 and 0.31 respectively). Considering SPM-A as reference method, Cohen's к is not significantly different from SPM-B Cohen's к as well (p-value = 1.00). The complete PET-pCASL pipeline was then preliminarily applied on 5 MCI patients and metabolism-perfusion regional correlations were assessed. The proposed approach can be considered as a promising tool for PET-pCASL joint analyses in MCI, even in the absence of a pCASL control group, to perform metabolism-perfusion regional correlation studies, and to assess and compare perfusion in hypometabolic or normo-metabolic areas.
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Affiliation(s)
- Valeria Cerina
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Cinzia Crivellaro
- Nuclear Medicine, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Sabrina Morzenti
- Medical Physics, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Federico E. Pozzi
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neurology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
| | | | | | - Rosa M. Moresco
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Gianpaolo Basso
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neuroradiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
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Avendaño-Estrada A, Olarte-Casas MÁ, Ávila-Rodríguez MÁ. Vectorial-based analysis of dual-tracer PET imaging: A proof of concept. Comput Biol Med 2024; 168:107705. [PMID: 37979207 DOI: 10.1016/j.compbiomed.2023.107705] [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: 07/17/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND The diagnosis of neurological diseases is complicated since they often share similar symptoms and occur in different severity levels. Imaging techniques such as PET molecular imaging are helpful for an early and accurate diagnosis and, staging allowing a noninvasive evaluation of the disease. The combination of two radioligands in the same patient could be valuable to achieve these diagnostic goals; nevertheless, the imaging data obtained with two radioligands is commonly interpreted independently. This novel approach to combine the PET data of two radiopharmaceuticals, separately acquired in the same subject, is to obtain new quantitative metrics. PET images of patients with Parkinson's disease (PD) and healthy controls (HC) were analyzed. Voxel-by-voxel uptake is compared by combining the imaging data. Dual-tracer PET imaging analysis was tested with [11C]DTBZ-[11C]Raclopride as proof of concept. RESULTS The new proposed metric based on a resultant vector is capable of efficiently discriminating healthy controls from PD patients (p < 0.0001) allowing the detection of slight changes in patients undergoing therapeutic approaches. Significant differences were found between HC and PD patients for the evaluated radiotracers. CONCLUSIONS The resultant vector appears to deliver useful information that could be helpful to evaluate PD patients under treatment and to improve differential diagnoses.
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Affiliation(s)
- Arturo Avendaño-Estrada
- Unidad Radiofarmacia-Ciclotrón, Facultad de Medicina, División de Investigación, Universidad Nacional Autónoma de México, Mexico; Centro de Investigación sobre el Envejecimiento, Centro de Investigación y de Estudios Avanzados Sede Sur, Mexico.
| | - Miguel Ángel Olarte-Casas
- Unidad PET/CT, Facultad de Medicina, División de Investigación, Universidad Nacional Autónoma de México, Mexico
| | - Miguel Ángel Ávila-Rodríguez
- Unidad Radiofarmacia-Ciclotrón, Facultad de Medicina, División de Investigación, Universidad Nacional Autónoma de México, Mexico; Centro de Investigación sobre el Envejecimiento, Centro de Investigación y de Estudios Avanzados Sede Sur, Mexico.
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Shekari M, Verwer EE, Yaqub M, Daamen M, Buckley C, Frisoni GB, Visser PJ, Farrar G, Barkhof F, Gispert JD, Boellaard R. Harmonization of brain PET images in multi-center PET studies using Hoffman phantom scan. EJNMMI Phys 2023; 10:68. [PMID: 37906338 PMCID: PMC10618151 DOI: 10.1186/s40658-023-00588-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/12/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Image harmonization has been proposed to minimize heterogeneity in brain PET scans acquired in multi-center studies. However, standard validated methods and software tools are lacking. Here, we assessed the performance of a framework for the harmonization of brain PET scans in a multi-center European clinical trial. METHOD Hoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using "Hoffman 3D brain Analysis tool," indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method's robustness against typical dose-calibrator-based errors was assessed. RESULTS The EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040-0.012), and Contrast SD (0.14-0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR. CONCLUSION Harmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.
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Affiliation(s)
- Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Eline E Verwer
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Marcel Daamen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Center, Department of Rehabilitation and Geriatrics, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Queen Square Institute of Neurology, University College London, London, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
- Universitat Pompeu Fabra, Barcelona, Spain.
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain.
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
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Mattoli MV, Cocciolillo F, Chiacchiaretta P, Dotta F, Trevisi G, Carrarini C, Thomas A, Sensi S, Pizzi AD, Nicola ADD, Crosta AD, Mammarella N, Padovani A, Pilotto A, Moda F, Tiraboschi P, Martino G, Bonanni L. Combined 18F-FDG PET-CT markers in dementia with Lewy bodies. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12515. [PMID: 38145190 PMCID: PMC10746864 DOI: 10.1002/dad2.12515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/10/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION 18F-Fluoro-deoxyglucose-positron emission tomography (FDG-PET) is a supportive biomarker in dementia with Lewy bodies (DLB) diagnosis and its advanced analysis methods, including radiomics and machine learning (ML), were developed recently. The aim of this study was to evaluate the FDG-PET diagnostic performance in predicting a DLB versus Alzheimer's disease (AD) diagnosis. METHODS FDG-PET scans were visually and semi-quantitatively analyzed in 61 patients. Radiomics and ML analyses were performed, building five ML models: (1) clinical features; (2) visual and semi-quantitative PET features; (3) radiomic features; (4) all PET features; and (5) overall features. RESULTS At follow-up, 34 patients had DLB and 27 had AD. At visual analysis, DLB PET signs were significantly more frequent in DLB, having the highest diagnostic accuracy (86.9%). At semi-quantitative analysis, the right precuneus, superior parietal, lateral occipital, and primary visual cortices showed significantly reduced uptake in DLB. The ML model 2 had the highest diagnostic accuracy (84.3%). DISCUSSION FDG-PET is a valuable tool in DLB diagnosis, having visual and semi-quantitative analyses with the highest diagnostic accuracy at ML analyses.
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Affiliation(s)
- Maria Vittoria Mattoli
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
- Nuclear Medicine UnitPresidio Ospedaliero Santo SpiritoPescaraItaly
| | - Fabrizio Cocciolillo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed EmatologiaUOC di Medicina Nucleare, Fondazione Policlinico Universitario Agostino Gemelli IRCCSRomeItaly
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
- Advanced Computing Core, Center for Advanced Studies and Technology ‐ C.A.S.TUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Francesco Dotta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Gianluca Trevisi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Claudia Carrarini
- Department of NeuroscienceCatholic University of Sacred HeartRomeItaly
- IRCCS San RaffaeleRomeItaly
| | - Astrid Thomas
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Stefano Sensi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Andrea Delli Pizzi
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | | | - Adolfo Di Crosta
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
| | - Nicola Mammarella
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
- Parkinson's Disease Rehabilitation CentreFERB ONLUS‐S. Isidoro HospitalTrescore BalnearioBergamoItaly
| | - Fabio Moda
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Pietro Tiraboschi
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Gianluigi Martino
- Department of Radiological Sciences, Nuclear Medicine UniteSS. Annunziata HospitalVia dei Vestini 31ChietiItaly
| | - Laura Bonanni
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
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Perovnik M, Vo A, Nguyen N, Jamšek J, Rus T, Tang CC, Trošt M, Eidelberg D. Automated differential diagnosis of dementia syndromes using FDG PET and machine learning. Front Aging Neurosci 2022; 14:1005731. [PMID: 36408106 PMCID: PMC9667048 DOI: 10.3389/fnagi.2022.1005731] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/10/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. METHODS We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. RESULTS Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. CONCLUSION Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia,Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States,*Correspondence: Matej Perovnik,
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, United States
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Chris C. Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia,Department of Nuclear Medicine, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
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Perovnik M, Tomše P, Jamšek J, Emeršič A, Tang C, Eidelberg D, Trošt M. Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients. Sci Rep 2022; 12:11752. [PMID: 35817836 PMCID: PMC9273623 DOI: 10.1038/s41598-022-15667-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 06/28/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer's disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer's disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients' clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76-0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia.
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
| | - Andreja Emeršič
- Laboratory for CSF Diagnostics, Department of Neurology, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
| | - Chris Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
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Guedj E, Varrone A, Boellaard R, Albert NL, Barthel H, van Berckel B, Brendel M, Cecchin D, Ekmekcioglu O, Garibotto V, Lammertsma AA, Law I, Peñuelas I, Semah F, Traub-Weidinger T, van de Giessen E, Van Weehaeghe D, Morbelli S. EANM procedure guidelines for brain PET imaging using [ 18F]FDG, version 3. Eur J Nucl Med Mol Imaging 2021; 49:632-651. [PMID: 34882261 PMCID: PMC8803744 DOI: 10.1007/s00259-021-05603-w] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022]
Abstract
The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [18F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [18F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [18F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.
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Affiliation(s)
- Eric Guedj
- APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Aix Marseille Univ, Marseille, France. .,Service Central de Biophysique et Médecine Nucléaire, Hôpital de la Timone, 264 rue Saint Pierre, 13005, Marseille, France.
| | - Andrea Varrone
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Healthcare Services, Stockholm, Sweden
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Nathalie L Albert
- Department of Nuclear Medicine, Ludwig Maximilians-University of Munich, Munich, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University, Leipzig, Germany
| | - Bart van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Matthias Brendel
- Department of Nuclear Medicine, Ludwig Maximilians-University of Munich, Munich, Germany.,German Centre of Neurodegenerative Diseases (DZNE), Site Munich, Bonn, Germany
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Ozgul Ekmekcioglu
- Sisli Hamidiye Etfal Education and Research Hospital, Nuclear Medicine Dept., University of Health Sciences, Istanbul, Turkey
| | - Valentina Garibotto
- NIMTLab, Faculty of Medicine, Geneva University, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Iván Peñuelas
- Department of Nuclear Medicine, Clinica Universidad de Navarra, IdiSNA, University of Navarra, Pamplona, Spain
| | - Franck Semah
- Nuclear Medicine Department, University Hospital, Lille, France
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Elsmarieke van de Giessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Meibergdreef 9, Amsterdam, The Netherlands
| | | | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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10
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Doruyter AGG, Parkes J, Carr J, Warwick JM. PET-CT in brain disorders: The South African context. SA J Radiol 2021; 25:2201. [PMID: 34858659 PMCID: PMC8603194 DOI: 10.4102/sajr.v25i1.2201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/12/2021] [Indexed: 11/30/2022] Open
Abstract
Positron emission tomography combined with X-ray computed tomography (PET-CT) has an established role in the management of brain disorders, but may be underutilised in South Africa. Possible barriers to access include the limited number of PET-CT facilities and the lack of contemporary guidelines for the use of brain PET-CT in South Africa. The current review aims to highlight the evidence-based usage of brain Positron emission tomography (PET) in dementia, movement disorders, brain tumours, epilepsy, neuropsychiatric lupus, immune-mediated encephalitides, and brain infections. While being areas of research, there is currently no clinical role for the use of PET-CT in traumatic brain injury or in psychiatric or neurodevelopmental disorders. Strategies to expand the appropriate use of PET-CT in brain disorders are discussed in this article.
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Affiliation(s)
- Alexander G G Doruyter
- NuMeRI Node for Infection Imaging, Central Analytical Facilities, Stellenbosch University, Cape Town, South Africa.,Division of Nuclear Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jeannette Parkes
- Division of Radiation Oncology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jonathan Carr
- Division of Neurology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - James M Warwick
- Division of Nuclear Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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11
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Proesmans S, Raedt R, Germonpré C, Christiaen E, Descamps B, Boon P, De Herdt V, Vanhove C. Voxel-Based Analysis of [18F]-FDG Brain PET in Rats Using Data-Driven Normalization. Front Med (Lausanne) 2021; 8:744157. [PMID: 34746179 PMCID: PMC8565796 DOI: 10.3389/fmed.2021.744157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/24/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: [18F]-FDG PET is a widely used imaging modality that visualizes cellular glucose uptake and provides functional information on the metabolic state of different tissues in vivo. Various quantification methods can be used to evaluate glucose metabolism in the brain, including the cerebral metabolic rate of glucose (CMRglc) and standard uptake values (SUVs). Especially in the brain, these (semi-)quantitative measures can be affected by several physiological factors, such as blood glucose level, age, gender, and stress. Next to this inter- and intra-subject variability, the use of different PET acquisition protocols across studies has created a need for the standardization and harmonization of brain PET evaluation. In this study we present a framework for statistical voxel-based analysis of glucose uptake in the rat brain using histogram-based intensity normalization. Methods: [18F]-FDG PET images of 28 normal rat brains were coregistered and voxel-wisely averaged. Ratio images were generated by voxel-wisely dividing each of these images with the group average. The most prevalent value in the ratio image was used as normalization factor. The normalized PET images were voxel-wisely averaged to generate a normal rat brain atlas. The variability of voxel intensities across the normalized PET images was compared to images that were either normalized by whole brain normalization, or not normalized. To illustrate the added value of this normal rat brain atlas, 9 animals with a striatal hemorrhagic lesion and 9 control animals were intravenously injected with [18F]-FDG and the PET images of these animals were voxel-wisely compared to the normal atlas by group- and individual analyses. Results: The average coefficient of variation of the voxel intensities in the brain across normal [18F]-FDG PET images was 6.7% for the histogram-based normalized images, 11.6% for whole brain normalized images, and 31.2% when no normalization was applied. Statistical voxel-based analysis, using the normal template, indicated regions of significantly decreased glucose uptake at the site of the ICH lesion in the ICH animals, but not in control animals. Conclusion: In summary, histogram-based intensity normalization of [18F]-FDG uptake in the brain is a suitable data-driven approach for standardized voxel-based comparison of brain PET images.
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Affiliation(s)
- Silke Proesmans
- 4Brain Lab, Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Robrecht Raedt
- 4Brain Lab, Department of Head and Skin, Ghent University, Ghent, Belgium
| | | | - Emma Christiaen
- IbiTech-MEDISIP-Infinity Lab, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Benedicte Descamps
- IbiTech-MEDISIP-Infinity Lab, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Paul Boon
- 4Brain Lab, Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Veerle De Herdt
- 4Brain Lab, Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Christian Vanhove
- IbiTech-MEDISIP-Infinity Lab, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
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12
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Added value of semiquantitative analysis of brain FDG-PET for the differentiation between MCI-Lewy bodies and MCI due to Alzheimer's disease. Eur J Nucl Med Mol Imaging 2021; 49:1263-1274. [PMID: 34651219 DOI: 10.1007/s00259-021-05568-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 09/17/2021] [Indexed: 01/13/2023]
Abstract
PURPOSE FDG-PET is an established supportive biomarker in dementia with Lewy bodies (DLB), but its diagnostic accuracy is unknown at the mild cognitive impairment (MCI-LB) stage when the typical metabolic pattern may be difficultly recognized at the individual level. Semiquantitative analysis of scans could enhance accuracy especially in less skilled readers, but its added role with respect to visual assessment in MCI-LB is still unknown. METHODS We assessed the diagnostic accuracy of visual assessment of FDG-PET by six expert readers, blind to diagnosis, in discriminating two matched groups of patients (40 with prodromal AD (MCI-AD) and 39 with MCI-LB), both confirmed by in vivo biomarkers. Readers were provided in a stepwise fashion with (i) maps obtained by the univariate single-subject voxel-based analysis (VBA) with respect to a control group of 40 age- and sex-matched healthy subjects, and (ii) individual odds ratio (OR) plots obtained by the volumetric regions of interest (VROI) semiquantitative analysis of the two main hypometabolic clusters deriving from the comparison of MCI-AD and MCI-LB groups in the two directions, respectively. RESULTS Mean diagnostic accuracy of visual assessment was 76.8 ± 5.0% and did not significantly benefit from adding the univariate VBA map reading (77.4 ± 8.3%) whereas VROI-derived OR plot reading significantly increased both accuracy (89.7 ± 2.3%) and inter-rater reliability (ICC 0.97 [0.96-0.98]), regardless of the readers' expertise. CONCLUSION Conventional visual reading of FDG-PET is moderately accurate in distinguishing between MCI-LB and MCI-AD, and is not significantly improved by univariate single-subject VBA but by a VROI analysis built on macro-regions, allowing for high accuracy independent of reader skills.
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13
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Doyen M, Mairal E, Bordonne M, Zaragori T, Roch V, Imbert L, Verger A. Effect of Point Spread Function Deconvolution in Reconstruction of Brain 18F-FDG PET Images on the Diagnostic Thinking Efficacy in Alzheimer's Disease. Front Med (Lausanne) 2021; 8:721551. [PMID: 34395486 PMCID: PMC8358179 DOI: 10.3389/fmed.2021.721551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/06/2021] [Indexed: 11/22/2022] Open
Abstract
Purpose: This study aims to determine the effect of applying Point Spread Function (PSF) deconvolution, which is known to improve contrast and spatial resolution in brain 18F-FDG PET images, to the diagnostic thinking efficacy in Alzheimer's disease (AD). Methods: We compared Hoffman 3-D brain phantom images reconstructed with or without PSF. The effect of PSF deconvolution on AD diagnostic clinical performance was determined from digital brain 18F-FDG PET images of AD (n = 38) and healthy (n = 35) subjects compared to controls (n = 36). Performances were assessed with SPM at the group level (p < 0.001 for the voxel) and at the individual level by visual interpretation of SPM T-maps (p < 0.005 for the voxel) by the consensual analysis of three experienced raters. Results: A mix of large hypometabolic (1,483cm3, mean value of −867 ± 492 Bq/ml) and intense hypermetabolic (902 cm3, mean value of 1,623 ± 1,242 Bq/ml) areas was observed in the PSF compared to the no PSF phantom images. Significant hypometabolic areas were observed in the AD group compared to the controls, for reconstructions with and without PSF (respectively 23.7 and 26.2 cm3), whereas no significant hypometabolic areas were observed when comparing the group of healthy subjects to the control group. At the individual level, no significant differences in diagnostic performances for discriminating AD were observed visually (sensitivity of 89 and 92% for reconstructions with and without PSF respectively, similar specificity of 74%). Conclusion: Diagnostic thinking efficacy performances for diagnosing AD are similar for 18F-FDG PET images reconstructed with or without PSF.
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Affiliation(s)
- Matthieu Doyen
- Université de Lorraine, IADI, INSERM U1254 and Nancyclotep Imaging Platform, Nancy, France
| | - Elise Mairal
- Department of Nuclear Medicine, Université de Lorraine, CHRU Nancy, Nancy, France
| | - Manon Bordonne
- Department of Nuclear Medicine, Université de Lorraine, CHRU Nancy, Nancy, France
| | - Timothée Zaragori
- Université de Lorraine, IADI, INSERM U1254 and Nancyclotep Imaging Platform, Nancy, France
| | - Véronique Roch
- Université de Lorraine, IADI, INSERM U1254 and Nancyclotep Imaging Platform, Nancy, France
| | - Laetitia Imbert
- Université de Lorraine, IADI, INSERM U1254 and Nancyclotep Imaging Platform, Nancy, France.,Department of Nuclear Medicine, Université de Lorraine, CHRU Nancy, Nancy, France
| | - Antoine Verger
- Université de Lorraine, IADI, INSERM U1254 and Nancyclotep Imaging Platform, Nancy, France.,Department of Nuclear Medicine, Université de Lorraine, CHRU Nancy, Nancy, France
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14
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Paredes-Pacheco J, López-González FJ, Silva-Rodríguez J, Efthimiou N, Niñerola-Baizán A, Ruibal Á, Roé-Vellvé N, Aguiar P. SimPET-An open online platform for the Monte Carlo simulation of realistic brain PET data. Validation for 18 F-FDG scans. Med Phys 2021; 48:2482-2493. [PMID: 33713354 PMCID: PMC8252452 DOI: 10.1002/mp.14838] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose SimPET (www.sim‐pet.org) is a free cloud‐based platform for the generation of realistic brain positron emission tomography (PET) data. In this work, we introduce the key features of the platform. In addition, we validate the platform by performing a comparison between simulated healthy brain FDG‐PET images and real healthy subject data for three commercial scanners (GE Advance NXi, GE Discovery ST, and Siemens Biograph mCT). Methods The platform provides a graphical user interface to a set of automatic scripts taking care of the code execution for the phantom generation, simulation (SimSET), and tomographic image reconstruction (STIR). We characterize the performance using activity and attenuation maps derived from PET/CT and MRI data of 25 healthy subjects acquired with a GE Discovery ST. We then use the created maps to generate synthetic data for the GE Discovery ST, the GE Advance NXi, and the Siemens Biograph mCT. The validation was carried out by evaluating Bland‐Altman differences between real and simulated images for each scanner. In addition, SPM voxel‐wise comparison was performed to highlight regional differences. Examples for amyloid PET and for the generation of ground‐truth pathological patients are included. Results The platform can be efficiently used for generating realistic simulated FDG‐PET images in a reasonable amount of time. The validation showed small differences between SimPET and acquired FDG‐PET images, with errors below 10% for 98.09% (GE Discovery ST), 95.09% (GE Advance NXi), and 91.35% (Siemens Biograph mCT) of the voxels. Nevertheless, our SPM analysis showed significant regional differences between the simulated images and real healthy patients, and thus, the use of the platform for converting control subject databases between different scanners requires further investigation. Conclusions The presented platform can potentially allow scientists in clinical and research settings to perform MC simulation experiments without the need for high‐end hardware or advanced computing knowledge and in a reasonable amount of time.
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Affiliation(s)
- José Paredes-Pacheco
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Francisco Javier López-González
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Jesús Silva-Rodríguez
- Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain.,R&D Department, Qubiotech Health Intelligence SL, A Coruña, Galicia, Spain
| | - Nikos Efthimiou
- Positron Emission Tomography Research Centre, University of Hull, Hull, HU6 7RX, UK
| | - Aida Niñerola-Baizán
- Nuclear Medicine Department, Hospital Clinic Barcelona, Universitat de Barcelona, Barcelona, Spain.,Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Álvaro Ruibal
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain
| | - Núria Roé-Vellvé
- Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Pablo Aguiar
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain
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15
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Verger A, Doyen M, Campion JY, Guedj E. The pons as reference region for intensity normalization in semi-quantitative analysis of brain 18FDG PET: application to metabolic changes related to ageing in conventional and digital control databases. EJNMMI Res 2021; 11:31. [PMID: 33761019 PMCID: PMC7990981 DOI: 10.1186/s13550-021-00771-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 03/03/2021] [Indexed: 02/06/2023] Open
Abstract
Background The objective of the study is to define the most appropriate region for intensity normalization in brain 18FDG PET semi-quantitative analysis. The best option could be based on previous absolute quantification studies, which showed that the metabolic changes related to ageing affect the quasi-totality of brain regions in healthy subjects. Consequently, brain metabolic changes related to ageing were evaluated in two populations of healthy controls who underwent conventional (n = 56) or digital (n = 78) 18FDG PET/CT. The median correlation coefficients between age and the metabolism of each 120 atlas brain region were reported for 120 distinct intensity normalizations (according to the 120 regions). SPM linear regression analyses with age were performed on most significant normalizations (FWE, p < 0.05). Results The cerebellum and pons were the two sole regions showing median coefficients of correlation with age less than − 0.5. With SPM, the intensity normalization by the pons provided at least 1.7- and 2.5-fold more significant cluster volumes than other normalizations for conventional and digital PET, respectively. Conclusions The pons is the most appropriate area for brain 18FDG PET intensity normalization for examining the metabolic changes through ageing.
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Affiliation(s)
- A Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France.,IADI, INSERM U1254, Université de Lorraine, 54000, Nancy, France
| | - M Doyen
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France.,IADI, INSERM U1254, Université de Lorraine, 54000, Nancy, France
| | - J Y Campion
- CNRS, Ecole Centrale de Marseille, UMR 7249, Institut Fresnel, Aix-Marseille Université, Marseille, France.,CERIMED, Aix-Marseille University, Marseille, France
| | - Eric Guedj
- CNRS, Ecole Centrale de Marseille, UMR 7249, Institut Fresnel, Aix-Marseille Université, Marseille, France. .,CERIMED, Aix-Marseille University, Marseille, France. .,Department of Nuclear Medicine, Assistance Publique Hôpitaux de Marseille, Timone University Hospital, Marseille, France.
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16
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Gjerum L, Andersen BB, Bruun M, Simonsen AH, Henriksen OM, Law I, Hasselbalch SG, Frederiksen KS. Comparison of the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers in patients suspected of Alzheimer's disease. PLoS One 2021; 16:e0248413. [PMID: 33711065 PMCID: PMC7954298 DOI: 10.1371/journal.pone.0248413] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/26/2021] [Indexed: 12/14/2022] Open
Abstract
Background The two biomarkers 2-[18F]FDG-PET and cerebrospinal fluid biomarkers are both recommended to support the diagnosis of Alzheimer’s disease. However, there is a lack of knowledge for the comparison of the two biomarkers in a routine clinical setting. Objective The aim was to compare the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer’s disease. Methods Eighty-one patients clinically suspected of Alzheimer’s disease were retrospectively included from the Copenhagen Memory Clinic. As part of the clinical work-up all patients had a standard diagnostic program examination including MRI and ancillary investigations with 2-[18F]FDG-PET and cerebrospinal fluid biomarkers. An incremental study design was used to evaluate the clinical impact of the biomarkers. First, the diagnostic evaluation was based on the standard diagnostic program, then the diagnostic evaluation was revised after addition of either cerebrospinal fluid biomarkers or 2-[18F]FDG-PET. At each diagnostic evaluation, two blinded dementia specialists made a consensus decision on diagnosis, prediction of disease course, and change in patient management. Confidence in the decision was measured on a visual analogue scale (0–100). After 6 months, the diagnostic evaluation was performed with addition of the other biomarker. A clinical follow-up after 12 months was used as reference for diagnosis and disease course. Results The two biomarkers had a similar clinical value across all diagnosis when added individually to the standard diagnostic program. However, for the correctly diagnosed patient with Alzheimer’s disease cerebrospinal fluid biomarkers had a significantly higher impact on diagnostic confidence (mean scores±SD: 88±11 vs. 82±11, p = 0.046) and a significant reduction in the need for ancillary investigations (23 vs. 18 patients, p = 0.049) compared to 2-[18F]FDG-PET. Conclusion The two biomarkers had similar clinical impact on diagnosis, but cerebrospinal fluid biomarkers had a more significant value in corroborating the diagnosis of Alzheimer’s disease compared to 2-[18F]FDG-PET.
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Affiliation(s)
- Le Gjerum
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Bo Andersen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Marie Bruun
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anja Hviid Simonsen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Steen Gregers Hasselbalch
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
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17
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Caminiti SP, Sala A, Presotto L, Chincarini A, Sestini S, Perani D, Schillaci O, Berti V, Calcagni ML, Cistaro A, Morbelli S, Nobili F, Pappatà S, Volterrani D, Gobbo CL. Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps. Eur J Nucl Med Mol Imaging 2021; 48:2486-2499. [PMID: 33423088 DOI: 10.1007/s00259-020-05175-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/20/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level. METHODS Selection of HC images was based on visual rating, after Cook's distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB. RESULTS Two-step Cook's distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes. CONCLUSIONS The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings.
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Affiliation(s)
- Silvia Paola Caminiti
- Vita-Salute San Raffaele University, Milan, Italy.,In vivo human molecular and structural neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Arianna Sala
- Vita-Salute San Raffaele University, Milan, Italy.,In vivo human molecular and structural neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Luca Presotto
- Nuclear Medicine Unit, San Raffaele Hospital, Via Olgettina 60, 20132, Milan, Italy
| | | | | | - Daniela Perani
- Vita-Salute San Raffaele University, Milan, Italy. .,In vivo human molecular and structural neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,Nuclear Medicine Unit, San Raffaele Hospital, Via Olgettina 60, 20132, Milan, Italy.
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Mairal E, Doyen M, Rivasseau-Jonveaux T, Malaplate C, Guedj E, Verger A. Clinical impact of digital and conventional PET control databases for semi-quantitative analysis of brain 18F-FDG digital PET scans. EJNMMI Res 2020; 10:144. [PMID: 33258085 PMCID: PMC7704892 DOI: 10.1186/s13550-020-00733-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023] Open
Abstract
PURPOSE Digital PET cameras markedly improve sensitivity and spatial resolution of brain 18F-FDG PET images compared to conventional cameras. Our study aimed to assess whether specific control databases are required to improve the diagnostic performance of these recent advances. METHODS We retrospectively selected two groups of subjects, twenty-seven Alzheimer's Disease (AD) patients and twenty-two healthy control (HC) subjects. All subjects underwent a brain 18F-FDG PET on a digital camera (Vereos, Philips®). These two group (AD and HC) are compared, using a Semi-Quantitative Analysis (SQA), to two age and sex matched controls acquired with a digital PET/CT (Vereos, Philips®) or a conventional PET/CT (Biograph 6, Siemens®) camera, at group and individual levels. Moreover, individual visual interpretation of SPM T-maps was provided for the positive diagnosis of AD by 3 experienced raters. RESULTS At group level, SQA using digital controls detected more marked hypometabolic areas in AD (+ 116 cm3 at p < 0.001 uncorrected for the voxel, corrected for the cluster) than SQA using conventional controls. At the individual level, the accuracy of SQA for discriminating AD using digital controls was higher than SQA using conventional controls (86% vs. 80%, p < 0.01, at p < 0.005 uncorrected for the voxel, corrected for the cluster), with higher sensitivity (89% vs. 78%) and similar specificity (82% vs. 82%). These results were confirmed by visual analysis (accuracies of 84% and 82% for digital and conventional controls respectively, p = 0.01). CONCLUSION There is an urgent need to establish specific digital PET control databases for SQA of brain 18F-FDG PET images as such databases improve the accuracy of AD diagnosis.
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Affiliation(s)
- Elise Mairal
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, Rue du Morvan, 54500, Vandoeuvre-les-Nancy, France
| | - Matthieu Doyen
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, Rue du Morvan, 54500, Vandoeuvre-les-Nancy, France.,IADI, INSERM U1254, Université de Lorraine, 54000, Nancy, France
| | - Thérèse Rivasseau-Jonveaux
- Clinical Memory and Research Center, Department of Geriatrics, CHRU Nancy, Université de Lorraine, 2LPN EA 7489, 54000, Nancy, France
| | - Catherine Malaplate
- Department of Biochemistry, Molecular Biology and Nutrition CHRU Nancy, Université de Lorraine, 54000, Nancy, France
| | - Eric Guedj
- APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Aix Marseille Univ, Marseille, France
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, Rue du Morvan, 54500, Vandoeuvre-les-Nancy, France. .,IADI, INSERM U1254, Université de Lorraine, 54000, Nancy, France.
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Mathies F, Lange C, Mäurer A, Apostolova I, Klutmann S, Buchert R. Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission. J Alzheimers Dis 2020; 77:1609-1622. [PMID: 32925050 DOI: 10.3233/jad-200530] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease. OBJECTIVE This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI. METHODS The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET. RESULTS FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666). CONCLUSION Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.
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Affiliation(s)
- Franziska Mathies
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Anja Mäurer
- Evangelisches Geriatriezentrum Berlin, Berlin, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Susanne Klutmann
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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20
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López-González FJ, Silva-Rodríguez J, Paredes-Pacheco J, Niñerola-Baizán A, Efthimiou N, Martín-Martín C, Moscoso A, Ruibal Á, Roé-Vellvé N, Aguiar P. Intensity normalization methods in brain FDG-PET quantification. Neuroimage 2020; 222:117229. [PMID: 32771619 DOI: 10.1016/j.neuroimage.2020.117229] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/28/2020] [Accepted: 07/31/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output. METHODS Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings. RESULTS Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm3) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering. CONCLUSIONS Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.
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Affiliation(s)
- Francisco J López-González
- Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Jesús Silva-Rodríguez
- R&D Department, Qubiotech Health Intelligence, SL., Rúa Real n° 24, Planta 1, A Coruña, Galicia, Spain; Nuclear Medicine Department & Molecular Imaging Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N 15706, Santiago de Compostela, Galicia, Spain.
| | - José Paredes-Pacheco
- Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Aida Niñerola-Baizán
- Nuclear Medicine Department, Hospital Clínic, Barcelona, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Nikos Efthimiou
- Positron Emission Tomography Research Centre, University of Hull, Hull HU6 7RX, United Kingdom
| | | | - Alexis Moscoso
- Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Nuclear Medicine Department & Molecular Imaging Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N 15706, Santiago de Compostela, Galicia, Spain
| | - Álvaro Ruibal
- Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Nuclear Medicine Department & Molecular Imaging Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N 15706, Santiago de Compostela, Galicia, Spain
| | - Núria Roé-Vellvé
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Pablo Aguiar
- Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Nuclear Medicine Department & Molecular Imaging Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N 15706, Santiago de Compostela, Galicia, Spain.
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21
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Brugnolo A, De Carli F, Pagani M, Morbelli S, Jonsson C, Chincarini A, Frisoni GB, Galluzzi S, Perneczky R, Drzezga A, van Berckel BNM, Ossenkoppele R, Didic M, Guedj E, Arnaldi D, Massa F, Grazzini M, Pardini M, Mecocci P, Dottorini ME, Bauckneht M, Sambuceti G, Nobili F. Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer's Disease. J Alzheimers Dis 2020; 68:383-394. [PMID: 30776000 DOI: 10.3233/jad-181022] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Several automatic tools have been implemented for semi-quantitative assessment of brain [18]F-FDG-PET. OBJECTIVE We aimed to head-to-head compare the diagnostic performance among three statistical parametric mapping (SPM)-based approaches, another voxel-based tool (i.e., PALZ), and a volumetric region of interest (VROI-SVM)-based approach, in distinguishing patients with prodromal Alzheimer's disease (pAD) from controls. METHODS Sixty-two pAD patients (MMSE score = 27.0±1.6) and one hundred-nine healthy subjects (CTR) (MMSE score = 29.2±1.2) were enrolled in five centers of the European Alzheimer's Disease Consortium. The three SPM-based methods, based on different rationales, included 1) a cluster identified through the correlation analysis between [18]F-FDG-PET and a verbal memory test (VROI-1), 2) a VROI derived from the comparison between pAD and CTR (VROI-2), and 3) visual analysis of individual maps obtained by the comparison between each subject and CTR (SPM-Maps). The VROI-SVM approach was based on 6 VROI plus 6 VROI asymmetry values derived from the pAD versus CTR comparison thanks to support vector machine (SVM). RESULTS The areas under the ROC curves between pAD and CTR were 0.84 for VROI-1, 0.83 for VROI-2, 0.79 for SPM maps, 0.87 for PALZ, and 0.95 for VROI-SVM. Pairwise comparisons of Youden index did not show statistically significant differences in diagnostic performance between VROI-1, VROI-2, SPM-Maps, and PALZ score whereas VROI-SVM performed significantly (p < 0.005) better than any of the other methods. CONCLUSION The study confirms the good accuracy of [18]F-FDG-PET in discriminating healthy subjects from pAD and highlights that a non-linear, automatic VROI classifier based on SVM performs better than the voxel-based methods.
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Affiliation(s)
- Andrea Brugnolo
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, Italy.,Clinical Psychology Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Fabrizio De Carli
- Institute of Bioimaging and Molecular Physiology, Consiglio Nazionale delle Ricerche (CNR), Genoa, Italy
| | - Marco Pagani
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy.,Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden
| | - Slivia Morbelli
- Department of Health Sciences (DISSAL), University of Genoa, Italy.,Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cathrine Jonsson
- Medical Radiation Physics and Nuclear Medicine, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden
| | | | - Giovanni B Frisoni
- LENITEM Laboratory of Epidemiology and Neuroimaging, IRCCS S. Giovanni di Dio-FBF, Brescia, Italy.,University Hospitals and University of Geneva, Geneva, Switzerland
| | - Samantha Galluzzi
- LENITEM Laboratory of Epidemiology and Neuroimaging, IRCCS S. Giovanni di Dio-FBF, Brescia, Italy
| | - Robert Perneczky
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Germany.,Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE) Munich, Germany.,Neuroepidemiology and Ageing Research Unit, School of Public Health, Faculty of Medicine, The Imperial College London of Science, Technology and Medicine, London, UK
| | - Alexander Drzezga
- Department of Nuclear Medicine, University Hospital of Cologne, Germany; previously at Department of Nuclear Medicine, Technische Universität, Munich, Germany
| | - Bart N M van Berckel
- Department of Nuclear Medicine & PET Research, VU University Medical Center, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Department of Nuclear Medicine & PET Research, VU University Medical Center, Amsterdam, The Netherlands
| | - Mira Didic
- APHM, CHU Timone, Service de Neurologie et Neuropsychologie, Aix-Marseille University, Marseille, France
| | - Eric Guedj
- APHM, CHU Timone, Service de Médecine Nucléaire, CERIMED, Institut Fresnel, CNRS, Ecole Centrale Marseille, Aix-Marseille University, France
| | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, Italy.,Neurology Clinics, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, Italy
| | - Matteo Grazzini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, Italy
| | - Matteo Pardini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, Italy.,Neurology Clinics, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Patrizia Mecocci
- Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Massimo E Dottorini
- Department of Diagnostic Imaging, Nuclear Medicine Unit, Perugia General Hospital, Perugia, Italy
| | - Matteo Bauckneht
- Department of Health Sciences (DISSAL), University of Genoa, Italy.,Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Gianmario Sambuceti
- Department of Health Sciences (DISSAL), University of Genoa, Italy.,Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, Italy.,Neurology Clinics, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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22
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Niñerola-Baizán A, Aguiar P, Cabrera-Martín M, Vigil C, Gómez-Grande A, Lorenzo C, Rubí S, Sopena P, Camacho V. Relevance of quantification in brain PET studies with 18F-FDG. Rev Esp Med Nucl Imagen Mol 2020. [DOI: 10.1016/j.remnie.2020.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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23
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Niñerola-Baizán A, Aguiar P, Cabrera-Martín MN, Vigil C, Gómez-Grande A, Lorenzo C, Rubí S, Sopena P, Camacho V. Relevance of quantification in brain PET studies with 18F-FDG. Rev Esp Med Nucl Imagen Mol 2020; 39:184-192. [PMID: 32345572 DOI: 10.1016/j.remn.2020.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 03/01/2020] [Accepted: 03/03/2020] [Indexed: 12/14/2022]
Abstract
The inclusion of 18F-FDG PET as a biomarker in the diagnostic criteria of neurodegenerative diseases and its indication in the presurgical assessment for drug-resistant epilepsies allow to improve specificity of these diagnosis. The traditional interpretation of neurological PET studies has been performed qualitatively, although in the last decade, several quantitative evaluation methods have emerged. This technical development has become relevant in clinical practice, improving specificity, reproducibility and reducing the interrater reliability derived from visual analysis. In this article we update/review the main imaging processing techniques currently used. This may allow the Nuclear Medicine physician to know their advantages and disadvantages when including these procedures in daily clinical practice.
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Affiliation(s)
- A Niñerola-Baizán
- Servicio de Medicina Nuclear, Hospital Clínic, Barcelona, España; Grupo de Imagen Biomédica, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, España
| | - P Aguiar
- Grupo de Imaxe Molecular e Física Médica, Departamento de Radioloxía, Facultade de Medicina, Universidade de Santiago de Compostela, Santiago de Compostela, España; Servicio de Medicina Nuclear, Hospital Clínico de Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, España
| | - M N Cabrera-Martín
- Servicio de Medicina Nuclear, Hospital Clínico San Carlos, Madrid, España
| | - C Vigil
- Servicio Medicina Nuclear, Hospital Universitario Central de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, España.
| | - A Gómez-Grande
- Servicio de Medicina Nuclear, Hospital Universitario 12 de Octubre, Madrid, España
| | - C Lorenzo
- Servicio de Medicina Nuclear, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, España
| | - S Rubí
- Servicio de Medicina Nuclear, Hospital Universitari Son Espases, Institut d'Investigació Sanitària Illes Balears (IdISBa), Palma, España
| | - P Sopena
- Servicio de Medicina Nuclear, Hospital Vithas-Nisa 9 de Octubre, Valencia, España; Servicio de Medicina Nuclear, Hospital Universitario y Politécnico La Fe, Valencia, España
| | - V Camacho
- Servicio de Medicina Nuclear, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, España
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24
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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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25
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Boccardi M, Nicolosi V, Festari C, Bianchetti A, Cappa S, Chiasserini D, Falini A, Guerra UP, Nobili F, Padovani A, Sancesario G, Morbelli S, Parnetti L, Tiraboschi P, Muscio C, Perani D, Pizzini FB, Beltramello A, Salvini Porro G, Ciaccio M, Schillaci O, Trabucchi M, Tagliavini F, Frisoni GB. Italian consensus recommendations for a biomarker-based aetiological diagnosis in mild cognitive impairment patients. Eur J Neurol 2019; 27:475-483. [PMID: 31692118 DOI: 10.1111/ene.14117] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 11/04/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Biomarkers support the aetiological diagnosis of neurocognitive disorders in vivo. Incomplete evidence is available to drive clinical decisions; available diagnostic algorithms are generic and not very helpful in clinical practice. The aim was to develop a biomarker-based diagnostic algorithm for mild cognitive impairment patients, leveraging on knowledge from recognized national experts. METHODS With a Delphi procedure, experienced clinicians making variable use of biomarkers in clinical practice and representing five Italian scientific societies (neurology - Società Italiana di Neurologia per le Demenze; neuroradiology - Associazione Italiana di Neuroradiologia; biochemistry - Società Italiana di Biochimica Clinica; psychogeriatrics - Associazione Italiana di Psicogeriatria; nuclear medicine - Associazione Italiana di Medicina Nucleare) defined the theoretical framework, relevant literature, the diagnostic issues to be addressed and the diagnostic algorithm. An N-1 majority defined consensus achievement. RESULTS The panellists chose the 2011 National Institute on Aging and Alzheimer's Association diagnostic criteria as the reference theoretical framework and defined the algorithm in seven Delphi rounds. The algorithm includes baseline clinical and cognitive assessment, blood examination, and magnetic resonance imaging with exclusionary and inclusionary roles; dopamine transporter single-photon emission computed tomography (if no/unclear parkinsonism) or metaiodobenzylguanidine cardiac scintigraphy for suspected dementia with Lewy bodies with clear parkinsonism (round VII, votes (yes-no-abstained): 3-1-1); 18 F-fluorodeoxyglucose positron emission tomography for suspected frontotemporal lobar degeneration and low diagnostic confidence of Alzheimer's disease (round VII, 4-0-1); cerebrospinal fluid for suspected Alzheimer's disease (round IV, 4-1-0); and amyloid positron emission tomography if cerebrospinal fluid was not possible/accepted (round V, 4-1-0) or inconclusive (round VI, 5-0-0). CONCLUSIONS These consensus recommendations can guide clinicians in the biomarker-based aetiological diagnosis of mild cognitive impairment, whilst guidelines cannot be defined with evidence-to-decision procedures due to incomplete evidence.
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Affiliation(s)
- M Boccardi
- IRCCS Istituto Centro S.Giovanni di Dio-Fatebenefratelli, Brescia, Italy.,University of Geneva, Geneva, Switzerland
| | - V Nicolosi
- IRCCS Istituto Centro S.Giovanni di Dio-Fatebenefratelli, Brescia, Italy
| | - C Festari
- IRCCS Istituto Centro S.Giovanni di Dio-Fatebenefratelli, Brescia, Italy.,University of Brescia, Brescia, Italy
| | - A Bianchetti
- Istituto Clinico S. Anna, Brescia, Italy.,Italian Psychogeriatric Association (AIP), Brescia, Italy
| | - S Cappa
- IRCCS Istituto Centro S.Giovanni di Dio-Fatebenefratelli, Brescia, Italy.,University Institute of Higher Studies, Pavia, Italy.,Italian Society of Neurology for the Study of the Dementias (SINdem), Milan, Italy
| | - D Chiasserini
- University of Perugia, Perugia, Italy.,Italian Society of Clinical Biochemistry and Clinical Molecular Biology - Laboratory Medicine (SIBioC), Rimini, Italy
| | - A Falini
- IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Italian Association of Neuroradiology (AINR), Milan, Italy
| | - U P Guerra
- Poliambulanza Foundation, Brescia, Italy.,Italian Association of Nuclear Medicine (AIMN), Bari, Italy
| | - F Nobili
- Italian Association of Nuclear Medicine (AIMN), Bari, Italy.,University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - A Padovani
- Italian Society of Neurology for the Study of the Dementias (SINdem), Milan, Italy.,Brescia University Hospital, Brescia, Italy
| | - G Sancesario
- Italian Society of Clinical Biochemistry and Clinical Molecular Biology - Laboratory Medicine (SIBioC), Rimini, Italy.,IRCCS Santa Lucia Foundation, Neuroimmunology Unit Via Ardeatina 354, Rome, Italy
| | - S Morbelli
- University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - L Parnetti
- Ospedale S. Maria della Misericordia, University of Perugia, Perugia, Italy
| | | | - C Muscio
- IRCCS 'Carlo Besta', Milan, Italy
| | - D Perani
- IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | | | - A Beltramello
- Verona University Hospital, Verona, Italy.,IRCCS 'Sacro Cuore-Don Calabria', Negrar, Verona, Italy
| | | | - M Ciaccio
- Italian Society of Clinical Biochemistry and Clinical Molecular Biology - Laboratory Medicine (SIBioC), Rimini, Italy.,University of Palermo, Palermo, Italy
| | - O Schillaci
- University Tor Vergata, Rome, Italy.,IRCCS-Neuromed, Pozzilli, Italy
| | - M Trabucchi
- Italian Psychogeriatric Association (AIP), Brescia, Italy.,University Tor Vergata, Rome, Italy
| | | | - G B Frisoni
- IRCCS Istituto Centro S.Giovanni di Dio-Fatebenefratelli, Brescia, Italy.,University of Geneva, Geneva, Switzerland
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26
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Trošt M, Perovnik M, Pirtošek Z. Correlations of Neuropsychological and Metabolic Brain Changes in Parkinson's Disease and Other α-Synucleinopathies. Front Neurol 2019; 10:1204. [PMID: 31798525 PMCID: PMC6868095 DOI: 10.3389/fneur.2019.01204] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 10/28/2019] [Indexed: 12/14/2022] Open
Abstract
Cognitive impairment is a common feature in Parkinson's disease (PD) and other α-synucleinopathies as 80% of PD patients develop dementia within 20 years. Early cognitive changes in PD patients present as a dysexecutive syndrome, broadly characterized as a disruption of the fronto-striatal dopamine network. Cognitive deficits in other domains (recognition memory, attention processes and visuospatial abilities) become apparent with the progression of PD and development of dementia. In dementia with Lewy bodies (DLB) the cognitive impairment develops early or even precedes parkinsonism and it is more pronounced in visuospatial skills and memory. Cognitive impairment in the rarer α-synucleinopathies (multiple system atrophy and pure autonomic failure) is less well studied. Metabolic brain imaging with positron emission tomography and [18F]-fluorodeoxyglucose (FDG-PET) is a well-established diagnostic method in neurodegenerative diseases, including dementias. Changes in glucose metabolism precede those seen on structural magnetic resonance imaging (MRI). Reduction in glucose metabolism and atrophy have been suggested to represent consecutive changes of neurodegeneration and are linked to specific cognitive disorders (e.g., dysexecutive syndrome, memory impairment, visuospatial deficits etc.). Advances in the statistical analysis of FDG-PET images enabling a network analysis broadened our understanding of neurodegenerative brain processes. A specific cognitive pattern related to PD was identified by applying voxel-based network modeling approach. The magnitude of this pattern correlated significantly with patients' cognitive skills. Specific metabolic brain changes were observed also in patients with DLB as well as in a prodromal phase of α-synucleinopathy: REM sleep behavior disorder. Metabolic brain imaging with FDG-PET is a reliable biomarker of neurodegenerative brain diseases throughout their course, precisely reflecting their topographic distribution, stage and functional impact.
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Affiliation(s)
- Maja Trošt
- Department for Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.,Department for Nuclear Medicine, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Matej Perovnik
- Department for Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Zvezdan Pirtošek
- Department for Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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O'Sullivan S, Heinsen H, Grinberg LT, Chimelli L, Amaro E, do Nascimento Saldiva PH, Jeanquartier F, Jean-Quartier C, da Graça Morais Martin M, Sajid MI, Holzinger A. The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure. Brain Inform 2019; 6:3. [PMID: 30843118 PMCID: PMC6403267 DOI: 10.1186/s40708-019-0096-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 01/16/2019] [Indexed: 02/06/2023] Open
Abstract
Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an ever-increasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI- and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.
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Affiliation(s)
- Shane O'Sullivan
- Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.
| | - Helmut Heinsen
- Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.,Morphological Brain Research Unit, University of Würzburg, Würzburg, Germany.,Institute of Radiology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Lea Tenenholz Grinberg
- Morphological Brain Research Unit, University of Würzburg, Würzburg, Germany.,Aging Brain Project, Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.,Albert Einstein Instituto Israelita de Ensino e Pesquisa, São Paulo, Brazil
| | - Leila Chimelli
- Laboratory of Neuropathology, State Institute of Brain, Rio de Janeiro, Brazil
| | - Edson Amaro
- Institute of Radiology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Paulo Hilário do Nascimento Saldiva
- Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.,Institute of Advanced Studies, Universidade de Sao Paulo, São Paulo, Brazil
| | - Fleur Jeanquartier
- Holzinger Group, Institute for Medical Informatics and Statistics, Medical University of Graz, Graz, Austria
| | - Claire Jean-Quartier
- Holzinger Group, Institute for Medical Informatics and Statistics, Medical University of Graz, Graz, Austria
| | | | - Mohammed Imran Sajid
- Department of Upper GI Surgery, Wirral University Teaching Hospital, Birkenhead, United Kingdom
| | - Andreas Holzinger
- Holzinger Group, Institute for Medical Informatics and Statistics, Medical University of Graz, Graz, Austria
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Nobili F, Arbizu J, Bouwman F, Drzezga A, Agosta F, Nestor P, Walker Z, Boccardi M. European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus. Eur J Neurol 2018; 25:1201-1217. [PMID: 29932266 DOI: 10.1111/ene.13728] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 06/20/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND AND PURPOSE Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured. METHODS Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion. RESULTS Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline. CONCLUSIONS Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.
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Affiliation(s)
- F Nobili
- Department of Neuroscience (DINOGMI), University of Genoa and Polyclinic San Martino Hospital, Genoa, Italy
| | - J Arbizu
- Department of Nuclear Medicine, Clinica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - F Bouwman
- Department of Neurology and Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - A Drzezga
- Department of Nuclear Medicine, University Hospital of Cologne, University of Cologne and German Center for Neurodegenerative Diseases (DZNE), Cologne, Germany
| | - F Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - P Nestor
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Z Walker
- Division of Psychiatry, Essex Partnership University NHS Foundation Trust, University College London, London, UK
| | - M Boccardi
- Department of Psychiatry, Laboratoire du Neuroimagerie du Vieillissement (LANVIE), University of Geneva, Geneva, Switzerland
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Walker Z, Gandolfo F, Orini S, Garibotto V, Agosta F, Arbizu J, Bouwman F, Drzezga A, Nestor P, Boccardi M, Altomare D, Festari C, Nobili F. Clinical utility of FDG PET in Parkinson's disease and atypical parkinsonism associated with dementia. Eur J Nucl Med Mol Imaging 2018; 45:1534-1545. [PMID: 29779045 PMCID: PMC6061481 DOI: 10.1007/s00259-018-4031-2] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 04/16/2018] [Indexed: 12/11/2022]
Abstract
Purpose There are no comprehensive guidelines for the use of FDG PET in the following three clinical scenarios: (1) diagnostic work-up of patients with idiopathic Parkinson’s disease (PD) at risk of future cognitive decline, (2) discriminating idiopathic PD from progressive supranuclear palsy, and (3) identifying the underlying neuropathology in corticobasal syndrome. Methods We therefore performed three literature searches and evaluated the selected studies for quality of design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were the sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiving operating characteristic curve, and positive/negative likelihood ratio of FDG PET in detecting the target condition. Using the Delphi method, a panel of seven experts voted for or against the use of FDG PET based on published evidence and expert opinion. Results Of 91 studies selected from the three literature searches, only four included an adequate quantitative assessment of the performance of FDG PET. The majority of studies lacked robust methodology due to lack of critical outcomes, inadequate gold standard and no head-to-head comparison with an appropriate reference standard. The panel recommended the use of FDG PET for all three clinical scenarios based on nonquantitative evidence of clinical utility. Conclusion Despite widespread use of FDG PET in clinical practice and extensive research, there is still very limited good quality evidence for the use of FDG PET. However, in the opinion of the majority of the panellists, FDG PET is a clinically useful imaging biomarker for idiopathic PD and atypical parkinsonism associated with dementia.
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Affiliation(s)
- Zuzana Walker
- Division of Psychiatry, University College London, London, UK. .,St Margaret's Hospital, Essex Partnership University NHS Foundation Trust, Epping, CM16 6TN, UK.
| | - Federica Gandolfo
- Alzheimer Operative Unit, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Stefania Orini
- Alzheimer Operative Unit, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, University Hospitals of Geneva, Geneva University, Geneva, Switzerland
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Javier Arbizu
- Department of Nuclear Medicine, Clinica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Femke Bouwman
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - Alexander Drzezga
- Department of Nuclear Medicine, University Hospital of Cologne, University of Cologne and German Center for Neurodegenerative Diseases (DZNE), Cologne, Germany
| | - Peter Nestor
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Queensland Brain Institute, University of Queensland and the Mater Hospital, Brisbane, Australia
| | - Marina Boccardi
- LANVIE (Laboratoire de Neuroimagerie du Vieillissement), Department of Psychiatry, University of Geneva, Geneva, Switzerland.,LANE - Laboratory of Alzheimer's Neuroimaging & Epidemiology, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Daniele Altomare
- LANE - Laboratory of Alzheimer's Neuroimaging & Epidemiology, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Cristina Festari
- LANE - Laboratory of Alzheimer's Neuroimaging & Epidemiology, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa & Clinical Neurology Polyclinic IRCCS San Martino-IST, Genoa, Italy.
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Boccardi M, Festari C, Altomare D, Gandolfo F, Orini S, Nobili F, Frisoni GB. Assessing FDG-PET diagnostic accuracy studies to develop recommendations for clinical use in dementia. Eur J Nucl Med Mol Imaging 2018; 45:1470-1486. [DOI: 10.1007/s00259-018-4024-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 04/13/2018] [Indexed: 12/14/2022]
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Clinical utility of FDG-PET for the clinical diagnosis in MCI. Eur J Nucl Med Mol Imaging 2018; 45:1497-1508. [DOI: 10.1007/s00259-018-4039-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Accepted: 04/19/2018] [Indexed: 10/17/2022]
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