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Wyman-Chick KA, Chaudhury P, Bayram E, Abdelnour C, Matar E, Chiu SY, Ferreira D, Hamilton CA, Donaghy PC, Rodriguez-Porcel F, Toledo JB, Habich A, Barrett MJ, Patel B, Jaramillo-Jimenez A, Scott GD, Kane JPM. Differentiating Prodromal Dementia with Lewy Bodies from Prodromal Alzheimer's Disease: A Pragmatic Review for Clinicians. Neurol Ther 2024; 13:885-906. [PMID: 38720013 PMCID: PMC11136939 DOI: 10.1007/s40120-024-00620-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 04/05/2024] [Indexed: 05/12/2024] Open
Abstract
This pragmatic review synthesises the current understanding of prodromal dementia with Lewy bodies (pDLB) and prodromal Alzheimer's disease (pAD), including clinical presentations, neuropsychological profiles, neuropsychiatric symptoms, biomarkers, and indications for disease management. The core clinical features of dementia with Lewy bodies (DLB)-parkinsonism, complex visual hallucinations, cognitive fluctuations, and REM sleep behaviour disorder are common prodromal symptoms. Supportive clinical features of pDLB include severe neuroleptic sensitivity, as well as autonomic and neuropsychiatric symptoms. The neuropsychological profile in mild cognitive impairment attributable to Lewy body pathology (MCI-LB) tends to include impairment in visuospatial skills and executive functioning, distinguishing it from MCI due to AD, which typically presents with impairment in memory. pDLB may present with cognitive impairment, psychiatric symptoms, and/or recurrent episodes of delirium, indicating that it is not necessarily synonymous with MCI-LB. Imaging, fluid and other biomarkers may play a crucial role in differentiating pDLB from pAD. The current MCI-LB criteria recognise low dopamine transporter uptake using positron emission tomography or single photon emission computed tomography (SPECT), loss of REM atonia on polysomnography, and sympathetic cardiac denervation using meta-iodobenzylguanidine SPECT as indicative biomarkers with slowing of dominant frequency on EEG among others as supportive biomarkers. This review also highlights the emergence of fluid and skin-based biomarkers. There is little research evidence for the treatment of pDLB, but pharmacological and non-pharmacological treatments for DLB may be discussed with patients. Non-pharmacological interventions such as diet, exercise, and cognitive stimulation may provide benefit, while evaluation and management of contributing factors like medications and sleep disturbances are vital. There is a need to expand research across diverse patient populations to address existing disparities in clinical trial participation. In conclusion, an early and accurate diagnosis of pDLB or pAD presents an opportunity for tailored interventions, improved healthcare outcomes, and enhanced quality of life for patients and care partners.
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Affiliation(s)
- Kathryn A Wyman-Chick
- Struthers Parkinson's Center and Center for Memory and Aging, Department of Neurology, HealthPartners/Park Nicollet, Bloomington, USA.
| | - Parichita Chaudhury
- Cleo Roberts Memory and Movement Center, Banner Sun Health Research Institute, Sun City, USA
| | - Ece Bayram
- Parkinson and Other Movement Disorders Center, University of California San Diego, San Diego, USA
| | - Carla Abdelnour
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | - Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Shannon Y Chiu
- Department of Neurology, Mayo Clinic Arizona, Phoenix, USA
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Solna, Sweden
- Department of Radiology, Mayo Clinic Rochester, Rochester, USA
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, Spain
| | - Calum A Hamilton
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - Paul C Donaghy
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | | | - Jon B Toledo
- Nantz National Alzheimer Center, Stanley Appel Department of Neurology, Houston Methodist Hospital, Houston, USA
| | - Annegret Habich
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Solna, Sweden
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Matthew J Barrett
- Department of Neurology, Parkinson's and Movement Disorders Center, Virginia Commonwealth University, Richmond, USA
| | - Bhavana Patel
- Department of Neurology, College of Medicine, University of Florida, Gainesville, USA
- Norman Fixel Institute for Neurologic Diseases, University of Florida, Gainesville, USA
| | - Alberto Jaramillo-Jimenez
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
- School of Medicine, Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia
| | - Gregory D Scott
- Department of Pathology and Laboratory Services, VA Portland Medical Center, Portland, USA
| | - Joseph P M Kane
- Centre for Public Health, Queen's University Belfast, Belfast, UK
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Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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Nakata T, Shimada K, Iba A, Oda H, Terashima A, Koide Y, Kawasaki R, Yamada T, Ishii K. Differential diagnosis of MCI with Lewy bodies and MCI due to Alzheimer's disease by visual assessment of occipital hypoperfusion on SPECT images. Jpn J Radiol 2024; 42:308-318. [PMID: 37861956 DOI: 10.1007/s11604-023-01501-3] [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: 06/30/2023] [Accepted: 09/27/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) or dementia with Lewy bodies (DLB) is important. We evaluated morphological and functional differences between MCI with Lewy bodies (MCI-LB) and MCI due to AD (MCI-AD), and a method for differentiating between these conditions using brain MRI and brain perfusion SPECT. METHODS A continuous series of 101 subjects, who had visited our memory clinic and met the definition of MCI, were enrolled retrospectively. They were consisted of 60 MCI-LB and 41 MCI-AD subjects. Relative cerebral blood flow (rCBF) on SPECT images and relative brain atrophy on MRI images were evaluated. We performed voxel-based analysis and visually inspected brain perfusion SPECT images for regional brain atrophy, occipital hypoperfusion and the cingulate island sign (CIS), for differential diagnosis of MCI-LB and MCI-AD. RESULTS MRI showed no significant differences in regional atrophy between the MCI-LB and MCI-AD groups. In MCI-LB subjects, occipital rCBF was significantly decreased compared with MCI-AD subjects (p < 0.01, family wise error [FWE]-corrected). Visual inspection of occipital hypoperfusion had sensitivity, specificity, and accuracy values of 100%, 73.2% and 89.1%, respectively, for differentiating MCI-LB and MCI-AD. Occipital hypoperfusion was offered higher diagnostic utility than the CIS. CONCLUSIONS The occipital lobe was the region with significantly decreased rCBF in MCI-LB compared with MCI-AD subjects. Occipital hypoperfusion on brain perfusion SPECT may be a more useful imaging biomarker than the CIS for visually differentiating MCI-LB and MCI-AD.
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Affiliation(s)
- Takashi Nakata
- Neurocognitive Disorders Medical Center, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, 670-8560, Japan.
- Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohnohigashi, Osakasayama, Osaka, Japan.
- Department of Aging Brain and Cognitive Disorders, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himji, Hyogo, Japan.
| | - Kenichi Shimada
- Neurocognitive Disorders Medical Center, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, 670-8560, Japan
- Department of Aging Brain and Cognitive Disorders, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himji, Hyogo, Japan
| | - Akiko Iba
- Department of Aging Brain and Cognitive Disorders, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himji, Hyogo, Japan
- Department of Psychiatry, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, Japan
- Hyogo Mental Health Center, 3 Noborio, Kamitanigami, Yamadacho, Kita-Ku, Kobe, Hyogo, Japan
| | - Haruhiko Oda
- Neurocognitive Disorders Medical Center, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, 670-8560, Japan
- Department of Aging Brain and Cognitive Disorders, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himji, Hyogo, Japan
- Hyogo Mental Health Center, 3 Noborio, Kamitanigami, Yamadacho, Kita-Ku, Kobe, Hyogo, Japan
| | - Akira Terashima
- Neurocognitive Disorders Medical Center, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, 670-8560, Japan
- Department of Aging Brain and Cognitive Disorders, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himji, Hyogo, Japan
| | - Yutaka Koide
- Department of Diagnostic and Interventional Radiology, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, Japan
- Department of Radiology and Nuclear Medicine, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himeji, Hyogo, Japan
| | - Ryota Kawasaki
- Department of Diagnostic and Interventional Radiology, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, Japan
- Department of Radiology and Nuclear Medicine, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himeji, Hyogo, Japan
| | - Takahiro Yamada
- Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohnohigashi, Osakasayama, Osaka, Japan
| | - Kazunari Ishii
- Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohnohigashi, Osakasayama, Osaka, Japan
- Department of Diagnostic and Interventional Radiology, Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo, Japan
- Department of Radiology and Nuclear Medicine, Hyogo Brain and Heart Center, 520 Saisho-Ko, Himeji, Hyogo, Japan
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Ekmekcioglu O, Albert NL, Heinrich K, Tolboom N, Van Weehaeghe D, Traub-Weidinger T, Atay LO, Garibotto V, Morbelli S. Neurological Disorders and Women's Health: Contribution of Molecular Neuroimaging Techniques. Semin Nucl Med 2024; 54:237-246. [PMID: 38365546 DOI: 10.1053/j.semnuclmed.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/18/2024]
Abstract
Sex differences in brain physiology and the mechanisms of drug action have been extensively reported. These biological variances, from structure to hormonal and genetic aspects, can profoundly influence healthy functioning and disease mechanisms and might have implications for treatment and drug development. Molecular neuroimaging techniques may help to disclose sex's impact on brain functioning, as well as the neuropathological changes underpinning several diseases. This narrative review summarizes recent lines of evidence based on PET and SPECT imaging, highlighting sex differences in normal conditions and various neurological disorders.
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Affiliation(s)
- Ozgul Ekmekcioglu
- Department of Nuclear Medicine, University of Health Sciences, Sisli Hamidiye Etfal Education and Research Hospital, Istanbul, Turkey.
| | - Nathalie L Albert
- Department of Nuclear Medicine, LMU University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Kathrin Heinrich
- Department of Medicine III, LMU University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Tatiana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Faculty of Medicine, University of Geneva, CIBM Center for Biomedical Imaging, Geneva, Switzerland
| | - Silvia Morbelli
- Nuclear Medicine Unit, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
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Khatri U, Kwon GR. Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer's Disease Progression Using 18F-FDG PET. Bioengineering (Basel) 2023; 10:1225. [PMID: 37892955 PMCID: PMC10603890 DOI: 10.3390/bioengineering10101225] [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: 09/20/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early and accurate prediction of AD progression is crucial for early intervention and personalized treatment planning. Although AD does not yet have a reliable therapy, several medications help slow down the disease's progression. However, more study is still needed to develop reliable methods for detecting AD and its phases. In the recent past, biomarkers associated with AD have been identified using neuroimaging methods. To uncover biomarkers, deep learning techniques have quickly emerged as a crucial methodology. A functional molecular imaging technique known as fluorodeoxyglucose positron emission tomography (18F-FDG-PET) has been shown to be effective in assisting researchers in understanding the morphological and neurological alterations to the brain associated with AD. Convolutional neural networks (CNNs) have also long dominated the field of AD progression and have been the subject of substantial research, while more recent approaches like vision transformers (ViT) have not yet been fully investigated. In this paper, we present a self-supervised learning (SSL) method to automatically acquire meaningful AD characteristics using the ViT architecture by pretraining the feature extractor using the self-distillation with no labels (DINO) and extreme learning machine (ELM) as classifier models. In this work, we examined a technique for predicting mild cognitive impairment (MCI) to AD utilizing an SSL model which learns powerful representations from unlabeled 18F-FDG PET images, thus reducing the need for large-labeled datasets. In comparison to several earlier approaches, our strategy showed state-of-the-art classification performance in terms of accuracy (92.31%), specificity (90.21%), and sensitivity (95.50%). Then, to make the suggested model easier to understand, we highlighted the brain regions that significantly influence the prediction of MCI development. Our methods offer a precise and efficient strategy for predicting the transition from MCI to AD. In conclusion, this research presents a novel Explainable SSL-ViT model that can accurately predict AD progress based on 18F-FDG PET scans. SSL, attention, and ELM mechanisms are integrated into the model to make it more predictive and interpretable. Future research will enable the development of viable treatments for neurodegenerative disorders by combining brain areas contributing to projection with observed anatomical traits.
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Affiliation(s)
| | - Goo-Rak Kwon
- Department of Information and Communication Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea;
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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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Donaghy PC, Carrarini C, Ferreira D, Habich A, Aarsland D, Babiloni C, Bayram E, Kane JP, Lewis SJ, Pilotto A, Thomas AJ, Bonanni L. Research diagnostic criteria for mild cognitive impairment with Lewy bodies: A systematic review and meta-analysis. Alzheimers Dement 2023; 19:3186-3202. [PMID: 37096339 PMCID: PMC10695683 DOI: 10.1002/alz.13105] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/26/2023]
Abstract
INTRODUCTION Operationalized research criteria for mild cognitive impairment with Lewy bodies (MCI-LB) were published in 2020. The aim of this systematic review and meta-analysis was to review the evidence for the diagnostic clinical features and biomarkers in MCI-LB set out in the criteria. METHODS MEDLINE, PubMed, and Embase were searched on 9/28/22 for relevant articles. Articles were included if they presented original data reporting the rates of diagnostic features in MCI-LB. RESULTS Fifty-seven articles were included. The meta-analysis supported the inclusion of the current clinical features in the diagnostic criteria. Evidence for striatal dopaminergic imaging and meta-iodobenzylguanidine cardiac scintigraphy, though limited, supports their inclusion. Quantitative electroencephalogram (EEG) and fluorodeoxyglucose positron emission tomography (PET) show promise as diagnostic biomarkers. DISCUSSION The available evidence largely supports the current diagnostic criteria for MCI-LB. Further evidence will help refine the diagnostic criteria and understand how best to apply them in clinical practice and research. HIGHLIGHTS A meta-analysis of the diagnostic features of MCI-LB was carried out. The four core clinical features were more common in MCI-LB than MCI-AD/stable MCI. Neuropsychiatric and autonomic features were also more common in MCI-LB. More evidence is needed for the proposed biomarkers. FDG-PET and quantitative EEG show promise as diagnostic biomarkers in MCI-LB.
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Affiliation(s)
- Paul C Donaghy
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Claudia Carrarini
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy
- IRCCS San Raffaele Pisana, Rome, Italy
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Annegret Habich
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Centre for Age-Related Diseases, Stavanger University Hospital, Stavanger, Norway
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
- Hospital San Raffaele of Cassino, Cassino, Italy
| | - Ece Bayram
- Parkinson and Other Movement Disorders Center, Department of Neurosciences, University of California San Diego, California, USA
| | - Joseph Pm Kane
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Simon Jg Lewis
- Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, Australia
| | - Andrea Pilotto
- Department of Clinical and Experimental Sciences, Neurology Unit, University of Brescia, Brescia, Italy
| | - Alan J Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
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Hansen N, Bouter C, Müller SJ, van Riesen C, Khadhraoui E, Ernst M, Riedel CH, Wiltfang J, Lange C. New Insights into Potential Biomarkers in Patients with Mild Cognitive Impairment Occurring in the Prodromal Stage of Dementia with Lewy Bodies. Brain Sci 2023; 13:brainsci13020242. [PMID: 36831785 PMCID: PMC9953759 DOI: 10.3390/brainsci13020242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/14/2023] [Accepted: 01/22/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Prodromal dementia with Lewy bodies (DLB) can emerge with the onset of mild cognitive impairment (MCI). Standard biomarkers can help identify such patients to improve therapy and treatment strategies. Our review aims to describe the latest evidence on promising biomarkers in prodromal DLB with MCI onset (MCI-LB). METHODS We selected articles on different biomarkers in MCI-LB from PubMed and conducted a narrative review. RESULTS We identified potentially promising clinical biomarkers, e.g., (1) assessing autonomic symptoms specifically, (2) describing the cognitive profile in several subdomains including executive and visual functions, and (3) measuring the speed of speech. In addition, we describe the measurement of seeding amplification assays of alpha-synuclein in cerebrospinal fluid as a relevant biomarker for MCI-LB. Electroencephalographic markers, as in calculating the theta/beta ratio or intermittent delta activity, or analyzing peak frequency in electroencephalography-methods also potentially useful once they have been validated in large patient cohorts. The 18F fluorodesoxyglucose positron emission tomography (FDG-PET) technique is also discussed to investigate metabolic signatures, as well as a specific magnetic resonance imaging (MRI) technique such as for the volumetric region of interest analysis. CONCLUSIONS These biomarker results suggest that MCI-LB is a promising field for the use of biomarkers other than established ones to diagnose early prodromal DLB. Further large-scale studies are needed to better evaluate and subsequently use these promising biomarkers in prodromal DLB.
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Affiliation(s)
- Niels Hansen
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- Correspondence:
| | - Caroline Bouter
- Department of Nuclear Medicine, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Sebastian Johannes Müller
- Institute of Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Christoph van Riesen
- Department of Neurology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Eya Khadhraoui
- Institute of Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Marielle Ernst
- Institute of Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Christian Heiner Riedel
- Institute of Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 37075 Göttingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Claudia Lange
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
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9
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Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism on 18F-FDG PET/CT and Automated Classifiers. Diagnostics (Basel) 2022; 12:diagnostics12102425. [PMID: 36292114 PMCID: PMC9601187 DOI: 10.3390/diagnostics12102425] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/22/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: We evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain 18F-FDG PET/CT to improve the differential diagnosis between Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). Procedures: We retrospectively analyzed a total of 150 consecutive patients who underwent diagnostic evaluation for suspected AD (n = 67) or MCI (n = 83). All patients received brain 18F-FDG PET/CT according to the international guidelines, and images were analyzed both Qualitatively (QL) and Quantitatively (QN), the latter by a fully automated post-processing software that produced a z score metabolic map of 25 anatomically different cortical regions. A subset of n = 122 cases with a confirmed diagnosis of AD (n = 53) or MDI (n = 69) by 18–24-month clinical follow-up was finally included in the study. Univariate analysis and three automated classification models (classification tree –ClT-, ridge classifier –RC- and linear Support Vector Machine –lSVM-) were considered to estimate the ability of the z scores to discriminate between AD and MCI cases in. Results: The univariate analysis returned 14 areas where the z scores were significantly different between AD and MCI groups, and the classification accuracy ranged between 74.59% and 76.23%, with ClT and RC providing the best results. The best classification strategy consisted of one single split with a cut-off value of ≈ −2.0 on the z score from temporal lateral left area: cases below this threshold were classified as AD and those above the threshold as MCI. Conclusions: Our findings confirm the usefulness of brain 18F-FDG PET/CT QL and QN analyses in differentiating AD from MCI. Moreover, the combined use of automated classifications models can improve the diagnostic process since its use allows identification of a specific hypometabolic area involved in AD cases in respect to MCI. This data improves the traditional 18F-FDG PET/CT image interpretation and the diagnostic assessment of cognitive disorders.
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10
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Classification of Alzheimer’s Disease Using Dual-Phase 18F-Florbetaben Image with Rank-Based Feature Selection and Machine Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
18F-florbetaben (FBB) positron emission tomography is a representative imaging test that observes amyloid deposition in the brain. Compared to delay-phase FBB (dFBB), early-phase FBB shows patterns related to glucose metabolism in 18F-fluorodeoxyglucose perfusion images. The purpose of this study is to prove that classification accuracy is higher when using dual-phase FBB (dual FBB) versus dFBB quantitative analysis by using machine learning and to find an optimal machine learning model suitable for dual FBB quantitative analysis data. The key features of our method are (1) a feature ranking method for each phase of FBB with a cross-validated F1 score and (2) a quantitative diagnostic model based on machine learning methods. We compared four classification models: support vector machine, naïve Bayes, logistic regression, and random forest (RF). In composite standardized uptake value ratio, RF achieved the best performance (F1: 78.06%) with dual FBB, which was 4.83% higher than the result with dFBB. In conclusion, regardless of the two quantitative analysis methods, using the dual FBB has a higher classification accuracy than using the dFBB. The RF model is the machine learning model that best classifies a dual FBB. The regions that have the greatest influence on the classification of dual FBB are the frontal and temporal lobes.
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11
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Perovnik M, Tomše P, Jamšek J, Tang C, Eidelberg D, Trošt M. Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography. Neuroimage Clin 2022; 35:103080. [PMID: 35709556 PMCID: PMC9207351 DOI: 10.1016/j.nicl.2022.103080] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/26/2022] [Accepted: 06/05/2022] [Indexed: 10/28/2022]
Abstract
PURPOSE Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Zaloška 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, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška 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, Zaloška 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, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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12
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Yoon EJ, Lee JY, Kim H, Yoo D, Shin JH, Nam H, Jeon B, Kim YK. Brain Metabolism Related to Mild Cognitive Impairment and Phenoconversion in Patients With Isolated REM Sleep Behavior Disorder. Neurology 2022; 98:e2413-e2424. [PMID: 35437260 PMCID: PMC9231839 DOI: 10.1212/wnl.0000000000200326] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 02/17/2022] [Indexed: 11/15/2022] Open
Abstract
Background and Objectives Mild cognitive impairment (MCI) in isolated REM sleep behavior disorder (iRBD) is a risk factor for subsequent neurodegeneration. We aimed to identify brain metabolism and functional connectivity changes related to MCI in patients with iRBD and the neuroimaging markers' predictive value for phenoconversion. Methods This is a prospective cohort study of patients with iRBD with a mean follow-up of 4.2 ± 2.6 years. At baseline, patients with iRBD and age- and sex-matched healthy controls (HCs) underwent 18F-fluorodeoxyglucose (FDG)–PET and resting-state fMRI scans and a comprehensive neuropsychological test battery. Voxel-wise group comparisons for FDG-PET data were performed using a general linear model. Seed-based connectivity maps were computed using brain regions showing significant hypometabolism associated with MCI in patients with iRBD and compared between groups. A Cox regression analysis was applied to investigate the association between brain metabolism and risk of phenoconversion. Results Forty patients with iRBD, including 21 with MCI (iRBD-MCI) and 19 with normal cognition (iRBD-NC), and 24 HCs were included in the study. The iRBD-MCI group revealed relative hypometabolism in the inferior parietal lobule, lateral and medial occipital, and middle and inferior temporal cortex bilaterally compared with HC and the iRBD-NC group. In seed-based connectivity analyses, the iRBD-MCI group exhibited decreased functional connectivity of the left angular gyrus with the occipital cortex. Of 40 patients with iRBD, 12 patients converted to Parkinson disease (PD) or dementia with Lewy bodies (DLB). Hypometabolism of the occipital pole (hazard ratio [95% CI] 6.652 [1.387–31.987]), medial occipital (4.450 [1.143–17.327]), and precuneus (3.635 [1.009–13.093]) was associated with higher phenoconversion rate to PD/DLB. Discussion MCI in iRBD is related to functional and metabolic changes in broad brain areas, particularly the occipital and parietal areas. Moreover, hypometabolism in these brain regions was a predictor of phenoconversion to PD or DLB. Evaluation of cognitive function and neuroimaging characteristics could be useful for risk stratification in patients with iRBD.
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Affiliation(s)
- Eun Jin Yoon
- Memory Network Medical Research Center, Seoul National University, Seoul, Korea, Republic of.,Department of Nuclear Medicine, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea, Republic of
| | - Jee-Young Lee
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea, Republic of
| | - Heejung Kim
- Department of Nuclear Medicine, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea, Republic of.,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea, Republic of
| | - Dallah Yoo
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea, Republic of.,Department of Neurology, Kyung Hee University Hospital, Seoul, Korea, Republic of
| | - Jung Hwan Shin
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea, Republic of
| | - Hyunwoo Nam
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea, Republic of
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea, Republic of
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea, Republic of
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13
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Bauckneht M, Lai R, D'Amico F, Miceli A, Donegani MI, Campi C, Schenone D, Raffa S, Chiola S, Lanfranchi F, Rebuzzi SE, Zanardi E, Cremante M, Marini C, Fornarini G, Morbelli S, Piana M, Sambuceti G. Opportunistic skeletal muscle metrics as prognostic tools in metastatic castration-resistant prostate cancer patients candidates to receive Radium-223. Ann Nucl Med 2022; 36:373-383. [PMID: 35044592 PMCID: PMC8938339 DOI: 10.1007/s12149-022-01716-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/07/2022] [Indexed: 12/11/2022]
Abstract
Objective Androgen deprivation therapy alters body composition promoting a significant loss in skeletal muscle (SM) mass through inflammation and oxidative damage. We verified whether SM anthropometric composition and metabolism are associated with unfavourable overall survival (OS) in a retrospective cohort of metastatic castration-resistant prostate cancer (mCRPC) patients submitted to 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) imaging before receiving Radium-223. Patients and methods Low-dose CT were opportunistically analysed using a cross-sectional approach to calculate SM and adipose tissue areas at the third lumbar vertebra level. Moreover, a 3D computational method was used to extract psoas muscles to evaluate their volume, Hounsfield Units (HU) and FDG retention estimated by the standardized uptake value (SUV). Baseline established clinical, lab and imaging prognosticators were also recorded. Results SM area predicted OS at univariate analysis. However, this capability was not additive to the power of mean HU and maximum SUV of psoas muscles volume. These factors were thus combined in the Attenuation Metabolic Index (AMI) whose power was tested in a novel uni- and multivariable model. While Prostate-Specific Antigen (PSA), Alkaline Phosphatase (ALP), Lactate Dehydrogenase and Hemoglobin, Metabolic Tumor Volume, Total Lesion Glycolysis and AMI were associated with long-term OS at the univariate analyses, only PSA, ALP and AMI resulted in independent prognosticator at the multivariate analysis. Conclusion The present data suggest that assessing individual 'patients' SM metrics through an opportunistic operator-independent computational analysis of FDG PET/CT imaging provides prognostic insights in mCRPC patients candidates to receive Radium-223. Graphical abstract ![]()
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Affiliation(s)
- Matteo Bauckneht
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy. .,Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genova, Italy.
| | - Rita Lai
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
| | - Francesca D'Amico
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy
| | - Alberto Miceli
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy
| | | | - Cristina Campi
- LISCOMP, Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
| | - Daniela Schenone
- LISCOMP, Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
| | - Stefano Raffa
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy
| | - Silvia Chiola
- Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | | | - Sara Elena Rebuzzi
- Medical Oncology, Ospedale San Paolo, Savona, Italy.,Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genova, Genoa, Italy
| | - Elisa Zanardi
- Academic Unit of Medical Oncology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Malvina Cremante
- Medical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cecilia Marini
- Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Bioimaging and Physiology (IBFM), CNR Institute of Molecular, Segrate, Milan, Italy
| | - Giuseppe Fornarini
- Medical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy.,Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Michele Piana
- LISCOMP, Department of Mathematics (DIMA), University of Genoa, Genoa, Italy.,CNR-SPIN Genoa, Genoa, Italy
| | - Gianmario Sambuceti
- Department of Health Sciences (DISSAL), University of Genova, Genova, Italy.,Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genova, Italy
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