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Li Y, Xin J, Fang S, Wang F, Jin Y, Wang L. Development and Validation of a Predictive Model for Early Identification of Cognitive Impairment Risk in Community-Based Hypertensive Patients. J Appl Gerontol 2024:7334648241257795. [PMID: 38832577 DOI: 10.1177/07334648241257795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024] Open
Abstract
Objective: To investigate the risk factors for the development of mild cognitive dysfunction in hypertensive patients in the community and to develop a risk prediction model. Method: The data used in this study were obtained from two sources: the China Health and Retirement Longitudinal Study (CHARLS) and the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 1121 participants from CHARLS were randomly allocated into a training set and a validation set, following a 70:30 ratio. Meanwhile, an additional 4016 participants from CLHLS were employed for external validation of the model. The patients in this study were divided into two groups: those with mild cognitive impairment and those without. General information, employment status, pension, health insurance, and presence of depressive symptoms were compared between the two groups. LASSO regression analysis was employed to identify the most predictive variables for the model, utilizing 14-fold cross-validation. The risk prediction model for cognitive impairment in hypertensive populations was developed using generalized linear models. The model's discriminatory power was evaluated through the area under the receiver operating characteristic (ROC) curve and calibration curves. Results: In the modeling group, eight variables such as gender, age, residence, education, alcohol use, depression, employment status, and health insurance were ultimately selected from an initial pool of 21 potential predictors to construct the risk prediction model. The area under the curve (AUC) values for the training, internal, and external validation sets were 0.777, 0.785, and 0.782, respectively. All exceeded the threshold of 0.7, suggesting that the model effectively predicts the incidence of mild cognitive dysfunction in community-based hypertensive patients. A risk prediction model was developed using a generalized linear model in conjunction with Lasso regression. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve. Hosmer-Lemeshow test values yielded p = .346 and p = .626, both of which exceeded the 0.05 threshold. Calibration curves demonstrated a significant agreement between the nomogram model and observed outcomes, serving as an effective tool for evaluating the model's predictive performance. Discussion: The predictive model developed in this study serves as a promising and efficient tool for evaluating cognitive impairment in hypertensive patients, aiding community healthcare workers in identifying at-risk populations.
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Affiliation(s)
- Yan Li
- Shanxi Medical University, Taiyuan, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jimei Xin
- Shanxi Medical University, Taiyuan, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Sen Fang
- Shanxi Medical University, Taiyuan, China
- Department of Geriatrics, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Fang Wang
- Shanxi Medical University, Taiyuan, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yufei Jin
- Shanxi Medical University, Taiyuan, China
- Department of Geriatrics, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Lei Wang
- Shanxi Medical University, Taiyuan, China
- Department of Geriatrics, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
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Gómez-Pascual A, Naccache T, Xu J, Hooshmand K, Wretlind A, Gabrielli M, Lombardo MT, Shi L, Buckley NJ, Tijms BM, Vos SJB, Ten Kate M, Engelborghs S, Sleegers K, Frisoni GB, Wallin A, Lleó A, Popp J, Martinez-Lage P, Streffer J, Barkhof F, Zetterberg H, Visser PJ, Lovestone S, Bertram L, Nevado-Holgado AJ, Gualerzi A, Picciolini S, Proitsi P, Verderio C, Botía JA, Legido-Quigley C. Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease. Comput Biol Med 2024; 176:108588. [PMID: 38761503 DOI: 10.1016/j.compbiomed.2024.108588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 05/09/2024] [Accepted: 05/09/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. METHOD Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. RESULTS Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. CONCLUSIONS This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.
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Affiliation(s)
- Alicia Gómez-Pascual
- Department of Information and Communications Engineering Faculty of Informatics, University of Murcia, Murcia, Spain; Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Talel Naccache
- Department of Data Science, City University of London, United Kingdom
| | - Jin Xu
- Institute of Pharmaceutical Science, King's College London, London, United Kingdom
| | | | | | | | - Marta Tiffany Lombardo
- CNR Institute of Neuroscience, 20854, Vedano al Lambro, Italy; School of Medicine and Surgery, University of Milano-Bicocca, 20126, Italy
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Oxford, United Kingdom
| | - Noel J Buckley
- Department of Psychiatry, University of Oxford, United Kingdom; Kavli Institute for Nanoscience Discovery, Denmark
| | - Betty M Tijms
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Mara Ten Kate
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Department of Neurology and Bru-BRAIN, UZ Brussel and Center for Neurosciences (C4N), Vrije Universiteit Brussel, Brussels, Belgium
| | - Kristel Sleegers
- Complex Genetics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium; Institute Born-Bunge, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Alberto Lleó
- Neurology Department, Hospital Sant Pau, Barcelona, Spain, Centro de Investigación en Red en enfermedades neurodegenerativas (CIBERNED)
| | - Julius Popp
- Old age psychiatry, University Hospital of Lausanne, University of Lausanne, Switzerland; Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, University of Zürich, Switzerland
| | | | - Johannes Streffer
- AC Immune SA, Lausanne, Switzerland, formerly Janssen R&D, LLC. Beerse, Belgium at the time of study conduct
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, United Kingdom
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, United Kingdom; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Pieter Jelle Visser
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, United Kingdom; Janssen Medical (UK), High Wycombe, United Kingdom
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany; Department of Psychology, University of Oslo, Oslo, Norway
| | | | - Alice Gualerzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS in Milan, Italy
| | | | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | | | - Juan A Botía
- Department of Information and Communications Engineering Faculty of Informatics, University of Murcia, Murcia, Spain
| | - Cristina Legido-Quigley
- Steno Diabetes Center Copenhagen, Herlev, Denmark; Institute of Pharmaceutical Science, King's College London, London, United Kingdom.
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Jang H, Na DL, Kwon JC, Jung NY, Moon Y, Lee JS, Park KW, Lee AY, Cho H, Lee JH, Kim BC, Park KH, Lee BC, Choi H, Kim J, Park MY. A Two-Year Observational Study to Evaluate Conversion Rates from High- and Low-Risk Patients with Amnestic Mild Cognitive Impairment to Probable Alzheimer's Disease in a Real-World Setting. J Alzheimers Dis Rep 2024; 8:851-862. [PMID: 38910942 PMCID: PMC11191635 DOI: 10.3233/adr-230189] [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: 12/15/2023] [Accepted: 03/20/2024] [Indexed: 06/25/2024] Open
Abstract
Background Predicting conversion to probable Alzheimer&s disease (AD) from amnestic mild cognitive impairment (aMCI) is difficult but important. A nomogram was developed previously for determining the risk of 3-year probable AD conversion in aMCI. Objective To compare the probable AD conversion rates with cognitive and neurodegenerative changes for 2 years from high- and low risk aMCI groups classified using the nomogram. Methods This prospective, multicenter, observational study was conducted in Korea. A total of patients were classified as high- or low-risk aMCI according to the nomogram and followed-up for 2 years to compare the annual conversion rate to probable AD and brain structure changes between the two groups. Results In total, 176 (high-risk, 85; low-risk, 91) and 160 (high-risk, 77; low-risk, 83) patients completed the 1-year and 2-year follow-up, respectively. The probable AD conversion rate was significantly higher in the high-risk (Year 1, 28.9%; Year 2, 46.1%) versus low-risk group (Year 1, 0.0%; Year 2, 4.9%, both p < 0.0001). Mean changes from baseline in Seoul Neuropsychological Screening Battery-Dementia Version, Clinical Dementia Rating-Sum of Box, and Korean version of the Instrumental Activities of Daily Living scores and cortical atrophy index at Years 1 and 2 were significantly greater in the high-risk group (p < 0.0001). Conclusions The high-risk aMCI group, as determined by the nomogram, had a higher conversion rate to probable AD and faster cognitive decline and neurodegeneration change than the low-risk group. These real-world results have clinical implications that help clinicians in accurately predicting patient outcomes and facilitating early decision-making.Trial Registration: ClinicalTrials.gov (NCT03448445).
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Affiliation(s)
- Hyemin Jang
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jay Cheol Kwon
- Department of Neurology, Changwon Fatima Hospital, Changwon, Republic of Korea
| | - Na-Yeon Jung
- Department of Neurology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Jung Seok Lee
- Department of Neurology, Jeju National University College of Medicine, Jeju, Republic of Korea
| | - Kyung-Won Park
- Department of Neurology, Cognitive Disorders and Dementia Center, Dong-A University College of Medicine and Institute of Convergence Bio-Health, Busan, Republic of Korea
| | - Ae Young Lee
- Department of Neurology, Chungnam National University School of Medicine, Daejeon, Republic of Korea
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Byeong C. Kim
- Department of Neurology, Chonnam National University Medical School & Hospital, Gwangju, Republic of Korea
| | - Kee Hyung Park
- Department of Neurology, College of Medicine, Gachon University Gil Hospital, Incheon, Republic of Korea
| | - Byung-Chul Lee
- Department of Neurology, College of Medicine, Hallym University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Jieun Kim
- Department of Medical, Eisai Korea Inc., Seoul, Republic of Korea
| | - Mee Young Park
- Department of Neurology, Yeungnam University College of Medicine, Daegu, Republic of Korea
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Yu HH, Tan CC, Huang SJ, Zhang XH, Tan L, Xu W. Predicting the reversion from mild cognitive impairment to normal cognition based on magnetic resonance imaging, clinical, and neuropsychological examinations. J Affect Disord 2024; 353:90-98. [PMID: 38452935 DOI: 10.1016/j.jad.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 02/22/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Reversion from mild cognitive impairment (MCI) to normal cognition (NC) is not uncommon and indicates a better cognitive trajectory. This study aims to identify predictors of MCI reversion and develop a predicting model. METHOD A total of 391 MCI subjects (mean age = 74.3 years, female = 61 %) who had baseline data of magnetic resonance imaging, clinical, and neuropsychological measurements were followed for two years. Multivariate logistic analyses were used to identify the predictors of MCI reversion after adjusting for age and sex. A stepwise backward logistic regression model was used to construct a predictive nomogram for MCI reversion. The nomogram was validated by internal bootstrapping and in an independent cohort. RESULT In the training cohort, the 2-year reversion rate was 19.95 %. Predictors associated with reversion to NC were higher education level (p = 0.004), absence of APOE4 allele (p = 0.001), larger brain volume (p < 0.005), better neuropsychological measurements performance (p < 0.001), higher glomerular filtration rate (p = 0.035), and lower mean arterial pressure (p = 0.060). The nomogram incorporating five predictors (education, hippocampus volume, the Alzheimer's Disease Assessment Scale-Cognitive score, the Rey Auditory Verbal Learning Test-immediate score, and mean arterial pressure) achieved good C-indexes of 0.892 (95 % confidence interval [CI], 0.859-0.926) and 0.806 (95 % CI, 0.709-0.902) for the training and validation cohort. LIMITATION Observational duration is relatively short; The predicting model warrant further validation in larger samples. CONCLUSION This prediction model could facilitate risk stratification and early management for the MCI population.
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Affiliation(s)
- Hai-Hong Yu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China; Medical College, Qingdao University, Qingdao, China
| | - Chen-Chen Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Shu-Juan Huang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Xin-Hao Zhang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Xu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
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Bonifazi G, Luchena C, Gaminde-Blasco A, Ortiz-Sanz C, Capetillo-Zarate E, Matute C, Alberdi E, De Pittà M. A nonlinear meccano for Alzheimer's emergence by amyloid β-mediated glutamatergic hyperactivity. Neurobiol Dis 2024; 194:106473. [PMID: 38493903 DOI: 10.1016/j.nbd.2024.106473] [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: 10/26/2023] [Revised: 03/10/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024] Open
Abstract
The pathophysiological process of Alzheimer's disease (AD) is believed to begin many years before the formal diagnosis of AD dementia. This protracted preclinical phase offers a crucial window for potential therapeutic interventions, yet its comprehensive characterization remains elusive. Accumulating evidence suggests that amyloid-β (Aβ) may mediate neuronal hyperactivity in circuit dysfunction in the early stages of AD. At the same time, neural activity can also facilitate Aβ accumulation through intricate feed-forward interactions, complicating elucidating the conditions governing Aβ-dependent hyperactivity and its diagnostic utility. In this study, we use biophysical modeling to shed light on such conditions. Our analysis reveals that the inherently nonlinear nature of the underlying molecular interactions can give rise to the emergence of various modes of hyperactivity. This diversity in the mechanisms of hyperactivity may ultimately account for a spectrum of AD manifestations.
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Affiliation(s)
- Giulio Bonifazi
- Basque Center for Applied Mathematics, Alameda Mazarredo 14, Bilbao 48009, Bizkaia, Spain; Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Krembil Research Institute, University Health Network, 60 Leonard Ave, Toronto M5T 0S8, ON, Canada
| | - Celia Luchena
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Adhara Gaminde-Blasco
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Carolina Ortiz-Sanz
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Estibaliz Capetillo-Zarate
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Carlos Matute
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Elena Alberdi
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Maurizio De Pittà
- Basque Center for Applied Mathematics, Alameda Mazarredo 14, Bilbao 48009, Bizkaia, Spain; Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Krembil Research Institute, University Health Network, 60 Leonard Ave, Toronto M5T 0S8, ON, Canada; Department of Physiology, University of Toronto, 1 King's College Circle, Toronto M5S 1A8, ON, Canada.
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Rehman H, Ang TFA, Tao Q, Espenilla AL, Au R, Farrer LA, Zhang X, Qiu WQ. Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status. J Alzheimers Dis 2024; 97:621-633. [PMID: 38143358 DOI: 10.3233/jad-230837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
BACKGROUND Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer's disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI). OBJECTIVE This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status. METHODS This cohort study included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification. RESULTS We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype. CONCLUSIONS This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.
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Affiliation(s)
- Habbiburr Rehman
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ting Fang Alvin Ang
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Qiushan Tao
- Department of Pharmacology & Experimental Therapeutics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Arielle Lauren Espenilla
- Department of Biostatistics and Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Rhoda Au
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Biostatistics and Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Biostatistics and Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
| | - Wei Qiao Qiu
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Pharmacology & Experimental Therapeutics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
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7
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Guo H, Sun S, Yang Y, Ma R, Wang C, Zheng S, Wang X, Li G. A Novel Score to Predict Individual Risk for Future Alzheimer's Disease: A Longitudinal Study of the ADNI Cohort. J Alzheimers Dis 2024; 101:923-936. [PMID: 39240641 DOI: 10.3233/jad-240532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
Background Identifying high-risk individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer's disease (AD) is crucial for early intervention. Objective This study aimed to develop and validate a novel clinical score for personalized estimation of MCI-to-AD conversion. Methods The data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were analyzed. Two-thirds of the MCI patients were randomly assigned to a training cohort (n = 478), and the remaining one-third formed the validation cohort (n = 239). Multivariable logistic regression was performed to identify factors associated with MCI-to-AD progression within 4 years. A prediction score was developed based on the regression coefficients derived from the logistic model and tested in the validation cohort. Results A lipidomics-signature was obtained that showed a significant association with disease progression. The MCI conversion scoring system (ranged from 0 to 14 points), consisting of the lipidomics-signature and five other significant variables (Apolipoprotein ɛ4, Rey Auditory Verbal Learning Test immediate and delayed recall, Alzheimer's Disease Assessment Scale delayed recall test, Functional Activities Questionnaire, and cortical thickness of the AD signature), was constructed. Higher conversion scores were associated with a higher proportion of patients converting to AD. The scoring system demonstrated good discrimination and calibration in both the training cohort (AUC = 0.879, p of Hosmer-Lemeshow test = 0.597) and the validation cohort (AUC = 0.915, p of Hosmer-Lemeshow test = 0.991). The risk classification achieved excellent sensitivity (0.84) and specificity (0.75). Conclusions The MCI-to-AD conversion score is a reliable tool for predicting the risk of disease progression in individuals with MCI.
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Affiliation(s)
- Hongxiu Guo
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shangqi Sun
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Yang
- Department of General Medicine, Binzhou Medical University Hospital, Binzhou, China
| | - Rong Ma
- Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cailin Wang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Siyi Zheng
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiufeng Wang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gang Li
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Chen Z, Chen K, Li Y, Geng D, Li X, Liang X, Lu H, Ding S, Xiao Z, Ma X, Zheng L, Ding D, Zhao Q, Yang L. Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI. Hum Brain Mapp 2024; 45:e26529. [PMID: 37991144 PMCID: PMC10789213 DOI: 10.1002/hbm.26529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023] Open
Abstract
Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.
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Affiliation(s)
- Zhihan Chen
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
| | - Keliang Chen
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuxin Li
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Daoying Geng
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Xiantao Li
- Department of Critical Care MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Xiaoniu Liang
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Huimeng Lu
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Saineng Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Zhenxu Xiao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaoxi Ma
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Ding Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qianhua Zhao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Liqin Yang
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
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Guthrie DM, Williams N, O'Rourke HM, Orange JB, Phillips N, Pichora-Fuller MK, Savundranayagam MY, Sutradhar R. Development and validation of risk of CPS decline (RCD): a new prediction tool for worsening cognitive performance among home care clients in Canada. BMC Geriatr 2023; 23:792. [PMID: 38041046 PMCID: PMC10693097 DOI: 10.1186/s12877-023-04463-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/06/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND To develop and validate a prediction tool, or nomogram, for the risk of a decline in cognitive performance based on the interRAI Cognitive Performance Scale (CPS). METHODS Retrospective, population-based, cohort study using Canadian Resident Assessment Instrument for Home Care (RAI-HC) data, collected between 2010 and 2018. Eligible home care clients, aged 18+, with at least two assessments were selected randomly for model derivation (75%) and validation (25%). All clients had a CPS score of zero (intact) or one (borderline intact) on intake into the home care program, out of a possible score of six. All individuals had to remain as home care recipients for the six months observation window in order to be included in the analysis. The primary outcome was any degree of worsening (i.e., increase) on the CPS score within six months. Using the derivation cohort, we developed a multivariable logistic regression model to predict the risk of a deterioration in the CPS score. Model performance was assessed on the validation cohort using discrimination and calibration plots. RESULTS We identified 39,292 eligible home care clients, with a median age of 79.0 years, 62.3% were female, 38.8% were married and 38.6% lived alone. On average, 30.3% experienced a worsening on the CPS score within the six-month window (i.e., a change from 0 or 1 to 2, 3, 4, 5, or 6). The final model had good discrimination (c-statistic of 0.65), with excellent calibration. CONCLUSIONS The model accurately predicted the risk of deterioration on the CPS score over six months among home care clients. This type of predictive model may provide useful information to support decisions for home care clinicians who use interRAI data internationally.
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Affiliation(s)
- Dawn M Guthrie
- Department of Kinesiology & Physical Education, Wilfrid Laurier University, Waterloo, ON, Canada
- Department of Health Sciences, Wilfrid Laurier University, Waterloo, ON, Canada
| | - Nicole Williams
- Department of Kinesiology & Physical Education, Wilfrid Laurier University, Waterloo, ON, Canada
| | - Hannah M O'Rourke
- College of Health Sciences, Faculty of Nursing, University of Alberta, Edmonton, AB, Canada
| | - Joseph B Orange
- School of Communication Sciences and Disorders, Western University, London, ON, Canada
| | - Natalie Phillips
- Department of Psychology, Centre for Research in Human Development, Concordia University, Montreal, QC, Canada
| | | | | | - Rinku Sutradhar
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
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Zhang T, Cong L, Chen B, He Z, Li G, Qin Q. Editorial: Multimodal interventions in Alzheimer's disease: from basic research to clinical practice. Front Neurol 2023; 14:1303733. [PMID: 37928148 PMCID: PMC10623440 DOI: 10.3389/fneur.2023.1303733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023] Open
Affiliation(s)
- Tongtong Zhang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Lin Cong
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ben Chen
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhuohao He
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences (CAS), Shanghai, China
| | - Guozhong Li
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Qi Qin
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
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11
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Choi US, Park JY, Lee JJ, Choi KY, Won S, Lee KH. Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging. Cereb Cortex 2023; 33:10858-10866. [PMID: 37718166 DOI: 10.1093/cercor/bhad331] [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: 05/27/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.
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Affiliation(s)
- Uk-Su Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu 41061, Republic of Korea
| | - Jun Young Park
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Neurozen Inc., Seoul 06168, Republic of Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul 08826, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Biomedical Sciences, Chosun University, Gwangju 61452, Republic of Korea
- Korea Brain Research Institute, Daegu 41061, Republic of Korea
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12
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Tong Y, Chen J, Sun J, Luo T, Duan S, Li K, Zhou K, Zeng J, Lu F. A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma. Front Oncol 2023; 13:1162238. [PMID: 37901318 PMCID: PMC10602760 DOI: 10.3389/fonc.2023.1162238] [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: 02/09/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Purpose To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent. Materials and methods The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). Results We selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram. Conclusion We successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.
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Affiliation(s)
- Yahan Tong
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Junyi Chen
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
| | - Jingjing Sun
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Taobo Luo
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Kai Li
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Kefeng Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jian Zeng
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Fangxiao Lu
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
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Huang J, Wang Y, Stein TD, Ang TFA, Zhu Y, Tao Q, Lunetta KL, Mez J, Au R, Farrer LA, Qiu WQ, Zhang X. The impact of blood MCP-1 levels on Alzheimer's disease with genetic variation of UNC5C and NAV3 loci. RESEARCH SQUARE 2023:rs.3.rs-3376348. [PMID: 37841863 PMCID: PMC10571626 DOI: 10.21203/rs.3.rs-3376348/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Background Previous study shows that monocyte chemoattractant protein-1 (MCP-1), which is implicated in the peripheral proinflammatory cascade and blood-brain barrier (BBB) disruption, modulates the genetic risks of AD in established AD loci. Methods In this study, we hypothesized that blood MCP-1 impacts the AD risk of genetic variants beyond known AD loci. We thus performed a genome-wide association study (GWAS) using the logistic regression via generalized estimating equations (GEE) and the Cox proportional-hazards models to examine the interactive effects between single nucleotide polymorphisms (SNPs) and blood MCP-1 level on AD in three cohorts: the Framingham Heart Study (FHS), Alzheimer's Disease Neuroimaging Initiative (ADNI) and Religious Orders Study/Memory and Aging Project (ROSMAP). Results We identified SNPs in two genes, neuron navigator 3 (NAV3, also named Unc-53 Homolog 3, rs696468) (p < 7.55×10- 9) and Unc-5 Netrin Receptor C (UNC5C rs72659964) (p < 1.07×10- 8) that showed an association between increasing levels of blood MCP-1 and AD. Elevating blood MCP-1 concentrations increased AD risk and AD pathology in genotypes of NAV3 (rs696468-CC) and UNC5C (rs72659964-AT + TT), but did not influence the other counterpart genotypes of these variants. Conclusions NAV3 and UNC5C are homologs and may increase AD risk through dysregulating the functions of neurite outgrowth and guidance. Overall, the association of risk alleles of NAV3 and UNC5C with AD is enhanced by peripheral MCP-1 level, suggesting that lowering the level of blood MCP-1 may reduce the risk of developing AD for people with these genotypes.
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Affiliation(s)
- Jinghan Huang
- Boston University Chobanian & Avedisian School of Medicine
| | - Yixuan Wang
- Boston University Chobanian & Avedisian School of Medicine
| | - Thor D Stein
- Boston University Chobanian & Avedisian School of Medicine
| | | | - Yibo Zhu
- Boston University Chobanian & Avedisian School of Medicine
| | - Qiushan Tao
- Boston University Chobanian & Avedisian School of Medicine
| | | | - Jesse Mez
- Boston University Chobanian & Avedisian School of Medicine
| | - Rhoda Au
- Boston University Chobanian & Avedisian School of Medicine
| | | | - Wei Qiao Qiu
- Boston University Chobanian & Avedisian School of Medicine
| | - Xiaoling Zhang
- Boston University Chobanian & Avedisian School of Medicine
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Wu Y, Wang X, Gu C, Zhu J, Fang Y. Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals. Age Ageing 2023; 52:afad182. [PMID: 37740920 PMCID: PMC10518045 DOI: 10.1093/ageing/afad182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Indexed: 09/25/2023] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations. METHODS Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance. RESULTS The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors. CONCLUSION Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.
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Affiliation(s)
- Yafei Wu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Xing Wang
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Chenming Gu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Junmin Zhu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Ya Fang
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China
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15
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Felsky D, Cannitelli A, Pipitone J. Whole Person Modeling: a transdisciplinary approach to mental health research. DISCOVER MENTAL HEALTH 2023; 3:16. [PMID: 37638348 PMCID: PMC10449734 DOI: 10.1007/s44192-023-00041-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.
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Affiliation(s)
- Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Alyssa Cannitelli
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Jon Pipitone
- Department of Psychiatry, Queen’s University, Kingston, ON Canada
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Xing D, Chen L, Zhang W, Yi Q, Huang H, Wu J, Yu W, Lü Y. Prediction of 3-Year Survival in Patients with Cognitive Impairment Based on Demographics, Neuropsychological Data, and Comorbidities: A Prospective Cohort Study. Brain Sci 2023; 13:1220. [PMID: 37626576 PMCID: PMC10452564 DOI: 10.3390/brainsci13081220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/15/2023] [Accepted: 08/18/2023] [Indexed: 08/27/2023] Open
Abstract
OBJECTIVES Based on readily available demographic data, neuropsychological assessment results, and comorbidity data, we aimed to develop and validate a 3-year survival prediction model for patients with cognitive impairment. METHODS In this prospective cohort study, 616 patients with cognitive impairment were included. Demographic information, data on comorbidities, and scores of the Mini-Mental State Examination (MMSE), Instrumental Activities of Daily Living (IADL) scale, and Neuropsychiatric Inventory Questionnaire were collected. Survival status was determined via telephone interviews and further verified in the official death register in the third year. A 7:3 ratio was used to divide patients into the training and validation sets. Variables with statistical significance (p < 0.05) in the single-factor analysis were incorporated into the binary logistic regression model. A nomogram was constructed according to multivariate analysis and validated. RESULTS The final cohort included 587 patients, of whom 525 (89.44%) survived and 62 (10.56%) died. Younger age, higher MMSE score, lower IADL score, absence of disinhibition, and Charlson comorbidity index score ≤ 1 were all associated with 3-year survival. These predictors yielded good discrimination with C-indices of 0.80 (0.73-0.87) and 0.85 (0.77-0.94) in the training and validation cohorts, respectively. According to the Hosmer-Lemeshow test results, neither cohort displayed any statistical significance, and calibration curves displayed a good match between predictions and results. CONCLUSIONS Our study provided further insight into the factors contributing to the survival of patients with cognitive impairment. CLINICAL IMPLICATIONS Our model showed good accuracy and discrimination ability, and it can be used at community hospitals or primary care facilities that lack sophisticated equipment.
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Affiliation(s)
- Dianxia Xing
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
- Department of Geriatrics, Chongqing University Three Gorges Hospital, Chongqing 404100, China
| | - Lihua Chen
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
| | - Wenbo Zhang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
| | - Qingjie Yi
- Department of Quality Control, Chongqing University Three Gorges Hospital, Chongqing 404100, China
| | - Hong Huang
- Department of Geriatrics, Chongqing University Three Gorges Hospital, Chongqing 404100, China
| | - Jiani Wu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
| | - Weihua Yu
- Institute of Neuroscience, Chongqing Medical University, Chongqing 400016, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
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Ma H, Zhang D, Wang Y, Ding Y, Yang J, Li K. Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI. BMC Psychiatry 2023; 23:466. [PMID: 37365541 DOI: 10.1186/s12888-023-04966-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/16/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Due to individual differences and lack of objective biomarkers, only 30-40% patients with major depressive disorder (MDD) achieve remission after initial antidepressant medication (ADM). We aimed to employ radiomics analysis after ComBat harmonization to predict early improvement to ADM in adolescents with MDD by using brain multiscale structural MRI (sMRI) and identify the radiomics features with high prediction power for selection of selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). METHODS 121 MDD patients were recruited for brain sMRI, including three-dimensional T1 weighted imaging (3D-T1WI)and diffusion tensor imaging (DTI). After receiving SSRIs or SNRIs for 2 weeks, the subjects were divided into ADM improvers (SSRIs improvers and SNRIs improvers) and non-improvers according to reduction rate of the Hamilton Depression Rating Scale, 17 item (HAM-D17) score. Then, sMRI data were preprocessed, and conventional imaging indicators and radiomics features of gray matter (GM) based on surface-based morphology (SBM) and voxel-based morphology (VBM) and diffusion properties of white matter (WM) were extracted and harmonized with ComBat harmonization. Two-level reduction strategy with analysis of variance (ANOVA) and recursive feature elimination (RFE) was utilized sequentially to decrease high-dimensional features. Support vector machine with radial basis function kernel (RBF-SVM) was used to integrate multiscale sMRI features to construct models for early improvement prediction. Area under the curve (AUC), accuracy, sensitivity, and specificity based on the leave-one-out cross-validation (LOO-CV) and receiver operating characteristic (ROC) curve analysis were calculated to evaluate the model performance. Permutation tests were used for assessing the generalization rate. RESULTS After 2-week ADM, 121 patients were divided into 67 ADM improvers (31 SSRIs improvers and 36 SNRIs improvers) and 54 ADM non-improvers. After two-level dimensionality reduction, 8 conventional indicators (2 VBM-based features and 6 diffusion features) and 49 radiomics features (16 VBM-based features and 33 diffusion features) were selected. The overall accuracy of RBF-SVM models based on conventional indicators and radiomics features was 74.80% and 88.19%. The radiomics model achieved the AUC, sensitivity, specificity, and accuracy of 0.889, 91.2%, 80.1% and 85.1%, 0.954, 89.2%, 87.4% and 88.5%, 0.942, 91.9%, 82.5% and 86.8% for predicting ADM improvers, SSRIs improvers and SNRIs improvers, respectively. P value of permutation tests were less than 0.001. The radiomics features predicting ADM improver were mainly located in the hippocampus, medial orbitofrontal gyrus, anterior cingulate gyrus, cerebellum (lobule vii-b), body of corpus callosum, etc. The radiomics features predicting SSRIs improver were primarily distributed in hippocampus, amygdala, inferior temporal gyrus, thalamus, cerebellum (lobule vi), fornix, cerebellar peduncle, etc. The radiomics features predicting SNRIs improver were primarily located in the medial orbitofrontal cortex, anterior cingulate gyrus, ventral striatum, corpus callosum, etc. CONCLUSIONS: These findings suggest the radiomics analysis based on brain multiscale sMRI after ComBat harmonization could effectively predict the early improvement of ADM in adolescent MDD patients with a high accuracy, which was superior to the model based on the conventional indicators. The radiomics features with high prediction power may help for the individual selection of SSRIs and SNRIs.
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Affiliation(s)
- Huan Ma
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China
| | - Dafu Zhang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Yao Wang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Yingying Ding
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Jianzhong Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China
| | - Kun Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China.
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Victorino DB, Faber J, Pinheiro DJLL, Scorza FA, Almeida ACG, Costa ACS, Scorza CA. Toward the Identification of Neurophysiological Biomarkers for Alzheimer's Disease in Down Syndrome: A Potential Role for Cross-Frequency Phase-Amplitude Coupling Analysis. Aging Dis 2023; 14:428-449. [PMID: 37008053 PMCID: PMC10017148 DOI: 10.14336/ad.2022.0906] [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: 06/17/2022] [Accepted: 09/06/2022] [Indexed: 11/18/2022] Open
Abstract
Cross-frequency coupling (CFC) mechanisms play a central role in brain activity. Pathophysiological mechanisms leading to many brain disorders, such as Alzheimer's disease (AD), may produce unique patterns of brain activity detectable by electroencephalography (EEG). Identifying biomarkers for AD diagnosis is also an ambition among research teams working in Down syndrome (DS), given the increased susceptibility of people with DS to develop early-onset AD (DS-AD). Here, we review accumulating evidence that altered theta-gamma phase-amplitude coupling (PAC) may be one of the earliest EEG signatures of AD, and therefore may serve as an adjuvant tool for detecting cognitive decline in DS-AD. We suggest that this field of research could potentially provide clues to the biophysical mechanisms underlying cognitive dysfunction in DS-AD and generate opportunities for identifying EEG-based biomarkers with diagnostic and prognostic utility in DS-AD.
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Affiliation(s)
- Daniella B Victorino
- Discipline of Neuroscience, Department of Neurology and Neurosurgery, Federal University of São Paulo / Paulista Medical School, São Paulo, SP, Brazil.
| | - Jean Faber
- Discipline of Neuroscience, Department of Neurology and Neurosurgery, Federal University of São Paulo / Paulista Medical School, São Paulo, SP, Brazil.
| | - Daniel J. L. L Pinheiro
- Discipline of Neuroscience, Department of Neurology and Neurosurgery, Federal University of São Paulo / Paulista Medical School, São Paulo, SP, Brazil.
| | - Fulvio A Scorza
- Discipline of Neuroscience, Department of Neurology and Neurosurgery, Federal University of São Paulo / Paulista Medical School, São Paulo, SP, Brazil.
| | - Antônio C. G Almeida
- Department of Biosystems Engineering, Federal University of São João Del Rei, Minas Gerais, MG, Brazil.
| | - Alberto C. S Costa
- Division of Psychiatry, Case Western Reserve University, Cleveland, OH, United States.
- Department of Macromolecular Science and Engineering, Case Western Reserve University, Cleveland, OH, United States.
| | - Carla A Scorza
- Discipline of Neuroscience, Department of Neurology and Neurosurgery, Federal University of São Paulo / Paulista Medical School, São Paulo, SP, Brazil.
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Based on Tau PET Radiomics Analysis for the Classification of Alzheimer's Disease and Mild Cognitive Impairment. Brain Sci 2023; 13:brainsci13020367. [PMID: 36831910 PMCID: PMC9953966 DOI: 10.3390/brainsci13020367] [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: 12/28/2022] [Revised: 02/06/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) are closely associated with Tau proteins accumulation. In this study, we aimed to implement radiomics analysis to discover high-order features from pathological biomarker and improve the classification accuracy based on Tau PET images. Two cross-racial independent cohorts from the ADNI database (121 AD patients, 197 MCI patients and 211 normal control (NC) subjects) and Huashan hospital (44 AD patients, 33 MCI patients and 36 NC subjects) were enrolled. The radiomics features of Tau PET imaging of AD related brain regions were computed for classification using a support vector machine (SVM) model. The radiomics model was trained and validated in the ADNI cohort and tested in the Huashan hospital cohort. The standard uptake value ratio (SUVR) and clinical scores model were also performed to compared with radiomics analysis. Additionally, we explored the possibility of using Tau PET radiomics features as a good biomarker to make binary identification of Tau-negative MCI versus Tau-positive MCI or apolipoprotein E (ApoE) ε4 carrier versus ApoE ε4 non-carrier. We found that the radiomics model demonstrated best classification performance in differentiating AD/MCI patients and NC in comparison to SUVR and clinical scores models, with an accuracy of 84.8 ± 4.5%, 73.1 ± 3.6% in the ANDI cohort. Moreover, the radiomics model also demonstrated greater performance in diagnosing AD than other methods in the Huashan hospital cohort, with an accuracy of 81.9 ± 6.1%. In addition, the radiomics model also showed the satisfactory classification performance in the MCI-tau subgroup experiment (72.3 ± 3.5%, 71.9 ± 3.6% and 63.7 ± 5.9%) and in the MCI-ApoE subgroup experiment (73.5 ± 4.3%, 70.1 ± 3.9% and 62.5 ± 5.4%). In conclusion, our study showed that based on Tau PET radiomics analysis has the potential to guide and facilitate clinical diagnosis, further providing evidence for identifying the risk factors in MCI patients.
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20
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Xiao Q, Zhang S, Li C, Zhu Y. Risk Factors for Delirium Superimposed on Dementia in Elderly Patients in Comprehensive Ward. Am J Alzheimers Dis Other Demen 2023; 38:15333175231206023. [PMID: 37812498 PMCID: PMC10624083 DOI: 10.1177/15333175231206023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
OBJECTIVE To investigate the incidence of delirium and its related risk factors in patients with senile dementia during hospitalization. METHODS A retrospective analysis of clinical data of 157 patients over 65 with cognitive impairment who were hospitalized in the comprehensive ward from October 2019 to February 2023 was conducted. Patients were assigned into delirium and non-delirium groups according to whether they exhibited delirium during hospitalization. General information about the patients and Visual Analogue Scale (VAS) score, blood C-reactive protein level, and blood superoxide dismutase (SOD) level were recorded. Univariate analysis was used to identify potential risk factors for delirium, and factors with statistical significance were subjected to multivariate logistic regression analysis. A prediction line chart for delirium in elderly dementia patients was constructed using R 4.03 software, and the model was validated. RESULTS Among the 157 patients with senile dementia, 42 patients exhibited delirium and 115 patients exhibited non-delirium. Multivariate logistic regression analysis showed that diabetes, cerebrovascular disease, VAS score ≥4 points, use of sedative drugs, and blood SOD <129 U/mL were independent risk factors for delirium during hospitalization in elderly dementia patients. A prediction nomogram was plotted based on the five risk factors, and receiver operating characteristic curve analysis presented an area under the curve of .875 (95% CI: .816-.934). The nomogram model was internally validated by the Bootstrap method, and the calibration curve showed good agreement between predicted and actual results. Hosmer-Lemeshow test demonstrated that the model had a good fit and high predictive ability. CONCLUSION Diabetes, cerebrovascular disease, VAS ≥4 points, use of sedative drugs, and blood SOD <129 U/mL were independent risk factors for delirium in patients with senile dementia during hospitalization. The nomogram model had good accuracy and clinical application value for predicting delirium in this study.
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Affiliation(s)
- Qifan Xiao
- International Department of China-Japan Friendship Hospital, Beijing, China
| | - Suqiao Zhang
- International Department of China-Japan Friendship Hospital, Beijing, China
| | - Chenlu Li
- Beijing University of Chinese Medicine, Beijing, China
| | - Yuqing Zhu
- International Department of China-Japan Friendship Hospital, Beijing, China
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21
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Yao K, Wang J, Ma B, He L, Zhao T, Zou X, Weng Z, Yao R. A nomogram for predicting risk of death during hospitalization in elderly patients with Alzheimer's disease at the time of admission. Front Neurol 2023; 14:1093154. [PMID: 36873432 PMCID: PMC9978216 DOI: 10.3389/fneur.2023.1093154] [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: 11/08/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023] Open
Abstract
Background and objectives Elderly patients with Alzheimer's disease (AD) often have multiple underlying disorders that lead to frequent hospital admissions and are associated with adverse outcomes such as in-hospital mortality. The aim of our study was to develop a nomogram to be used at hospital admission for predicting the risk of death in patients with AD during hospitalization. Methods We established a prediction model based on a dataset of 328 patients hospitalized with AD -who were admitted and discharged from January 2015 to December 2020. A multivariate logistic regression analysis method combined with a minimum absolute contraction and selection operator regression model was used to establish the prediction model. The identification, calibration, and clinical usefulness of the predictive model were evaluated using the C-index, calibration diagram, and decision curve analysis. Internal validation was evaluated using bootstrapping. Results The independent risk factors included in our nomogram were diabetes, coronary heart disease (CHD), heart failure, hypotension, chronic obstructive pulmonary disease (COPD), cerebral infarction, chronic kidney disease (CKD), anemia, activities of daily living (ADL) and systolic blood pressure (SBP). The C-index and AUC of the model were both 0.954 (95% CI: 0.929-0.978), suggesting that the model had accurate discrimination ability and calibration. Internal validation achieved a good C-index of 0.940. Conclusion The nomogram including the comorbidities (i.e., diabetes, CHD, heart failure, hypotension, COPD, cerebral infarction, anemia and CKD), ADL and SBP can be conveniently used to facilitate individualized identification of risk of death during hospitalization in patients with AD.
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Affiliation(s)
- Kecheng Yao
- Department of Geriatrics, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Junpeng Wang
- Department of Geriatrics, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Baohua Ma
- Department of Medical Record, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Ling He
- Department of General Practice, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Tianming Zhao
- Department of Respiratory and Critical Care Medicine, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Xiulan Zou
- Department of Geriatrics, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Zean Weng
- Department of Neurology, The First College of Clinical Medical Sciences, Three Gorges University, Yichang, Hubei, China
| | - Rucheng Yao
- Department of Hepatopancreatobilary Surgery, The First College of Clinical Medical Sciences, Three Gorges University, Yichang, Hubei, China
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Huang J, Tao Q, Ang TFA, Farrell J, Zhu C, Wang Y, Stein TD, Lunetta KL, Massaro J, Mez J, Au R, Farrer LA, Qiu WQ, Zhang X. The impact of increasing levels of blood C-reactive protein on the inflammatory loci SPI1 and CD33 in Alzheimer's disease. Transl Psychiatry 2022; 12:523. [PMID: 36550123 PMCID: PMC9780312 DOI: 10.1038/s41398-022-02281-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/19/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
Apolipoprotein ε4 (APOE ε4) is the most significant genetic risk factor for late-onset Alzheimer's disease (AD). Elevated blood C-reactive protein (CRP) further increases the risk of AD for people carrying the APOE ε4 allele. We hypothesized that CRP, as a key inflammatory element, could modulate the impact of other genetic variants on AD risk. We selected ten single nucleotide polymorphisms (SNPs) in reported AD risk loci encoding proteins related to inflammation. We then tested the interaction effects between these SNPs and blood CRP levels on AD incidence using the Cox proportional hazards model in UK Biobank (n = 279,176 white participants with 803 incident AD cases). The five top SNPs were tested for their interaction with different CRP cutoffs for AD incidence in the Framingham Heart Study (FHS) Generation 2 cohort (n = 3009, incident AD = 156). We found that for higher concentrations of serum CRP, the AD risk increased for SNP genotypes in 3 AD-associated genes (SPI1, CD33, and CLU). Using the Cox model in stratified genotype analysis, the hazard ratios (HRs) for the association between a higher CRP level (≥10 vs. <10 mg/L) and the risk of incident AD were 1.94 (95% CI: 1.33-2.84, p < 0.001) for the SPI1 rs1057233-AA genotype, 1.75 (95% CI: 1.20-2.55, p = 0.004) for the CD33 rs3865444-CC genotype, and 1.76 (95% CI: 1.25-2.48, p = 0.001) for the CLU rs9331896-C genotype. In contrast, these associations were not observed in the other genotypes of these genes. Finally, two SNPs were validated in 321 Alzheimer's Disease Neuroimaging (ADNI) Mild Cognitive Impairment (MCI) patients. We observed that the SPI1 and CD33 genotype effects were enhanced by elevated CRP levels for the risk of MCI to AD conversion. Furthermore, the SPI1 genotype was associated with CSF AD biomarkers, including t-Tau and p-Tau, in the ADNI cohort when the blood CRP level was increased (p < 0.01). Our findings suggest that elevated blood CRP, as a peripheral inflammatory biomarker, is an important moderator of the genetic effects of SPI1 and CD33 in addition to APOE ε4 on AD risk. Monitoring peripheral CRP levels may be helpful for precise intervention and prevention of AD for these genotype carriers.
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Affiliation(s)
- Jinghan Huang
- Departments of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Qiushan Tao
- Departments of Pharmacology & Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Ting Fang Alvin Ang
- Departments of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - John Farrell
- Departments of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Congcong Zhu
- Departments of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Yixuan Wang
- Departments of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- VA Bedford Healthcare System, Bedford, MA, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
| | - Joseph Massaro
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
| | - Jesse Mez
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
- Departments of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Rhoda Au
- Departments of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
- Departments of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Lindsay A Farrer
- Departments of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Boston University School of Medicine, Framingham, MA, USA
- Departments of Neurology, Boston University School of Medicine, Boston, MA, USA
- Departments of Ophthalmology, Boston University School of Medicine, Boston, MA, USA
| | - Wei Qiao Qiu
- Departments of Pharmacology & Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA.
- Alzheimer's Disease Research Center, Boston University School of Medicine, Boston, MA, USA.
- Departments of Psychiatry, Boston University School of Medicine, Boston, MA, USA.
| | - Xiaoling Zhang
- Departments of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
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Velazquez M, Lee Y. Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects. Comput Biol Med 2022; 151:106201. [PMID: 36370583 DOI: 10.1016/j.compbiomed.2022.106201] [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: 06/04/2022] [Revised: 09/17/2022] [Accepted: 10/09/2022] [Indexed: 12/27/2022]
Abstract
Alzheimer's Disease (AD) is the most common type of dementia. Predicting the conversion to Alzheimer's from the mild cognitive impairment (MCI) stage is a complex problem that has been studied extensively. This study centers on individualized EMCI (the earliest MCI subset) to AD conversion prediction on multimodal data such as diffusion tensor imaging (DTI) scans and electronic health records (EHR) for their patients using the combination of both a balanced random forest model alongside a convolutional neural network (CNN) model. Our random forest model leverages EHR's patient biometric and neuropsychiatric test score features, while our CNN model uses the patient's diffusion tensor imaging (DTI) scans for conversion prediction. To accomplish this, 383 Early Mild Cognitive Impairment (EMCI) patients were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Within this set, 49 patients would eventually convert to AD (EMCI_C), whereas the remaining 335 did not convert (EMCI_NC). For the EHR-based classifier, 288 patients were used to train the random forest model, with 95 set aside for testing. For the CNN classifier, 405 DTI images were collected across 90 distinct patients. Nine clinical features were selected to be combined with the visual predictor. Due to the imbalanced classes, oversampling was performed for the clinical features and augmentation for the DTI images. A grid search algorithm is also used to determine the ideal weighting between our two models. Our results indicate that an ensemble model was effective (98.81% accuracy) at EMCI to AD conversion prediction. Additionally, our ensemble model provides explainability as feature importance can be assessed at both the model and individual prediction levels. Therefore, this ensemble model could serve as a diagnostic support tool or a means for identifying clinical trial candidates.
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Affiliation(s)
- Matthew Velazquez
- Computer Science, School of Science Engineering, University of Missouri, Kansas City, MO, USA.
| | - Yugyung Lee
- Computer Science, School of Science Engineering, University of Missouri, Kansas City, MO, USA.
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A Deep Longitudinal Model for Mild Cognitive Impairment to Alzheimer’s Disease Conversion Prediction in Low-Income Countries. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/1419310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive and fatal disease, due to the nonavailability of any permanent cure. Some treatments are under experimentation that can slow down and possibly pause the progression of the disease only if the disease is diagnosed earlier. The onset of AD can only be detected at the mild cognitive impairment (MCI) stage in which slight memory loss is observed but daily routine functions are intact. A small fraction of the patient progresses from MCI to AD. In this research, we have designed a cascaded deep neural network model to identify those MCI subjects who will progress to AD in the following year. The analysis and experimentation have been performed using twenty longitudinal neuropsychological measures (NMs) provided by Alzheimer’s Disease Neuroimaging Initiative (ADNI). After normalization and ranking of longitudinal data, the deep neural network regression model is trained and tuned to forecast the next in-sequence biomarker value using two previous follow-up readings for each marker. Then, the three time-domain window samples are fed into another deep neural network classifier model for the classification of MCI progressor (MCIp) and MCI stables (MCIs). Our model presented regression forecasting MAE of 0.13 and classification accuracy of 86.9% with AUC of 92.1% (Sensitivity: 67.7%, specificity: 92.3%) over 5-fold cross-validation. We conclude that time-domain measures of NM alone can deliver comparable MCI to AD conversion prediction performance without leveraging more expensive and invasive counterparts such as MR imaging, PET scans, and CSF measures. Middle and low-income countries will benefit from such cheap and effective solutions greatly.
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25
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Ma S, Huang H, Zhong Z, Zheng H, Li M, Yao L, Yu B, Wang H. Effect of acupuncture on brain regions modulation of mild cognitive impairment: A meta-analysis of functional magnetic resonance imaging studies. Front Aging Neurosci 2022; 14:914049. [PMID: 36212046 PMCID: PMC9540390 DOI: 10.3389/fnagi.2022.914049] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Background As a non-pharmacological therapy, acupuncture has significant efficacy in treating Mild Cognitive Impairment (MCI) compared to pharmacological therapies. In recent years, advances in neuroimaging techniques have provided new perspectives to elucidate the central mechanisms of acupuncture for MCI. Many acupuncture brain imaging studies have found significant improvements in brain function after acupuncture treatment of MCI, but the underlying mechanisms of brain regions modulation are unclear. Objective A meta-analysis of functional magnetic resonance imaging studies of MCI patients treated with acupuncture was conducted to summarize the effects of acupuncture on the modulation of MCI brain regions from a neuroimaging perspective. Methods Using acupuncture, neuroimaging, magnetic resonance, and Mild Cognitive Impairment as search terms, PubMed, EMBASE, Web of Science, Cochrane Library, Cochrane Database of Systematic Reviews, Cochrane Database of Abstracts of Reviews of Effects (DARE), Google Scholar, China National Knowledge Infrastructure (CNKI), China Biology Medicine disk (CBM disk), Wanfang and Chinese Scientific Journal Database (VIP) for brain imaging studies on acupuncture on MCI published up to April 2022. Voxel-based neuroimaging meta-analysis of fMRI data was performed using voxel-based d Mapping with Permutation of Subject Images (SDM-PSI), allowing for Family-Wise Error Rate (FWER) correction correction for correction multiple comparisons of results. Subgroup analysis was used to compare the differences in brain regions between the acupuncture treatment group and other control groups. Meta-regression was used to explore demographic information and altered cognitive function effects on brain imaging outcomes. Linear models were drawn using MATLAB 2017a, and visual graphs for quality evaluation were produced using R software and RStudio software. Results A total of seven studies met the inclusion criteria, with 94 patients in the treatment group and 112 patients in the control group. All studies were analyzed using the regional homogeneity (ReHo) method. The experimental design of fMRI included six task state studies and one resting-state study. The meta-analysis showed that MCI patients had enhanced activity in the right insula, left anterior cingulate/paracingulate gyri, right thalamus, right middle frontal gyrus, right median cingulate/paracingulate gyri, and right middle temporal gyrus brain regions after acupuncture treatment. Further analysis of RCT and longitudinal studies showed that Reho values were significantly elevated in two brain regions, the left anterior cingulate/paracingulate gyrus and the right insula, after acupuncture. The MCI group showed stronger activity in the right supramarginal gyrus after acupuncture treatment compared to healthy controls. Meta-regression analysis showed that the right anterior thalamic projection ReHo index was significantly correlated with the MMSE score after acupuncture treatment in all MCI patients. Conclusions Acupuncture therapy has a modulating effect on the brain regions of MCI patients. However, due to the inadequate experimental design of neuroimaging studies, multi-center neuroimaging studies with large samples are needed better to understand the potential neuroimaging mechanisms of acupuncture for MCI. In addition, machine learning algorithm-based predictive models for evaluating the efficacy of acupuncture for MCI may become a focus of future research. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022287826, identifier: CRD 42022287826.
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Affiliation(s)
- Shiqi Ma
- College of Acupuncture and Massage, Changchun University of Chinese Medicine, Changchun, China
| | - Haipeng Huang
- Northeast Asian Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Zhen Zhong
- College of Acupuncture and Massage, Changchun University of Chinese Medicine, Changchun, China
| | - Haizhu Zheng
- College of Acupuncture and Massage, Changchun University of Chinese Medicine, Changchun, China
| | - Mengyuan Li
- College of Acupuncture and Massage, Changchun University of Chinese Medicine, Changchun, China
| | - Lin Yao
- College of Acupuncture and Massage, Changchun University of Chinese Medicine, Changchun, China
| | - Bin Yu
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Hongfeng Wang
- Northeast Asian Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
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Ma H, Zhang D, Sun D, Wang H, Yang J. Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis. BMC Med Imaging 2022; 22:164. [PMID: 36096776 PMCID: PMC9465920 DOI: 10.1186/s12880-022-00892-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 09/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Radiomics is an emerging image analysis framework that provides more details than conventional methods. In present study, we aimed to identify structural radiomics features of gray matter (GM) and white matter (WM), and to develop and validate the classification model for major depressive disorder (MDD) and subthreshold depression (StD) diagnosis using radiomics analysis. METHODS A consecutive cohort of 142 adolescents and young adults, including 43 cases with MDD, 49 cases with StD and 50 healthy controls (HC), were recruited and underwent the three-dimensional T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI). We extracted radiomics features representing the shape and diffusion properties of GM and WM from all participants. Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features. RESULTS The results showed that a total of 3030 features were extracted after preprocessing, including 2262 shape-related features from each T1-weighted image representing GM morphometry and 768 features from each DTI representing the diffusion properties of WM. 25 features were selected ultimately, including ten features for MDD versus HC, eight features for StD versus HC, and seven features for MDD versus StD. The accuracies and area under curve (AUC) the RFC achieved were 86.75%, 0.93 for distinguishing MDD from HC with significant radiomics features located in the left medial orbitofrontal cortex, right superior and middle temporal regions, right anterior cingulate, left cuneus and hippocampus, 70.51%, 0.69 for discriminating StD from HC within left cuneus, medial orbitofrontal cortex, cerebellar vermis, hippocampus, anterior cingulate and amygdala, right superior and middle temporal regions, and 59.15%, 0.66 for differentiating MDD from StD within left medial orbitofrontal cortex, middle temporal and cuneus, right superior frontal, superior temporal regions and hippocampus, anterior cingulate, respectively. CONCLUSION These findings provide preliminary evidence that radiomics features of brain structure are valid for discriminating MDD and StD subjects from healthy controls. The MRI-based radiomics approach, with further improvement and validation, might be a potential facilitating method to clinical diagnosis of MDD or StD.
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Affiliation(s)
- Huan Ma
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, 374# DianMian Road, 650101, Kunming, China
| | - Dafu Zhang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, 650018, Kunming, China
| | - Dewei Sun
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Hongbo Wang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Jianzhong Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, 374# DianMian Road, 650101, Kunming, China.
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Inglese M, Patel N, Linton-Reid K, Loreto F, Win Z, Perry RJ, Carswell C, Grech-Sollars M, Crum WR, Lu H, Malhotra PA, Aboagye EO. A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease. COMMUNICATIONS MEDICINE 2022; 2:70. [PMID: 35759330 PMCID: PMC9209493 DOI: 10.1038/s43856-022-00133-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 05/24/2022] [Indexed: 01/12/2023] Open
Abstract
Background Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
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Affiliation(s)
- Marianna Inglese
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Neva Patel
- Department of Nuclear Medicine, Imperial College NHS Trust, London, UK
| | | | - Flavia Loreto
- Department of Brain Sciences, Imperial College London, London, UK
| | - Zarni Win
- Department of Nuclear Medicine, Imperial College NHS Trust, London, UK
| | - Richard J. Perry
- Department of Brain Sciences, Imperial College London, London, UK
- Department of Clinical Neurosciences, Imperial College NHS Trust, London, UK
| | - Christopher Carswell
- Department of Clinical Neurosciences, Imperial College NHS Trust, London, UK
- Department of Neurology, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK
| | - Matthew Grech-Sollars
- Department of Surgery and Cancer, Imperial College London, London, UK
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Guilford, UK
| | - William R. Crum
- Department of Surgery and Cancer, Imperial College London, London, UK
- Institute for Translational Medicine and Therapeutics, Imperial College London, London, UK
| | - Haonan Lu
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Paresh A. Malhotra
- Department of Brain Sciences, Imperial College London, London, UK
- Department of Clinical Neurosciences, Imperial College NHS Trust, London, UK
| | - Eric O. Aboagye
- Department of Surgery and Cancer, Imperial College London, London, UK
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Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach. Brain Imaging Behav 2022; 16:2150-2163. [PMID: 35650376 DOI: 10.1007/s11682-022-00685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2022] [Indexed: 11/02/2022]
Abstract
To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.
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Zhang L, Wang D, Dai Y, Wang X, Cao Y, Liu W, Tao Z. Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer's Disease Based on Chemiluminescence Immunoassay for Total Tau in Plasma. Front Aging Neurosci 2022; 14:863673. [PMID: 35645782 PMCID: PMC9136081 DOI: 10.3389/fnagi.2022.863673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/14/2022] [Indexed: 11/10/2022] Open
Abstract
Background Predicting amnestic mild cognitive impairment (aMCI) in conversion and Alzheimer's disease (AD) remains a daunting task. Standard diagnostic procedures for AD population are reliant on neuroimaging features (positron emission tomography, PET), cerebrospinal fluid (CSF) biomarkers (Aβ1-42, T-tau, P-tau), which are expensive or require invasive sampling. The blood-based biomarkers offer the opportunity to provide an alternative approach for easy diagnosis of AD, which would be a less invasive and cost-effective screening tool than currently approved CSF or amyloid β positron emission tomography (PET) biomarkers. Methods We developed and validated a sensitive and selective immunoassay for total Tau in plasma. Robust signatures were obtained based on several clinical features selected by multiple machine learning algorithms between the three participant groups. Subsequently, a well-fitted nomogram was constructed and validated, integrating clinical factors and total Tau concentration. The predictive performance was evaluated according to the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. Decision curve analysis and calibration curves are used to evaluate the net benefit of nomograms in clinical decision-making. Results Under optimum conditions, chemiluminescence analysis (CLIA) displays a desirable dynamic range within Tau concentration from 7.80 to 250 pg/mL with readily achieved higher performances (LOD: 5.16 pg/mL). In the discovery cohort, the discrimination between the three well-defined participant groups according to Tau concentration was in consistent agreement with clinical diagnosis (AD vs. non-MCI: AUC = 0.799; aMCI vs. non-MCI: AUC = 0.691; AD vs. aMCI: AUC = 0.670). Multiple machine learning algorithms identified Age, Gender, EMPG, Tau, ALB, HCY, VB12, and/or Glu as robust signatures. A nomogram integrated total Tau concentration and clinical factors provided better predictive performance (AD vs. non-MCI: AUC = 0.960, AD vs. aMCI: AUC = 0.813 in discovery cohort; AD vs. non-MCI: AUC = 0.938, AD vs. aMCI: AUC = 0.754 in validation cohort). Conclusion The developed assay and a satisfactory nomogram model hold promising clinical potential for early diagnosis of aMCI and AD participants.
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Dong N, Fu C, Li R, Zhang W, Liu M, Xiao W, Taylor HM, Nicholas PJ, Tanglay O, Young IM, Osipowicz KZ, Sughrue ME, Doyen SP, Li Y. Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer’s Disease and Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:854733. [PMID: 35592700 PMCID: PMC9110794 DOI: 10.3389/fnagi.2022.854733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Alzheimer’s Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD. Methods Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests. Results 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment. Conclusion Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.
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Affiliation(s)
- Ningxin Dong
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Changyong Fu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Renren Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wei Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Weixin Xiao
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | | | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
| | | | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Yunxia Li,
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Yang F, Jiang J, Alberts I, Wang M, Li T, Sun X, Rominger A, Zuo C, Shi K. Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer's disease: an exploratory radiomic analysis study. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:513. [PMID: 35928737 PMCID: PMC9347042 DOI: 10.21037/atm-21-4349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 12/19/2021] [Indexed: 11/28/2022]
Abstract
Background This study aimed to explore the potential of a combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and magnetic resonance imaging (MRI) to improve predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). The predictive performances and specific associated biomarkers of these imaging techniques used alone (single-modality imaging) and in combination (dual-modality imaging) were compared. Methods This study enrolled 377 patients with MCI and 94 healthy control participants from 2 medical centers. Enrolment was based on the patients' brain MRI and PET images. Radiomic analysis was performed to evaluate the predictive performance of dual-modality 18F-FDG PET and MRI scans. Regions of interest (ROIs) were determined using an a priori brain atlas. Radiomic features in these ROIs were extracted from the MRI and 18F-FDG PET scan data. These features were either concatenated or used separately to select features and construct Cox regression models for prediction in each modality. Harrell's concordance index (C-index) was then used to assess the predictive accuracies of the resulting models, and correlations between the MRI and 18F-FDG PET features were evaluated. Results The C-indices for the two test datasets were 0.77 and 0.80 for dual-modality 18F-FDG PET/MRI, 0.75 and 0.73 for single-modality 18F-FDG PET, and 0.74 and 0.76 for single-modality MRI. In addition, there was a significant correlation between the crucial image signatures of the different modalities. Conclusions These results indicate the value of imaging features in monitoring the progress of MCI in populations at high risk of developing AD. However, the incremental benefit of combining 18F-FDG PET and MRI is limited, and radiomic analysis of a single modality may yield acceptable predictive results.
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Affiliation(s)
- Fan Yang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Min Wang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Taoran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaoming Sun
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
- Department of Informatics, Technische Universität München, Munich, Germany
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Lin RH, Wang CC, Tung CW. A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19084839. [PMID: 35457705 PMCID: PMC9025386 DOI: 10.3390/ijerph19084839] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022]
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.
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Affiliation(s)
- Run-Hsin Lin
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan;
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei 10617, Taiwan;
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan;
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan
- Correspondence: ; Tel.: +88-6-3724-6166 (ext. 35771); Fax: +88-6-3758-6456
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Chen Y, Qian X, Zhang Y, Su W, Huang Y, Wang X, Chen X, Zhao E, Han L, Ma Y. Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Front Aging Neurosci 2022; 14:840386. [PMID: 35493941 PMCID: PMC9049273 DOI: 10.3389/fnagi.2022.840386] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/02/2022] [Indexed: 11/13/2022] Open
Abstract
Background and PurposeAlzheimer’s disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.MethodsWe systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment.ResultsIn total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67–0.98, and the validation models were 0.62–0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer’s Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models.ConclusionIn this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.
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Affiliation(s)
- Yanru Chen
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Xiaoling Qian
- Department of Neurology, Second Hospital of Lanzhou University, Lanzhou, China
| | - Yuanyuan Zhang
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Wenli Su
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Yanan Huang
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Xinyu Wang
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Xiaoli Chen
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Enhan Zhao
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
| | - Lin Han
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
- Department of Nursing, Gansu Provincial Hospital, Lanzhou, China
- *Correspondence: Yuxia Ma,
| | - Yuxia Ma
- Evidence-Based Nursing, School of Nursing, Lanzhou University, Lanzhou, China
- First School of Clinical Medicine, Lanzhou University, Lanzhou, China
- *Correspondence: Yuxia Ma,
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Lin Y, Dai P, Lin Q, Chen J. A Predictive Nomogram for Atypical Meningioma Based on Preoperative MRI and Routine Blood Tests. World Neurosurg 2022; 163:e610-e616. [DOI: 10.1016/j.wneu.2022.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 11/30/2022]
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Shi D, Zhang H, Wang G, Wang S, Yao X, Li Y, Guo Q, Zheng S, Ren K. Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis. Front Aging Neurosci 2022; 14:806828. [PMID: 35309885 PMCID: PMC8928361 DOI: 10.3389/fnagi.2022.806828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/19/2022] [Indexed: 12/03/2022] Open
Abstract
Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuang Zheng
- School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Jiang J, Wang M, Alberts I, Sun X, Li T, Rominger A, Zuo C, Han Y, Shi K, Initiative FTADN. Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease. Eur J Nucl Med Mol Imaging 2022; 49:2163-2173. [PMID: 35032179 DOI: 10.1007/s00259-022-05687-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/11/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. METHOD FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. RESULTS The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. CONCLUSION The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.
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Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China.
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Xiaoming Sun
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Taoran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- Human Phenome Institute, Fudan University, Shanghai, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.
- School of Biomedical Engineering, Hainan University, Haikou, China.
- National Clinical Research Center for Geriatric Disorders, Beijing, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
- Department of Informatics, Technische Universität München, Munich, Germany
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Song R, Wu X, Liu H, Guo D, Tang L, Zhang W, Feng J, Li C. Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study. Korean J Radiol 2022; 23:89-100. [PMID: 34983097 PMCID: PMC8743156 DOI: 10.3348/kjr.2021.0323] [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: 04/21/2021] [Revised: 08/19/2021] [Accepted: 08/24/2021] [Indexed: 11/15/2022] Open
Abstract
Objective To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer’s disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. Results The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer’s continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer’s disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. Conclusion We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.
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Affiliation(s)
- Rao Song
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Tang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junbang Feng
- Department of Radiology, Chongqing Emergency Medical Center, Chongqing, China
| | - Chuanming Li
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Kong J, Zhu S, Shi G, Liu Z, Zhang J, Ren J. Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model. Front Oncol 2021; 11:739933. [PMID: 34631575 PMCID: PMC8499696 DOI: 10.3389/fonc.2021.739933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Chemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy. MATERIALS AND METHODS In total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model. RESULTS Of the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 = 0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645-0.787) in the training group and 0.718 (95% CI: 0.612-0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674-0.810) in the training group and 0.715 (95% CI: 0.609-0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy. CONCLUSIONS A radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.
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Affiliation(s)
- Jie Kong
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shuchai Zhu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Gaofeng Shi
- Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhikun Liu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jun Zhang
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jialiang Ren
- Pharmaceutical Diagnosis, GE Healthcare, Beijing, China
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Tang C, Zhao X, Wu W, Zhong W, Wu X. An individualized prediction of time to cognitive impairment in Parkinson's disease: A combined multi-predictor study. Neurosci Lett 2021; 762:136149. [PMID: 34352339 DOI: 10.1016/j.neulet.2021.136149] [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: 02/24/2021] [Revised: 05/29/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Cognitive impairment (CI) is important for the prognosis of Parkinson's disease (PD). Early prediction whether and when cognitive decline from normal cognition (NC) will occur is crucial for preventing or delaying the progression timely. The current study aimed to provide a personalized risk assessment of CI by using baseline information and establishing a multi-predictor nomogram. METHODS 108 patients with PD were collected from the Parkinson's Progression Markers Initiative (PPMI), of whom 58 had progressed to CI and 50 remained NC during 5-year follow up. Radiomics signatures were obtained by using least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. Clinical factors and laboratory biomarkers were selected by multivariate Cox regression analysis. The combined model of radiomics signatures and clinical risk factors was developed by a multivariate Cox proportional hazard model. A multi-predictor nomogram derived from the combined model was established for individualized estimation of time to progress (TTP) of CI. We analyzed the risk of two subgroups of the combined model by Kaplan-Meier (KM) analysis. RESULTS The combined model showed the best performance with a C-index of 0.988 and 0.926 in the training and validation datasets. KM analysis verified significant TTP of CI (P<0.05) between two subgroups stratified by the cutoff value (-0.058). CONCLUSION The combined model and its multi-predictor nomogram can be used to perfectly and individually predict the TTP of CI for patients with PD. Stratification of PD will benefit its timely clinical intervention and the delay and prevention of CI.
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Affiliation(s)
- Chunyan Tang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoyan Zhao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Wu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weijia Zhong
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xiaojia Wu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Soni N, Ora M, Bathla G, Nagaraj C, Boles Ponto LL, Graham MM, Saini J, Menda Y. Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions. Neuroradiol J 2021; 34:263-288. [PMID: 33666110 PMCID: PMC8447818 DOI: 10.1177/1971400921998968] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Neurodegenerative diseases (NDDs) are characterized by progressive neuronal loss, leading to dementia and movement disorders. NDDs broadly include Alzheimer's disease, frontotemporal lobar degeneration, parkinsonian syndromes, and prion diseases. There is an ever-increasing prevalence of mild cognitive impairment and dementia, with an accompanying immense economic impact, prompting efforts aimed at early identification and effective interventions. Neuroimaging is an essential tool for the early diagnosis of NDDs in both clinical and research settings. Structural, functional, and metabolic imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are widely available. They show encouraging results for diagnosis, monitoring, and treatment response evaluation. The current review focuses on the complementary role of various imaging modalities in relation to NDDs, the qualitative and quantitative utility of newer MRI techniques, novel radiopharmaceuticals, and integrated PET/MRI in the setting of NDDs.
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Affiliation(s)
- Neetu Soni
- University of Iowa Hospitals and Clinics, USA
| | - Manish Ora
- Department of Nuclear Medicine, SGPGIMS, India
| | - Girish Bathla
- Neuroradiology Department, University of Iowa Hospitals and
Clinics, USA
| | - Chandana Nagaraj
- Department of Neuro Imaging and Interventional Radiology,
NIMHANS, India
| | | | - Michael M Graham
- Division of Nuclear Medicine, University of Iowa Hospitals and
Clinics, USA
| | - Jitender Saini
- Department of Neuro Imaging and Interventional Radiology,
NIMHANS, India
| | - Yusuf Menda
- University of Iowa Hospitals and Clinics, USA
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Wang L, Li P, Hou M, Zhang X, Cao X, Li H. Construction of a risk prediction model for Alzheimer's disease in the elderly population. BMC Neurol 2021; 21:271. [PMID: 34233656 PMCID: PMC8262052 DOI: 10.1186/s12883-021-02276-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 06/09/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer's disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population. METHODS Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model. RESULTS Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set). CONCLUSION The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.
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Affiliation(s)
- Lingling Wang
- Department of Neurology, People's Hospital of Xinjiang Uygur Autonomous Region, NO.91 Tianchi Road, Tianshan District, Urumqi, Xinjiang, 830001, Uygur Autonomous Region, China
| | - Ping Li
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Ming Hou
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Xiumin Zhang
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Xiaolin Cao
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Hongyan Li
- Department of Neurology, People's Hospital of Xinjiang Uygur Autonomous Region, NO.91 Tianchi Road, Tianshan District, Urumqi, Xinjiang, 830001, Uygur Autonomous Region, China.
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Chen W, Li S, Ma Y, Lv S, Wu F, Du J, Wu H, Wang S, Zhao Q. A simple nomogram prediction model to identify relatively young patients with mild cognitive impairment who may progress to Alzheimer's disease. J Clin Neurosci 2021; 91:62-68. [PMID: 34373060 DOI: 10.1016/j.jocn.2021.06.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 05/16/2021] [Accepted: 06/14/2021] [Indexed: 12/25/2022]
Abstract
AIM Construct a clinical predictive model based on easily accessible clinical features and imaging data to identify patients 65 years of age and younger with mild cognitive impairment(MCI) who may progress to Alzheimer's disease(AD). METHODS From the ADNI database, patients with MCI who were less than or equal to 65 years of age and who had been followed for 6-60 months were selected.We collected demographic data, neuropsychological test scale scores, and structural magnetic images of these patients. Clinical characteristics were then screened, and VBM and SBM analyses were performed using structural nuclear magnetic images to obtain imaging histology characteristics. Finally, predictive models were constructed combining the clinical and imaging histology characteristics. RESULTS The constructed nomogram has a cross-validated AUC of 0.872 in the training set and 0.867 in the verification set, and the calibration curve fits well.We also provide an online model-based forecasting tool. CONCLUSION The model has good performance and uses convenience,it should be able to provide assistance in clinical work to screen relatively young MCI patients who may progress to AD.
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Affiliation(s)
- Wenhong Chen
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Songtao Li
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yangyang Ma
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuyue Lv
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Fan Wu
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jianshi Du
- Department of Vascular Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Honglin Wu
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuai Wang
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qing Zhao
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
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Cui LB, Xu X, Cao F. Building the Precision Medicine for Mental Disorders via Radiomics/Machine Learning and Neuroimaging. Front Neurosci 2021; 15:685005. [PMID: 34220441 PMCID: PMC8250851 DOI: 10.3389/fnins.2021.685005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Affiliation(s)
- Long-Biao Cui
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China.,Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Xian Xu
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Feng Cao
- The Second Medical Center, National Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
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Tang L, Wu X, Liu H, Wu F, Song R, Zhang W, Guo D, Feng J, Li C. Individualized Prediction of Early Alzheimer's Disease Based on Magnetic Resonance Imaging Radiomics, Clinical, and Laboratory Examinations: A 60-Month Follow-Up Study. J Magn Reson Imaging 2021; 54:1647-1657. [PMID: 33987915 DOI: 10.1002/jmri.27689] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accurately predicting whether and when mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is of vital importance to help developing individualized treatment plans to defer the occurrence of irreversible dementia. PURPOSE To develop and validate radiomics models and multipredictor nomogram for predicting the time to progression (TTP) from MCI to AD. STUDY TYPE Retrospective. POPULATION One hundred sixty-two MCI patients (96 men and 66 women [median age, 72; age range, 56-88 years]) were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. FIELD STRENGTH/SEQUENCE T1 -weighted imaging and T2 -weighted fluid-attenuation inversion recovery imaging acquired at 3.0 T. ASSESSMENT During the 5-year follow-up, 68 patients converted to AD and 94 remained stable. Patients were randomly divided into the training (n = 112) and validation datasets (n = 50). Radiomic features were extracted from the whole cerebral cortex and subcortical nucleus of MR images. A radiomics model was established using least absolute shrinkage and selection operator (LASSO) Cox regression. The clinical-laboratory model and radiomics-clinical-laboratory model were developed by multivariate Cox proportional hazard model. The performance of each model was assessed by the concordance index (C-index). A multipredictor nomogram derived from the radiomics-clinical-laboratory model was constructed for individualized TTP estimation. STATISTICAL TESTS LASSO cox regression, univariate and multivariate Cox regression, Kaplan-Meier analysis and Student's t test were performed. RESULTS The C-index of the radiomics, clinical-laboratory and radiomics-clinical-laboratory models were 0.924 (95% confidence interval [CI]: 0.894-0.952), 0.903 (0.868-0.938), 0.950 (0.929-0.971) in the training cohort and 0.811 (0.707-0.914), 0.901 (0824-0.977), 0.907 (0.836-0.979) in the validation cohort, respectively. A multipredictor nomogram with 15 predictors was established, which had high accuracy for individual TTP prediction with the C-index of 0.950 (0.929-0.971). DATA CONCLUSION The prediction of individual TTP from MCI to AD could be accurately conducted using the radiomics-clinical-laboratory model and multipredictor nomogram. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Lin Tang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Jinan, China
| | - Rao Song
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junbang Feng
- Department of Radiology, Chongqing Emergency Medical Center, Chongqing, China
| | - Chuanming Li
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Shi D, Zhang H, Wang S, Wang G, Ren K. Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis. Front Aging Neurosci 2021; 13:624731. [PMID: 34045953 PMCID: PMC8144304 DOI: 10.3389/fnagi.2021.624731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 03/22/2021] [Indexed: 01/08/2023] Open
Abstract
This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.
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Affiliation(s)
| | | | | | | | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xia Men University, Xiamen, China
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Velazquez M, Lee Y. Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects. PLoS One 2021; 16:e0244773. [PMID: 33914757 PMCID: PMC8084194 DOI: 10.1371/journal.pone.0244773] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/12/2021] [Indexed: 12/01/2022] Open
Abstract
Alzheimer's Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.
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Affiliation(s)
- Matthew Velazquez
- Department of Computer Science, University of Missouri - Kansas City, Kansas City, MO, United States of America
| | - Yugyung Lee
- Department of Computer Science, University of Missouri - Kansas City, Kansas City, MO, United States of America
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Kim JP, Kim J, Jang H, Kim J, Kang SH, Kim JS, Lee J, Na DL, Kim HJ, Seo SW, Park H. Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach. Sci Rep 2021; 11:6954. [PMID: 33772041 PMCID: PMC7997887 DOI: 10.1038/s41598-021-86114-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 02/23/2021] [Indexed: 02/01/2023] Open
Abstract
Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.
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Affiliation(s)
- Jun Pyo Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jonghoon Kim
- grid.264381.a0000 0001 2181 989XDepartment of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Korea
| | - Hyemin Jang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jaeho Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.256753.00000 0004 0470 5964Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
| | - Sung Hoon Kang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Ji Sun Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jongmin Lee
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Duk L. Na
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon-si, Korea
| | - Hyunjin Park
- grid.410720.00000 0004 1784 4496Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea ,grid.264381.a0000 0001 2181 989XSchool of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon-si, Republic of Korea
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Song LL, Chen SJ, Chen W, Shi Z, Wang XD, Song LN, Chen DS. Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images. BMC Med Imaging 2021; 21:54. [PMID: 33743615 PMCID: PMC7981906 DOI: 10.1186/s12880-021-00581-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 03/07/2021] [Indexed: 01/04/2023] Open
Abstract
Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00581-9.
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Affiliation(s)
- Le-le Song
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Shun-Jun Chen
- The Department of Ultrasound, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Wang Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Zhan Shi
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Xiao-Dong Wang
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Li-Na Song
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dian-Sen Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China.
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Lin Y, Huang K, Xu H, Qiao Z, Cai S, Wang Y, Huang L. Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning. Clin Neurophysiol 2020; 131:2429-2439. [PMID: 32829290 DOI: 10.1016/j.clinph.2020.07.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 06/05/2020] [Accepted: 07/02/2020] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic resonance imaging-based prediction system for MCI progression. METHODS A total of 164 MCI patients with longitudinal data were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After preprocessing, a discriminative dictionary learning framework was applied to differentiate MCI patches, avoiding the segmentation of regions of interest. Then, the proportion of patches classified as more severe atrophy patches in a patient was calculated as his or her feature to be input into a simple support vector machine. Finally, a new subject was predicted with fourfold cross-validation (CV), and the area under the receiver operating characteristic curve (AUC) was determined. RESULTS The average accuracy and AUC values after fourfold CV were 0.973 and 0.984, respectively. The effects of the data from one or two time points were also investigated. CONCLUSION The proposed prediction system achieves desirable and reliable performance in predicting progression for MCI patients. Additionally, the prediction of MCI progression with longitudinal data was more effective and accurate. SIGNIFICANCE The developed scheme is expected to advance the clinical research and treatment of MCI patients.
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Affiliation(s)
- Yanyan Lin
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China
| | - Kexin Huang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China
| | - Hanxiao Xu
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China
| | - Zhengzheng Qiao
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China
| | - Suping Cai
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China.
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- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China
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Qiu Q, Duan J, Deng H, Han Z, Gu J, Yue NJ, Yin Y. Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery. Front Oncol 2020; 10:1398. [PMID: 32850451 PMCID: PMC7431604 DOI: 10.3389/fonc.2020.01398] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/02/2020] [Indexed: 12/24/2022] Open
Abstract
Background and purpose: Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Materials and methods: Two hundred six patients with ESCC were enrolled and divided into a training cohort (n = 146) and a validation cohort (n = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. Results: The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature (P < 0.001) and clinical nomogram (P < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680-0.812) vs. 0.685 (0.620-0.750) vs. 0.614 (0.538-0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696-0.752) vs. 0.671 (0.624-0.718) vs. 0.629 (0.597-0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. Conclusions: A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.
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Affiliation(s)
- Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinghao Duan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hongbin Deng
- Department of Medical Imaging Ultrasonography, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhujun Han
- Department of Radiation Oncology, Yantai Yuhuangding Hospital, Yantai, China
| | - Jiabing Gu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ning J Yue
- Department of Radiation Oncology, The Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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