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Kim REY, Lee M, Kang DW, Wang SM, Kim D, Lim HK. Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest. J Alzheimers Dis 2024; 97:259-271. [PMID: 38143346 DOI: 10.3233/jad-230602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
BACKGROUND Brain volume is associated with cognitive decline in later life, and cortical brain atrophy exceeding the normal range is related to inferior cognitive and behavioral outcomes in later life. OBJECTIVE To investigate the likelihood of cognitive decline, mild cognitive impairment (MCI), or dementia, when regional atrophy is present in participants' magnetic resonance imaging (MRI). METHODS Multi-center MRI data of 2,545 adults were utilized to measure regional volumes using NEUROPHET AQUA. Four lobes (frontal, parietal, temporal, and occipital), four Alzheimer's disease-related regions (entorhinal, fusiform, inferior temporal, and middle temporal area), and the hippocampus in the left and right hemispheres were measured and analyzed. The presence of regional atrophy from brain MRI was defined as ≤1.5 standard deviation (SD) compared to the age- and sex-matched cognitively normal population. The risk ratio for cognitive decline was investigated for participants with regional atrophy in contrast to those without regional atrophy. RESULTS The risk ratio for cognitive decline was significantly higher when hippocampal atrophy was present (MCI, 1.84, p < 0.001; dementia, 4.17, p < 0.001). Additionally, participants with joint atrophy in multiple regions showed a higher risk ratio for dementia, e.g., 9.6 risk ratio (95% confidence interval, 8.0-11.5), with atrophy identified in the frontal, temporal, and hippocampal gray matter, than those without atrophy. CONCLUSIONS Our study showed that individuals with multiple regional atrophy (either lobar or AD-specific regions) have a higher likelihood of developing dementia compared to the age- and sex-matched population without atrophy. Thus, further consideration is needed when assessing MRI findings.
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Affiliation(s)
- Regina E Y Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, Republic of Korea
- Department of Psychiatry, Iowa City, IA, University of Iowa, United States of America
| | - Minho Lee
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Nallapu BT, Petersen KK, Lipton RB, Davatzikos C, Ezzati A. Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment. J Alzheimers Dis 2024; 98:231-246. [PMID: 38393899 PMCID: PMC11044769 DOI: 10.3233/jad-230620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Background Blood-based biomarkers (BBMs) are of growing interest in the field of Alzheimer's disease (AD) and related dementias. Objective This study aimed to assess the ability of plasma biomarkers to 1) predict disease progression from mild cognitive impairment (MCI) to dementia and 2) improve the predictive ability of magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) measures when combined. Methods We used data from the Alzheimer's Disease Neuroimaging Initiative. Machine learning models were trained using the data from participants who remained cognitively stable (CN-s) and with Dementia diagnosis at 2-year follow-up visit. The models were used to predict progression to dementia in MCI individuals. We assessed the performance of models with plasma biomarkers against those with CSF and MRI measures, and also in combination with them. Results Our models with plasma biomarkers classified CN-s individuals from AD with an AUC of 0.75±0.03 and could predict conversion to dementia in MCI individuals with an AUC of 0.64±0.03 (17.1% BP, base prevalence). Models with plasma biomarkers performed better when combined with CSF and MRI measures (CN versus AD: AUC of 0.89±0.02; MCI-to-AD: AUC of 0.76±0.03, 21.5% BP). Conclusions Our results highlight the potential of plasma biomarkers in predicting conversion to dementia in MCI individuals. While plasma biomarkers could improve the predictive ability of CSF and MRI measures when combined, they also show the potential to predict non-progression to AD when considered alone. The predictive ability of plasma biomarkers is crucially linked to reducing the costly and effortful collection of CSF and MRI measures.
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Affiliation(s)
- Bhargav T. Nallapu
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Kellen K. Petersen
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Richard B. Lipton
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Christos Davatzikos
- Radiology Department, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ali Ezzati
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
- Department of Neurology, University of California, Irvine, Irvine, CA, USA
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Kress GT, Popa ES, Thompson PM, Bookheimer SY, Thomopoulos SI, Ching CRK, Zheng H, Hirsh DA, Merrill DA, Panos SE, Raji CA, Siddarth P, Bramen JE. Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline. Neuroimage Clin 2023; 39:103458. [PMID: 37421927 PMCID: PMC10338152 DOI: 10.1016/j.nicl.2023.103458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.
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Affiliation(s)
- Gavin T Kress
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Emily S Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Susan Y Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Daniel A Hirsh
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Department of Translational Neurosciences and Neurotherapeutics, Providence Saint John's Cancer Institute, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Stella E Panos
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Jennifer E Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
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Eslami M, Tabarestani S, Adjouadi M. A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease. Artif Intell Med 2023; 140:102543. [PMID: 37210151 DOI: 10.1016/j.artmed.2023.102543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/28/2023] [Accepted: 04/02/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mechanism driven by an integrated ML model to predict disease trajectory in a 2-year longitudinal study. The main aim of this study is to help capture visually in 2D and 3D renderings the diagnosis and prognosis of AD, therefore augmenting our understanding of the processes of multiclass classification and regression analysis. METHOD The proposed method, Machine Learning for Visualizing AD (ML4VisAD), is designed to predict disease progression through a visual output. This newly developed model takes baseline measurements as input to generate a color-coded visual image that reflects disease progression at different time points. The architecture of the network relies on convolutional neural networks. With 1123 subjects selected from the ADNI QT-PAD dataset, we use a 10-fold cross-validation process to evaluate the method. Multimodal inputs* include neuroimaging data (MRI, PET), neuropsychological test scores (excluding MMSE, CDR-SB, and ADAS to avoid bias), cerebrospinal fluid (CSF) biomarkers with measures of amyloid beta (ABETA), phosphorylated tau protein (PTAU), total tau protein (TAU), and risk factors that include age, gender, years of education, and ApoE4 gene. FINDINGS/RESULTS Based on subjective scores reached by three raters, the results showed an accuracy of 0.82 ± 0.03 for a 3-way classification and 0.68 ± 0.05 for a 5-way classification. The visual renderings were generated in 0.08 msec for a 23 × 23 output image and in 0.17 ms for a 45 × 45 output image. Through visualization, this study (1) demonstrates that the ML visual output augments the prospects for a more accurate diagnosis and (2) highlights why multiclass classification and regression analysis are incredibly challenging. An online survey was conducted to gauge this visualization platform's merits and obtain valuable feedback from users. All implementation codes are shared online on GitHub. CONCLUSION This approach makes it possible to visualize the many nuances that lead to a specific classification or prediction in the disease trajectory, all in context to multimodal measurements taken at baseline. This ML model can serve as a multiclass classification and prediction model while reinforcing the diagnosis and prognosis capabilities by including a visualization platform.
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Affiliation(s)
- Mohammad Eslami
- Harvard Ophthalmology AI lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA; Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
| | - Solale Tabarestani
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
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Shi Y, Wang Z, Chen P, Cheng P, Zhao K, Zhang H, Shu H, Gu L, Gao L, Wang Q, Zhang H, Xie C, Liu Y, Zhang Z. Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:171-180. [PMID: 33712376 DOI: 10.1016/j.bpsc.2020.12.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/16/2020] [Accepted: 12/16/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Individualized and reliable biomarkers are crucial for diagnosing Alzheimer's disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD. METHODS Episodic memory-related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-β plaques. RESULTS The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = 0.638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory-related regions showed significantly different amyloid-β positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups. CONCLUSIONS Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Piaoyue Cheng
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Kun Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, China; Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Lihua Gu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Lijuan Gao
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Haisan Zhang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China; School of Life Science and Technology, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, China; Department of Psychology, Xinxiang Medical University, Xinxiang, China; Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.
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Zubrikhina M, Abramova O, Yarkin V, Ushakov V, Ochneva A, Bernstein A, Burnaev E, Andreyuk D, Savilov V, Kurmishev M, Syunyakov T, Karpenko O, Andryushchenko A, Kostyuk G, Sharaev M. Machine learning approaches to Mild Cognitive Impairment detection based on structural MRI data and morphometric features. COGN SYST RES 2022. [DOI: 10.1016/j.cogsys.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Van Dyk K, Ahn J, Zhou X, Zhai W, Ahles TA, Bethea TN, Carroll JE, Cohen HJ, Dilawari AA, Graham D, Jacobsen PB, Jim H, McDonald BC, Nakamura ZM, Patel SK, Rentscher KE, Saykin AJ, Small BJ, Mandelblatt JS, Root JC. Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: The Thinking and Living with Cancer study. J Geriatr Oncol 2022; 13:1132-1140. [PMID: 36030173 PMCID: PMC10016202 DOI: 10.1016/j.jgo.2022.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 07/16/2022] [Accepted: 08/10/2022] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Many cancer survivors report cognitive problems following diagnosis and treatment. However, the clinical significance of patient-reported cognitive symptoms early in survivorship can be unclear. We used a machine learning approach to determine the association of persistent self-reported cognitive symptoms two years after diagnosis and neurocognitive test performance in a prospective cohort of older breast cancer survivors. MATERIALS AND METHODS We enrolled breast cancer survivors with non-metastatic disease (n = 435) and age- and education-matched non-cancer controls (n = 441) between August 2010 and December 2017 and followed until January 2020; we excluded women with neurological disease and all women passed a cognitive screen at enrollment. Women completed the FACT-Cog Perceived Cognitive Impairment (PCI) scale and neurocognitive tests of attention, processing speed, executive function, learning, memory and visuospatial ability, and timed activities of daily living assessments at enrollment (pre-systemic treatment) and annually to 24 months, for a total of 59 individual neurocognitive measures. We defined persistent self-reported cognitive decline as clinically meaningful decline (3.7+ points) on the PCI scale from enrollment to twelve months with persistence to 24 months. Analysis used four machine learning models based on data for change scores (baseline to twelve months) on the 59 neurocognitive measures and measures of depression, anxiety, and fatigue to determine a set of variables that distinguished the 24-month persistent cognitive decline group from non-cancer controls or from survivors without decline. RESULTS The sample of survivors and controls ranged in age from were ages 60-89. Thirty-three percent of survivors had self-reported cognitive decline at twelve months and two-thirds continued to have persistent decline to 24 months (n = 60). Least Absolute Shrinkage and Selection Operator (LASSO) models distinguished survivors with persistent self-reported declines from controls (AUC = 0.736) and survivors without decline (n = 147; AUC = 0.744). The variables that separated groups were predominantly neurocognitive test performance change scores, including declines in list learning, verbal fluency, and attention measures. DISCUSSION Machine learning may be useful to further our understanding of cancer-related cognitive decline. Our results suggest that persistent self-reported cognitive problems among older women with breast cancer are associated with a constellation of mild neurocognitive changes warranting clinical attention.
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Affiliation(s)
- Kathleen Van Dyk
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, United States of America.
| | - Jaeil Ahn
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, United States of America
| | - Xingtao Zhou
- Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
| | - Wanting Zhai
- Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
| | - Tim A Ahles
- Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Traci N Bethea
- Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
| | - Judith E Carroll
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America; Cousins Center for Psychoneuroimmunology, University of California, Los Angeles, CA, United States of America
| | - Harvey Jay Cohen
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC, United States of America
| | - Asma A Dilawari
- Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
| | - Deena Graham
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, United States of America
| | - Paul B Jacobsen
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, United States of America
| | - Heather Jim
- Department of Health Outcomes and Behavior, Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL, United States of America
| | - Brenna C McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences and the Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Zev M Nakamura
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States of America
| | - Sunita K Patel
- City of Hope National Medical Center, Los Angeles, CA, United States of America
| | - Kelly E Rentscher
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America; Cousins Center for Psychoneuroimmunology, University of California, Los Angeles, CA, United States of America
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences and the Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Brent J Small
- University of South Florida, Health Outcome and Behavior Program and Biostatistics Resource Core, H. Lee Moffitt Cancer Center, Research Institute at the University of South Florida, Tampa, FL, United States of America
| | - Jeanne S Mandelblatt
- Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
| | - James C Root
- Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
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Chen D, Yi F, Qin Y, Zhang J, Ge X, Han H, Cui J, Bai W, Wu Y, Yu H. A Stacking Framework for Multi-Classification of Alzheimer’s Disease Using Neuroimaging and Clinical Features. J Alzheimers Dis 2022; 87:1627-1636. [DOI: 10.3233/jad-215654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease (AD) is a severe health problem. Challenges still remain in early diagnosis. Objective: The objective of this study was to build a Stacking framework for multi-classification of AD by a combination of neuroimaging and clinical features to improve the performance. Methods: The data we used were from the Alzheimer’s Disease Neuroimaging Initiative database with a total of 493 subjects, including 125 normal control (NC), 121 early mild cognitive impairment, 109 late mild cognitive impairment (LMCI), and 138 AD. We selected structural magnetic resonance imaging (sMRI) features by voting strategy. The imaging features, demographic information, Mini-Mental State Examination, and Alzheimer’s Disease Assessment Scale-Cognitive Subscale were combined together as classification features. We proposed a two-layer Stacking ensemble framework to classify four types of people. The first layer represented support vector machine, random forests, adaptive boosting, and gradient boosting decision tree; the second layer was a logistic regression classifier. Additionally, we analyzed performance of only sMRI feature and combined features and compared the proposed model with four base classifiers. Results: The Stacking model combined with sMRI and non-imaging features outshined four base classifiers with an average accuracy of 86.96% . Compared with using sMRI data alone, sMRI combined with non-imaging features significantly improved diagnostic accuracy, especially in NC versus LMCI and LMCI versus AD by 14.08% . Conclusion: The Stacking framework we used can improve performance in diagnosis of AD using combined features.
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Affiliation(s)
- Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Fuliang Yi
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jiajia Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongjuan Han
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Wenlin Bai
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yan Wu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
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9
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Ezzati A, Zammit AR, Lipton RB. Comparing Performance of Different Predictive Models in Estimating Disease Progression in Alzheimer Disease. Alzheimer Dis Assoc Disord 2022; 36:176-179. [PMID: 34393191 PMCID: PMC8847534 DOI: 10.1097/wad.0000000000000474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 07/07/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Automatic classification techniques provide tools to analyze complex data and predict disease progression. METHODS A total of 305 cognitively normal; 475 patients with amnestic mild cognitive impairment (aMCI); and 162 patients with dementia were included in this study. We compared the performance of 3 different methods in predicting progression from aMCI to dementia: (1) index-based model; (2) logistic regression (LR); and (3) ensemble linear discriminant (ELD) machine learning models. LR and ELD models were trained using data from cognitively normal and dementia subgroups, and subsequently were applied to aMCI subgroup to predict their disease progression. RESULTS Performance of ELD models were better than LR models in prediction of conversion from aMCI to Alzheimer dementia at all time frames. ELD models performed better when a larger number of features were used for prediction. CONCLUSION Machine learning models have substantial potential to improve the predictive ability for cognitive outcomes.
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Affiliation(s)
- Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
| | - Andrea R. Zammit
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Richard B. Lipton
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
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10
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Ezzati A, Davatzikos C, Wolk DA, Hall CB, Habeck C, Lipton RB. Application of predictive models in boosting power of Alzheimer's disease clinical trials: A post hoc analysis of phase 3 solanezumab trials. Alzheimers Dement (N Y) 2022; 8:e12223. [PMID: 35310531 PMCID: PMC8919041 DOI: 10.1002/trc2.12223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 07/15/2021] [Accepted: 11/01/2021] [Indexed: 01/18/2023]
Abstract
Background The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and would also respond to the therapeutic intervention. Objective To investigate if predictive models can be an effective tool for identifying and excluding people unlikely to show cognitive decline as an enrichment strategy in AD trials. Method We used data from the placebo arms of two phase 3, double-blind trials, EXPEDITION and EXPEDITION2. Patients had 18 months of follow-up. Based on the longitudinal data from the placebo arm, we classified participants into two groups: one showed cognitive decline (any negative slope) and the other showed no cognitive decline (slope is zero or positive) on the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-cog). We used baseline data for EXPEDITION to train regression-based classifiers and machine learning classifiers to estimate probability of cognitive decline. Models were applied to EXPEDITION2 data to assess predicted performance in an independent sample. Features used in predictive models included baseline demographics, apolipoprotein E ε4 genotype, neuropsychological scores, functional scores, and volumetric magnetic resonance imaging. Result In EXPEDITION, 46.3% of placebo-treated patients showed no cognitive decline and the proportion was similar in EXPEDITION2 (45.6%). Models had high sensitivity and modest specificity in both the training (EXPEDITION) and replication samples (EXPEDITION2) for detecting the stable group. Positive predictive value of models was higher than the base prevalence of cognitive decline, and negative predictive value of models were higher than the base rate of participants who had stable cognition. Conclusion Excluding persons with AD unlikely to decline from the active and placebo arms of clinical trials using predictive models may boost the power of AD trials through selective inclusion of participants expected to decline.
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Affiliation(s)
- Ali Ezzati
- Department of NeurologyAlbert Einstein College of Medicine and Montefiore Medical CenterBronxNew YorkUSA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Penn Memory CenterPerelman Center for Advanced MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Charles B. Hall
- Department of Department of Epidemiology and Population HealthAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Christian Habeck
- Department of NeurologyCognitive Neuroscience DivisionColumbia UniversityNew YorkNew YorkUSA
| | - Richard B. Lipton
- Department of NeurologyAlbert Einstein College of Medicine and Montefiore Medical CenterBronxNew YorkUSA
- Department of Department of Epidemiology and Population HealthAlbert Einstein College of MedicineBronxNew YorkUSA
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11
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Silva-spínola A, Baldeiras I, Arrais JP, Santana I. The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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12
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Shojaie M, Tabarestani S, Cabrerizo M, DeKosky ST, Vaillancourt DE, Loewenstein D, Duara R, Adjouadi M. PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification. J Alzheimers Dis 2022; 84:1497-1514. [PMID: 34719488 DOI: 10.3233/jad-210064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer's disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. OBJECTIVE This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. METHODS From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. RESULTS Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. CONCLUSION The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.
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Affiliation(s)
- Mehdi Shojaie
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Solale Tabarestani
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Steven T DeKosky
- Department of Neurology, University of Florida, Gainesville, FL, USA.,1Florida ADRC (Florida Alzheimer's Disease Research Center), Gainesville, FL, USA
| | - David E Vaillancourt
- Department of Neurology, University of Florida, Gainesville, FL, USA.,Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA.,1Florida ADRC (Florida Alzheimer's Disease Research Center), Gainesville, FL, USA
| | - David Loewenstein
- Center for Cognitive Neuroscience and Aging, University of Miami Miller School of Medicine, Miami, FL, USA.,1Florida ADRC (Florida Alzheimer's Disease Research Center), Gainesville, FL, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease & Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA.,1Florida ADRC (Florida Alzheimer's Disease Research Center), Gainesville, FL, USA
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.,1Florida ADRC (Florida Alzheimer's Disease Research Center), Gainesville, FL, USA
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13
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Haneef R, Tijhuis M, Thiébaut R, Májek O, Pristaš I, Tolenan H, Gallay A. Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques. Arch Public Health 2022; 80:9. [PMID: 34983651 PMCID: PMC8725299 DOI: 10.1186/s13690-021-00770-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 12/17/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The capacity to use data linkage and artificial intelligence to estimate and predict health indicators varies across European countries. However, the estimation of health indicators from linked administrative data is challenging due to several reasons such as variability in data sources and data collection methods resulting in reduced interoperability at various levels and timeliness, availability of a large number of variables, lack of skills and capacity to link and analyze big data. The main objective of this study is to develop the methodological guidelines calculating population-based health indicators to guide European countries using linked data and/or machine learning (ML) techniques with new methods. METHOD We have performed the following step-wise approach systematically to develop the methodological guidelines: i. Scientific literature review, ii. Identification of inspiring examples from European countries, and iii. Developing the checklist of guidelines contents. RESULTS We have developed the methodological guidelines, which provide a systematic approach for studies using linked data and/or ML-techniques to produce population-based health indicators. These guidelines include a detailed checklist of the following items: rationale and objective of the study (i.e., research question), study design, linked data sources, study population/sample size, study outcomes, data preparation, data analysis (i.e., statistical techniques, sensitivity analysis and potential issues during data analysis) and study limitations. CONCLUSIONS This is the first study to develop the methodological guidelines for studies focused on population health using linked data and/or machine learning techniques. These guidelines would support researchers to adopt and develop a systematic approach for high-quality research methods. There is a need for high-quality research methodologies using more linked data and ML-techniques to develop a structured cross-disciplinary approach for improving the population health information and thereby the population health.
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Affiliation(s)
- Romana Haneef
- Department of Non-Communicable Diseases and Injuries, Santé Publique France, Saint-Maurice, France.
| | - Mariken Tijhuis
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Rodolphe Thiébaut
- Bordeaux University, Bordeaux School of Public Health, Bordeaux, France.,INSERM / INRIA SISTM team, Bordeaux Population health, Bordeaux, France.,Medical Information Department, Bordeaux University Hospital, Bordeaux, France
| | - Ondřej Májek
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic.,Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ivan Pristaš
- National Institute of public health, division of health informatics and biostatistics, Zagreb, Croatia
| | - Hanna Tolenan
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Anne Gallay
- Department of Non-Communicable Diseases and Injuries, Santé Publique France, Saint-Maurice, France
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14
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Das S, Panigrahi P, Chakrabarti S. Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques. J Alzheimers Dis Rep 2021; 5:771-788. [PMID: 34870103 PMCID: PMC8609489 DOI: 10.3233/adr-210314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2021] [Indexed: 01/25/2023] Open
Abstract
Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.
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Affiliation(s)
- Subhrangshu Das
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Priyanka Panigrahi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
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15
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Ezzati A, Abdulkadir A, Jack CR, Thompson PM, Harvey DJ, Truelove-Hill M, Sreepada LP, Davatzikos C, Lipton RB. Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia. Alzheimers Dement 2021; 17:1855-1867. [PMID: 34870371 DOI: 10.1002/alz.12491] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 07/15/2021] [Accepted: 09/07/2021] [Indexed: 01/18/2023]
Abstract
We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge-based cut-offs for each component of ATN and compared it to two data-driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data-driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia-like and normal groups. Both data-driven classification methods performed better than the empirical cut-offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data-driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.
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Affiliation(s)
- Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical center, Bronx, New York, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California-Davis, Davis, California, USA
| | - Monica Truelove-Hill
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lasya P Sreepada
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical center, Bronx, New York, USA.,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
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16
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Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
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17
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Ge XY, Cui K, Liu L, Qin Y, Cui J, Han HJ, Luo YH, Yu HM. Screening and predicting progression from high-risk mild cognitive impairment to Alzheimer's disease. Sci Rep 2021; 11:17558. [PMID: 34475445 PMCID: PMC8413294 DOI: 10.1038/s41598-021-96914-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 08/18/2021] [Indexed: 11/09/2022] Open
Abstract
Individuals with mild cognitive impairment (MCI) are clinically heterogeneous, with different risks of progression to Alzheimer's disease. Regular follow-up and examination may be time-consuming and costly, especially for MRI and PET. Therefore, it is necessary to identify a more precise MRI population. In this study, a two-stage screening frame was proposed for evaluating the predictive utility of additional MRI measurements among high-risk MCI subjects. In the first stage, the K-means cluster was performed for trajectory-template based on two clinical assessments. In the second stage, high-risk individuals were filtered out and imputed into prognosis models with varying strategies. As a result, the ADAS-13 was more sensitive for filtering out high-risk individuals among patients with MCI. The optimal model included a change rate of clinical assessments and three neuroimaging measurements and was significantly associated with a net reclassification improvement (NRI) of 0.246 (95% CI 0.021, 0.848) and integrated discrimination improvement (IDI) of 0.090 (95% CI - 0.062, 0.170). The ADAS-13 longitudinal models had the best discrimination performance (Optimism-corrected concordance index = 0.830), as validated by the bootstrap method. Considering the limited medical and financial resources, our findings recommend follow-up MRI examination 1 year after identification for high-risk individuals, while regular clinical assessments for low-risk individuals.
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Affiliation(s)
- Xiao-Yan Ge
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China
| | - Kai Cui
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Hong-Juan Han
- Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China
| | - Yan-Hong Luo
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Hong-Mei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, 56 XinJian South Road, Taiyuan, China.
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18
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Vélez JI, Samper LA, Arcos-Holzinger M, Espinosa LG, Isaza-Ruget MA, Lopera F, Arcos-Burgos M. A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease. Diagnostics (Basel) 2021; 11:887. [PMID: 34067584 PMCID: PMC8156402 DOI: 10.3390/diagnostics11050887] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.
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Affiliation(s)
- Jorge I. Vélez
- Department of Industrial Engineering, Universidad del Norte, Barranquilla 081007, Colombia
| | - Luiggi A. Samper
- Department of Public Health, Universidad del Norte, Barranquilla 081007, Colombia;
| | - Mauricio Arcos-Holzinger
- Grupo de Investigación en Psiquiatría (GIPSI), Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Lady G. Espinosa
- INPAC Research Group, Fundación Universitaria Sanitas, Bogotá 111321, Colombia; (L.G.E.); (M.A.I.-R.)
| | - Mario A. Isaza-Ruget
- INPAC Research Group, Fundación Universitaria Sanitas, Bogotá 111321, Colombia; (L.G.E.); (M.A.I.-R.)
| | - Francisco Lopera
- Neuroscience Research Group, University of Antioquia, Medellín 050010, Colombia;
| | - Mauricio Arcos-Burgos
- Grupo de Investigación en Psiquiatría (GIPSI), Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia;
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19
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Ezzati A, Harvey DJ, Habeck C, Golzar A, Qureshi IA, Zammit AR, Hyun J, Truelove-Hill M, Hall CB, Davatzikos C, Lipton RB. Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques. J Alzheimers Dis 2021; 73:1211-1219. [PMID: 31884486 DOI: 10.3233/jad-191038] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. OBJECTIVE The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. METHODS We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. RESULTS The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92. CONCLUSIONS Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.
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Affiliation(s)
- Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California-Davis, Davis, CA, USA
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | | | - Irfan A Qureshi
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Biohaven Pharmaceuticals, New Haven, CT, USA
| | - Andrea R Zammit
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jinshil Hyun
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | | | | | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
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20
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Beacher FD, Mujica-parodi LR, Gupta S, Ancora LA. Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer. Algorithms 2021; 14:147. [DOI: 10.3390/a14050147] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.
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Abstract
BACKGROUND The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). One strategy to boost the power of trials is to enroll individuals who are more likely to progress targeted using data-driven predictive models. OBJECTIVE To investigate if machine learning (ML) models can effectively predict clinical disease progression (cognitive decline) in mild-to-moderate AD patients during the timeframe of a phase III clinical trial. METHODS Data from 202 participants with a diagnosis of AD at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to train ML classifiers that can differentiate between individuals who had declining cognitive function (DC) and individuals with stable cognitive function (SC). DC was defined as any downward change in the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog) score over 12 months of follow-up. SC was defined by the absence of decline in ADAS-cog. Trained models were applied to data from 77 participants from the placebo arm of the phase III trial of Semagacestat (LFAN study) to identify subgroups of SC versus DC. RESULTS Only 74.8% of ADNI participants and 63.6% of LFAN participants had cognitive decline after one year of follow up. K-nearest neighbors (kNN) classifier had an accuracy of 68.3%, sensitivity of 80.1%, and specificity of 33.3% for identifying decliners in ADNI (training sample). In LFAN (validation sample), the model showed an overall accuracy of 61.3%, sensitivity of 65.5%, and specificity of 47.0% in identifying decliners at the 12 months of follow-up. The model had a positive predictive value of 80.8%, which was 17.2% more than the base prevalence of decliners. CONCLUSIONS Machine learning predictive models can be effectively used to boost the power of clinical trials by reducing the sample size.
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Affiliation(s)
- Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Richard B. Lipton
- Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
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