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Park C, Joo G, Roh M, Shin S, Yum S, Yeo NY, Park SW, Jang JW, Im H. Predicting the Progression of Mild Cognitive Impairment to Alzheimer's Dementia Using Recurrent Neural Networks With a Series of Neuropsychological Tests. J Clin Neurol 2024; 20:478-486. [PMID: 39227330 PMCID: PMC11372213 DOI: 10.3988/jcn.2023.0289] [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: 07/31/2023] [Revised: 04/30/2024] [Accepted: 05/24/2024] [Indexed: 09/05/2024] Open
Abstract
BACKGROUND AND PURPOSE The prevalence of Alzheimer's dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. METHODS Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer's Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. RESULTS The experimental results confirmed that the Preclinical Alzheimer's Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916. CONCLUSIONS Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.
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Affiliation(s)
- Chaeyoon Park
- Graduate School of Data Science, Kangwon National University, Chuncheon, Korea
| | - Gihun Joo
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Minji Roh
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Seunghun Shin
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Sujin Yum
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Korea
| | - Na Young Yeo
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Korea
| | - Sang Won Park
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Korea
- Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Jae-Won Jang
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Korea
- Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea
- Department of Convergence Security, Kangwon National University, Chuncheon, Korea.
| | - Hyeonseung Im
- Graduate School of Data Science, Kangwon National University, Chuncheon, Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
- Department of Convergence Security, Kangwon National University, Chuncheon, Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Korea.
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Bestetti A, Zangheri B, Gabanelli SV, Parini V, Fornara C. Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis. Nucl Med Commun 2024; 45:642-649. [PMID: 38632972 PMCID: PMC11149941 DOI: 10.1097/mnm.0000000000001853] [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: 02/03/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls. METHODS 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomics analysis was performed on the whole brain after normalization to an optimized template. After selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, the features obtained were added to a neural network model to find the accuracy in classifying HC and demented patients. Forty subjects not included in the training were used to test the models. The results of the three models (radiomics, 3D-CNN, combined model) were compared with each other. RESULTS Four radiomics features were selected. The sensitivity was 100% for the three models, but the specificity was higher with radiomics and combined one (100% vs. 85%). Moreover, the classification scores were significantly higher using the combined model in both normal and demented subjects. CONCLUSION The combination of radiomics features and 3D-CNN in a single model, applied to the whole brain 18FDG PET study, increases the accuracy in demented patients.
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Affiliation(s)
- Alberto Bestetti
- Department of Clinical and Community Sciences, State University of Milan, Milan
- Nuclear Medicine Department, MultiMedica Hospital
| | | | | | | | - Carla Fornara
- Division of Neurology, MultiMedica Hospital, Sesto San Giovanni, Italy
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Das SR, Ilesanmi A, Wolk DA, Gee JC. Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ? Magn Reson Med Sci 2024; 23:367-376. [PMID: 38880615 PMCID: PMC11234947 DOI: 10.2463/mrms.rev.2024-0053] [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: 04/28/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.
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Affiliation(s)
- Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ademola Ilesanmi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - James C. Gee
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Yin TT, Cao MH, Yu JC, Shi TY, Mao XH, Wei XY, Jia ZZ. T1-Weighted Imaging-Based Hippocampal Radiomics in the Diagnosis of Alzheimer's Disease. Acad Radiol 2024:S1076-6332(24)00370-2. [PMID: 38902110 DOI: 10.1016/j.acra.2024.06.012] [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: 05/08/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate the potential of T1-weighted imaging (T1WI)-based hippocampal radiomics as imaging markers for the diagnosis of Alzheimer's disease (AD) and their efficacy in discriminating between mild cognitive impairment (MCI) and dementia in AD. METHODS A total of 126 AD patients underwent T1WI-based magnetic resonance imaging (MRI) examinations, along with 108 age-sex-matched healthy controls (HC). This was a retrospective, single-center study conducted from November 2021 to February 2023. AD patients were categorized into two groups based on disease progression and cognitive function: AD-MCI and dementia (AD-D). T1WI-based radiomics features of the bilateral hippocampi were extracted. To diagnose AD and differentiate between AD-MCI and AD-D, predictive models were developed using random forest (RF), logistic regression (LR), and support vector machine (SVM). We compared radiomics features between the AD and HC groups, as well as within the subgroups of AD-MCI and AD-D. Area under the curve (AUC), accuracy, sensitivity, and specificity were all used to assess model performance. Furthermore, correlations between radiomics features and Mini-Mental State Examination (MMSE) scores, tau protein phosphorylated at threonine 181 (P-tau-181), and amyloid β peptide1-42 (Aβ1-42) were analyzed. RESULTS The RF model demonstrated superior performance in distinguishing AD from HC (AUC=0.961, accuracy=90.8%, sensitivity=90.7%, specificity=90.9%) and in identifying AD-MCI and AD-D (AUC=0.875, accuracy=80.7%, sensitivity=87.2%, specificity=73.2%) compared to the other models. Additionally, radiomics features were correlated with MMSE scores, P-tau-181, and Aβ1-42 levels in AD. CONCLUSION T1WI-based hippocampal radiomics features are valuable for diagnosing AD and identifying AD-MCI and AD-D.
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Affiliation(s)
- Ting Ting Yin
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Mao Hong Cao
- Department of Neurology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (M.H.C.)
| | - Jun Cheng Yu
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Ting Yan Shi
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Xiao Han Mao
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Xin Yue Wei
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Zhong Zheng Jia
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.).
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Bu S, Pang H, Li X, Zhao M, Wang J, Liu Y, Yu H. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy. BMC Med Imaging 2023; 23:204. [PMID: 38066432 PMCID: PMC10709839 DOI: 10.1186/s12880-023-01169-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. RESULTS The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. CONCLUSIONS Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.
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Affiliation(s)
- Shuting Bu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Huize Pang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Xiaolu Li
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Mengwan Zhao
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Juzhou Wang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Yu Liu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hongmei Yu
- Department of Neurology, the First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, Liaoning, 110001, PR China.
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Lohmann P, Bundschuh RA, Miederer I, Mottaghy FM, Langen KJ, Galldiks N. Clinical Applications of Radiomics in Nuclear Medicine. Nuklearmedizin 2023; 62:354-360. [PMID: 37935406 DOI: 10.1055/a-2191-3271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.
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Affiliation(s)
- Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3/-4), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Isabelle Miederer
- Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix M Mottaghy
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Karl Josef Langen
- Institute of Neuroscience and Medicine (INM-3/-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Norbert Galldiks
- Faculty of Medicine and University Hospital Cologne, Department of Neurology, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3/-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
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Qu Y, Wang P, Yao H, Wang D, Song C, Yang H, Zhang Z, Chen P, Kang X, Du K, Fan L, Zhou B, Han T, Yu C, Zhang X, Zuo N, Jiang T, Zhou Y, Liu B, Han Y, Lu J, Liu Y. Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease. Neurosci Bull 2023; 39:1533-1543. [PMID: 37014553 PMCID: PMC10533766 DOI: 10.1007/s12264-023-01041-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/29/2022] [Indexed: 04/05/2023] Open
Abstract
Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
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Affiliation(s)
- Yida Qu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Hongxiang Yao
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Dawei Wang
- Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572022, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaopeng Kang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Du
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Bing Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100091, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Beijing Institute of Geriatrics, Beijing, 100053, China
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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Bevilacqua R, Barbarossa F, Fantechi L, Fornarelli D, Paci E, Bolognini S, Giammarchi C, Lattanzio F, Paciaroni L, Riccardi GR, Pelliccioni G, Biscetti L, Maranesi E. Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer's Disease: A Systematic Review of Studies in the Field. J Clin Med 2023; 12:5432. [PMID: 37629474 PMCID: PMC10455452 DOI: 10.3390/jcm12165432] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/17/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023] Open
Abstract
The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer's disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer's disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer's disease patients-both at the dementia and pre-dementia phases of the disease-from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer's disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer's.
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Affiliation(s)
- Roberta Bevilacqua
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Federico Barbarossa
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Lorenzo Fantechi
- Unit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, Italy; (L.F.); (D.F.)
| | - Daniela Fornarelli
- Unit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, Italy; (L.F.); (D.F.)
| | - Enrico Paci
- Unit of Radiology, IRCCS INRCA, 60127 Ancona, Italy;
| | - Silvia Bolognini
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Cinzia Giammarchi
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Fabrizia Lattanzio
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Lucia Paciaroni
- Unit of Neurology, IRCCS INRCA, 60127 Ancona, Italy; (L.P.); (G.P.)
| | | | | | | | - Elvira Maranesi
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
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Zhang Y, Li H, Zheng Q. A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer's disease: deep learning analysis in 3238 participants worldwide. Eur Radiol 2023; 33:5385-5397. [PMID: 36892643 DOI: 10.1007/s00330-023-09519-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 12/19/2022] [Accepted: 02/02/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES Hippocampal characterization is one of the most significant hallmarks of Alzheimer's disease (AD); rather, the single-level feature is insufficient. A comprehensive hippocampal characterization is pivotal for developing a well-performing biomarker for AD. To verify whether a comprehensive characterization of hippocampal features of gray matter volume, segmentation probability, and radiomics features could better distinguish AD from normal control (NC), and to investigate whether the classification decision score could serve as a robust and individualized brain signature. METHODS A total of 3238 participants' structural MRI from four independent databases were employed to conduct a 3D residual attention network (3DRA-Net) to classify NC, mild cognitive impairment (MCI), and AD. The generalization was validated under inter-database cross-validation. The neurobiological basis of the classification decision score as a neuroimaging biomarker was systematically investigated by association with clinical profiles, as well as longitudinal trajectory analysis to reveal AD progression. All image analyses were performed only upon the single modality of T1-weighted MRI. RESULTS Our study exhibited an outstanding performance (ACC = 91.6%, AUC = 0.95) of the comprehensive characterization of hippocampal features in distinguishing AD (n = 282) from NC (n = 603) in Alzheimer's Disease Neuroimaging Initiative cohort, and ACC = 89.2% and AUC = 0.93 under external validation. More importantly, the constructed score was significantly correlated with clinical profiles (p < 0.05), and dynamically altered over the AD longitudinal progression, provided compelling evidence of a solid neurobiological basis. CONCLUSIONS This systemic study highlights the potential of the comprehensive characterization of hippocampal features to provide an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD. KEY POINTS • The comprehensive characterization of hippocampal features exhibited ACC = 91.6% (AUC = 0.95) in classifying AD from NC under intra-database cross-validation, and ACC = 89.2% (AUC = 0.93) in external validation. • The constructed classification score was significantly associated with clinical profiles, and dynamically altered over the AD longitudinal progression, which highlighted its potential of being an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.
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Affiliation(s)
- Yiyu Zhang
- School of Computer and Control Engineering, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai City, 264005, Shandong Province, China
| | - Hongming Li
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai City, 264005, Shandong Province, China.
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10
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Aghakhanyan G, Di Salle G, Fanni SC, Francischello R, Cioni D, Cosottini M, Volterrani D, Neri E. Radiomics insight into the neurodegenerative " hot" brain: A narrative review from the nuclear medicine perspective. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2023; 3:1143256. [PMID: 39355054 PMCID: PMC11440921 DOI: 10.3389/fnume.2023.1143256] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/10/2023] [Indexed: 10/03/2024]
Abstract
The application of radiomics for non-oncologic diseases is currently emerging. Despite its relative infancy state, the evidence highlights the potential of radiomics approaches to serve as neuroimaging biomarkers in the field of the neurodegenerative brain. This systematic review presents the last progress and potential application of radiomics in the field of neurodegenerative nuclear imaging applied to positron-emission tomography (PET) and single-photon emission computed tomography (SPECT) by focusing mainly on the two most common neurodegenerative disorders, Alzheimer's (AD) and Parkinson's disease (PD). A comprehensive review of the current literature was performed using the PubMed and Web of Science databases up to November 2022. The final collection of eighteen relevant publications was grouped as AD-related and PD-related. The main efforts in the field of AD dealt with radiomics-based early diagnosis of preclinical AD and the prediction of MCI to AD conversion, meanwhile, in the setting of PD, the radiomics techniques have been used in the attempt to improve the assessment of PD diagnosis, the differential diagnosis between PD and other parkinsonism, severity assessment, and outcome prediction. Although limited evidence with relatively small cohort studies, it seems that radiomics-based analysis using nuclear medicine tools, mainly [18F]Fluorodeoxyglucose (FDG) and β-amyloid (Aβ) PET, and dopamine transporter (DAT) SPECT, can be used for computer-aided diagnoses in AD-continuum and parkinsonian disorders. Combining nuclear radiomics analysis with clinical factors and introducing a multimodality approach can significantly improve classification and prediction efficiency in neurodegenerative disorders.
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Affiliation(s)
- Gayane Aghakhanyan
- Academic Radiology, Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, Pisa, Italy
| | - Gianfranco Di Salle
- Academic Radiology, Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, Pisa, Italy
| | - Salvatore Claudio Fanni
- Academic Radiology, Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, Pisa, Italy
| | - Roberto Francischello
- Academic Radiology, Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, Pisa, Italy
| | - Dania Cioni
- Academic Radiology, Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, Pisa, Italy
| | - Mirco Cosottini
- Neuroradiology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Duccio Volterrani
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, Pisa, Italy
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11
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Sensi SL, Russo M, Tiraboschi P. Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:187-218. [PMID: 36796942 DOI: 10.1016/b978-0-323-85538-9.00015-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.
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Affiliation(s)
- Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | - Mirella Russo
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Pietro Tiraboschi
- Division of Neurology V-Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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12
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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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13
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Zhu F, Xiao Y, Tao B, Gao Z, Gao X, Zhao Q, Zhang Q, Tang B, Zhang X, Zhao Y, Bishop JR, Sweeney JA, Lui S. Radiomic features of gray matter in never-treated first-episode schizophrenia. Cereb Cortex 2022; 33:5957-5967. [PMID: 36513368 DOI: 10.1093/cercor/bhac474] [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: 08/12/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 12/15/2022] Open
Abstract
Alterations of radiomic features (RFs) in gray matter are observed in schizophrenia, of which the results may be limited by small study samples and confounding effects of drug therapies. We tested for RFs alterations of gray matter in never-treated first-episode schizophrenia (NT-FES) patients and examined their associations with known gene expression profiles. RFs were examined in the first sample with 197 NT-FES and 178 healthy controls (HCs) and validated in the second independent sample (90 NT-FES and 74 HCs). One-year follow-up data were available from 87 patients to determine whether RFs were associated with treatment outcomes. Associations between identified RFs in NT-FES and gene expression profiles were evaluated. NT-FES exhibited alterations of 30 RFs, with the greatest involvement of microstructural heterogeneity followed by measures of brain region shape. The identified RFs were mainly located in the central executive network, frontal-temporal network, and limbic system. Two baseline RFs with the involvement of microstructural heterogeneity predicted treatment response with moderate accuracy (78% for the first sample, 70% for the second sample). Exploratory analyses indicated that RF alterations were spatially related to the expression of schizophrenia risk genes. In summary, the present findings link brain abnormalities in schizophrenia with molecular features and treatment response.
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Affiliation(s)
- Fei Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Bo Tao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Ziyang Gao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Xin Gao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Qiannan Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Qi Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Biqiu Tang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | | | - Yu Zhao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
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14
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Zhao K, Zheng Q, Dyrba M, Rittman T, Li A, Che T, Chen P, Sun Y, Kang X, Li Q, Liu B, Liu Y, Li S. Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104538. [PMID: 35098696 PMCID: PMC9036024 DOI: 10.1002/advs.202104538] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/30/2021] [Indexed: 05/28/2023]
Abstract
Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.
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Affiliation(s)
- Kun Zhao
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
| | - Qiang Zheng
- School of Computer and Control EngineeringYantai UniversityYantai264005China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE)Rostock18147Germany
| | - Timothy Rittman
- Department of Clinical NeurosciencesUniversity of CambridgeCambridge Biomedical CampusCambridgeCB2 0SZUK
| | - Ang Li
- State Key Laboratory of Brain and Cognitive Science, Institute of BiophysicsChinese Academy of SciencesBeijing100101China
| | - Tongtong Che
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Yuqing Sun
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Qiongling Li
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Yong Liu
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Shuyu Li
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
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15
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Wu J, Zhao K, Li Z, Wang D, Ding Y, Wei Y, Zhang H, Liu Y. A systematic analysis of diagnostic performance for Alzheimer's disease using structural MRI. PSYCHORADIOLOGY 2022; 2:287-295. [PMID: 38665142 PMCID: PMC10939341 DOI: 10.1093/psyrad/kkac001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/13/2022] [Accepted: 02/14/2022] [Indexed: 04/28/2024]
Abstract
Background Alzheimer's disease (AD) is one of the most common neurodegenerative disorders in the elderly. Although numerous structural magnetic resonance imaging (sMRI) studies have reported diagnostic models that could distinguish AD from normal controls (NCs) with 80-95% accuracy, limited efforts have been made regarding the clinically practical computer-aided diagnosis (CAD) system for AD. Objective To explore the potential factors that hinder the clinical translation of the AD-related diagnostic models based on sMRI. Methods To systematically review the diagnostic models for AD based on sMRI, we identified relevant studies published in the past 15 years on PubMed, Web of Science, Scopus, and Ovid. To evaluate the heterogeneity and publication bias among those studies, we performed subgroup analysis, meta-regression, Begg's test, and Egger's test. Results According to our screening criterion, 101 studies were included. Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies, accompanied by significant heterogeneity. Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI, which included but was not limited to the following aspects: (i) different datasets; (ii) different machine learning models, e.g. traditional machine learning or deep learning model; (iii) different cross-validation methods, e.g. k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation, but both overestimate the accuracy when compared to validation in independent samples; (iv) different sample sizes; and (v) the publication times. We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD. Conclusions Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI. However, considering the many factors hindering clinical radiology practice, there would still be a long way to go to improve.
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Affiliation(s)
- Jiangping Wu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Kun Zhao
- Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhuangzhuang Li
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Dong Wang
- School of Information Science and Engineering, Shandong Normal University, Ji'nan, 250014, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Ji'nan, 250014, China
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Han Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
- Center for Artificial Intelligence in Medical Imaging, Beijing University of Posts and Telecommunications, Beijing, 100876, China
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16
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Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife 2021; 10:e72129. [PMID: 34783653 PMCID: PMC8660024 DOI: 10.7554/elife.72129] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022] Open
Abstract
Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.
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Affiliation(s)
- Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Aurina Arnatkeviciute
- School of Psychological Sciences & Monash Biomedical Imaging, Monash UniversityClaytonAustralia
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Ben D Fulcher
- School of Physics, University of SydneySydneyAustralia
| | - Alex Fornito
- School of Psychological Sciences & Monash Biomedical Imaging, Monash UniversityClaytonAustralia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
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17
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Zhao K, Zheng Q, Che T, Dyrba M, Li Q, Ding Y, Zheng Y, Liu Y, Li S. Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis. Netw Neurosci 2021; 5:783-797. [PMID: 34746627 PMCID: PMC8567836 DOI: 10.1162/netn_a_00200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/08/2021] [Indexed: 12/13/2022] Open
Abstract
A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.
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Affiliation(s)
- Kun Zhao
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, China
| | - Tongtong Che
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Qiongling Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
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