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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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Wijaya A, Setiawan NA, Ahmad AH, Zakaria R, Othman Z. Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA). AIMS Neurosci 2023; 10:154-171. [PMID: 37426780 PMCID: PMC10323261 DOI: 10.3934/neuroscience.2023012] [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: 04/17/2023] [Revised: 05/27/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.
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Affiliation(s)
- Adi Wijaya
- Department of Health Information Management, Universitas Indonesia Maju, Jakarta, Indonesia
| | - Noor Akhmad Setiawan
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Asma Hayati Ahmad
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Rahimah Zakaria
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Zahiruddin Othman
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
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Cai C, Lv W, Chi F, Zhang B, Zhu L, Yang G, Zhao S, Zhu Y, Han X, Dai Z, Wang X, Lu L. Prognostic generalization of multi-level CT-dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma. Med Phys 2023; 50:922-934. [PMID: 36317870 DOI: 10.1002/mp.16044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 09/13/2022] [Accepted: 09/24/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE To investigate the prognostic performance of multi-level computed tomography (CT)-dose fusion dosiomics at the image-, matrix-, and feature-levels from the gross tumor volume (GTV) at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients. METHODS Two hundred and nineteen NPC patients (175 vs. 44 for training vs. internal validation) were used to train prediction model, and 32 NPC patients were used for external validation. We first extracted CT and dose information from intratumoral nasopharynx (GTV_nx) and lymph node (GTV_nd) regions. Then, the corresponding peritumoral regions (RING_3 mm and RING_5 mm) were also considered. Thus, the individual and combination of intratumoral and peritumoral regions were as follows: GTV_nx, GTV_nd, RING_3 mm_nx, RING_3 mm_nd, RING_5 mm_nx, RING_5 mm_nd, GTV_nxnd, RING_3 mm_nxnd, RING_5 mm_nxnd, GTV + RING_3 mm_nxnd, and GTV + RING_5 mm_nxnd. For each region, 11 models were built by combining five clinical parameters and 127 features from: (1) dose images alone; (2-7) fused dose and CT images via wavelet-based fusion using CT weights of 0.2, 0.4, 0.6, and 0.8, gradient transfer fusion, and guided-filtering-based fusion (GFF); (8) fused matrices (sumMat); (9-10) fused features derived via feature averaging (avgFea) and feature concatenation (conFea); and finally, (11) CT images alone. The concordance index (C-index) and Kaplan-Meier curves with log-rank test were used to assess model performance. RESULTS The fusion models' performance was better than single CT/dose model on both internal and external validation. Models that combined the information from both GTV_nx and GTV_nd regions outperformed the single region model. For internal validation, GTV + RING_3 mm_nxnd GFF model achieved the highest C-index both in recurrence-free survival (RFS) and metastasis-free survival (MFS) predictions (RFS: 0.822; MFS: 0.786). The highest C-index in external validation set was achieved by RING_3 mm_nxnd model (RFS: 0.762; MFS: 0.719). The GTV + RING_3 mm_nxnd GFF model is able to significantly separate patients into high-risk and low-risk groups compared to dose-only or CT-only models. CONCLUSION Fusion dosiomics model combining the primary tumor, the involved lymph node, and 3 mm peritumoral information outperformed single-modality models for different outcome predictions, which is helpful for clinical decision-making and the development of personalized treatment.
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Affiliation(s)
- Chunya Cai
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Wenbing Lv
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Feng Chi
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Bailin Zhang
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lin Zhu
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Geng Yang
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Shiwu Zhao
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yuanhu Zhu
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xu Han
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
| | - Zhenhui Dai
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xuetao Wang
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
- Pazhou Lab, Guangzhou, China
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Li L, Yu X, Sheng C, Jiang X, Zhang Q, Han Y, Jiang J. A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives. Transl Neurodegener 2022; 11:42. [PMID: 36109823 PMCID: PMC9476275 DOI: 10.1186/s40035-022-00315-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.
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Ma H, Zhang D, Sun D, Wang H, Yang J. Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis. BMC Med Imaging 2022; 22:164. [PMID: 36096776 PMCID: PMC9465920 DOI: 10.1186/s12880-022-00892-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 09/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Radiomics is an emerging image analysis framework that provides more details than conventional methods. In present study, we aimed to identify structural radiomics features of gray matter (GM) and white matter (WM), and to develop and validate the classification model for major depressive disorder (MDD) and subthreshold depression (StD) diagnosis using radiomics analysis. METHODS A consecutive cohort of 142 adolescents and young adults, including 43 cases with MDD, 49 cases with StD and 50 healthy controls (HC), were recruited and underwent the three-dimensional T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI). We extracted radiomics features representing the shape and diffusion properties of GM and WM from all participants. Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features. RESULTS The results showed that a total of 3030 features were extracted after preprocessing, including 2262 shape-related features from each T1-weighted image representing GM morphometry and 768 features from each DTI representing the diffusion properties of WM. 25 features were selected ultimately, including ten features for MDD versus HC, eight features for StD versus HC, and seven features for MDD versus StD. The accuracies and area under curve (AUC) the RFC achieved were 86.75%, 0.93 for distinguishing MDD from HC with significant radiomics features located in the left medial orbitofrontal cortex, right superior and middle temporal regions, right anterior cingulate, left cuneus and hippocampus, 70.51%, 0.69 for discriminating StD from HC within left cuneus, medial orbitofrontal cortex, cerebellar vermis, hippocampus, anterior cingulate and amygdala, right superior and middle temporal regions, and 59.15%, 0.66 for differentiating MDD from StD within left medial orbitofrontal cortex, middle temporal and cuneus, right superior frontal, superior temporal regions and hippocampus, anterior cingulate, respectively. CONCLUSION These findings provide preliminary evidence that radiomics features of brain structure are valid for discriminating MDD and StD subjects from healthy controls. The MRI-based radiomics approach, with further improvement and validation, might be a potential facilitating method to clinical diagnosis of MDD or StD.
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Affiliation(s)
- Huan Ma
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, 374# DianMian Road, 650101, Kunming, China
| | - Dafu Zhang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, 650018, Kunming, China
| | - Dewei Sun
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Hongbo Wang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Jianzhong Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, 374# DianMian Road, 650101, Kunming, China.
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Zhao Y, Zhang J, Chen Y, Jiang J. A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI. Brain Sci 2022; 12:1067. [PMID: 36009130 PMCID: PMC9406185 DOI: 10.3390/brainsci12081067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans. METHODS In this study, we selected tau-PET scans from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments. RESULTS Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD. CONCLUSIONS Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.
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Affiliation(s)
- Yan Zhao
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, China
- Department of Nuclear Medicine, Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
- Institute of Nuclear Medicine, Southwest Medical University, Luzhou 646000, China
- School of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Jieming Zhang
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Yue Chen
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, China
- Department of Nuclear Medicine, Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
- Institute of Nuclear Medicine, Southwest Medical University, Luzhou 646000, China
- School of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Jiehui Jiang
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, China
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai 200444, China
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Yang F, Jiang J, Alberts I, Wang M, Li T, Sun X, Rominger A, Zuo C, Shi K. Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer's disease: an exploratory radiomic analysis study. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:513. [PMID: 35928737 PMCID: PMC9347042 DOI: 10.21037/atm-21-4349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 12/19/2021] [Indexed: 11/28/2022]
Abstract
Background This study aimed to explore the potential of a combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and magnetic resonance imaging (MRI) to improve predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). The predictive performances and specific associated biomarkers of these imaging techniques used alone (single-modality imaging) and in combination (dual-modality imaging) were compared. Methods This study enrolled 377 patients with MCI and 94 healthy control participants from 2 medical centers. Enrolment was based on the patients' brain MRI and PET images. Radiomic analysis was performed to evaluate the predictive performance of dual-modality 18F-FDG PET and MRI scans. Regions of interest (ROIs) were determined using an a priori brain atlas. Radiomic features in these ROIs were extracted from the MRI and 18F-FDG PET scan data. These features were either concatenated or used separately to select features and construct Cox regression models for prediction in each modality. Harrell's concordance index (C-index) was then used to assess the predictive accuracies of the resulting models, and correlations between the MRI and 18F-FDG PET features were evaluated. Results The C-indices for the two test datasets were 0.77 and 0.80 for dual-modality 18F-FDG PET/MRI, 0.75 and 0.73 for single-modality 18F-FDG PET, and 0.74 and 0.76 for single-modality MRI. In addition, there was a significant correlation between the crucial image signatures of the different modalities. Conclusions These results indicate the value of imaging features in monitoring the progress of MCI in populations at high risk of developing AD. However, the incremental benefit of combining 18F-FDG PET and MRI is limited, and radiomic analysis of a single modality may yield acceptable predictive results.
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Affiliation(s)
- Fan Yang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Min Wang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Taoran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaoming Sun
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
- Department of Informatics, Technische Universität München, Munich, Germany
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Jiang J, Zhang J, Li Z, Li L, Huang B. Using Deep Learning Radiomics to Distinguish Cognitively Normal Adults at Risk of Alzheimer's Disease From Normal Control: An Exploratory Study Based on Structural MRI. Front Med (Lausanne) 2022; 9:894726. [PMID: 35530047 PMCID: PMC9070098 DOI: 10.3389/fmed.2022.894726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives We proposed a novel deep learning radiomics (DLR) method to distinguish cognitively normal adults at risk of Alzheimer's disease (AD) from normal control based on T1-weighted structural MRI images. Methods In this study, we selected MRI data from the Alzheimer's Disease Neuroimaging Initiative Database (ADNI), which included 417 cognitively normal adults. These subjects were divided into 181 individuals at risk of Alzheimer's disease (preAD group) and 236 normal control individuals (NC group) according to standard uptake ratio >1.18 calculated by amyloid Positron Emission Tomography (PET). We further divided the preaAD group into APOE+ and APOE- subgroups according to whether APOE ε4 was positive or not. All data sets were divided into one training/validation group and one independent test group. The proposed DLR method included three steps: (1) the pre-training of basic deep learning (DL) models, (2) the extraction, selection and fusion of DLR features, and (3) classification. The support vector machine (SVM) was used as the classifier. In the comparative experiments, we compared our proposed DLR method with three existing models: hippocampal model, clinical model, and traditional radiomics model. Ten-fold cross-validation was performed with 100 time repetitions. Results The DLR method achieved the best classification performance between preAD and NC than other models with an accuracy of 89.85% ± 1.12%. In comparison, the accuracies of the other three models were 72.44% ± 1.37%, 82.00% ± 4.09% and 79.65% ± 2.21%. In addition, the DLR model also showed the best classification performance (85.45% ± 9.04% and 92.80% ± 2.61%) in the subgroup experiment. Conclusion The results showed that the DLR method provided a potentially clinical value to distinguish preAD from NC.
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Affiliation(s)
- Jiehui Jiang
- Department of Radiology, Gongli Hospital, School of Medicine, Shanghai University, Shanghai, China
- School of Life Sciences, Institute of Biomedical Engineering, Shanghai University, Shanghai, China
| | - Jieming Zhang
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Zhuoyuan Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Lanlan Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Bingcang Huang
- Department of Radiology, Gongli Hospital, School of Medicine, Shanghai University, Shanghai, China
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Alongi P, Laudicella R, Panasiti F, Stefano A, Comelli A, Giaccone P, Arnone A, Minutoli F, Quartuccio N, Cupidi C, Arnone G, Piccoli T, Grimaldi LME, Baldari S, Russo G. Radiomics Analysis of Brain [ 18F]FDG PET/CT to Predict Alzheimer's Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis. Diagnostics (Basel) 2022; 12:diagnostics12040933. [PMID: 35453981 PMCID: PMC9030037 DOI: 10.3390/diagnostics12040933] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Early in-vivo diagnosis of Alzheimer's disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosis of AD, also by comparing the results with following Amyloid-PET and final clinical diagnosis. METHODS From July 2016 to September 2017, 43 patients underwent PET/CT scans with FDG and Florbetaben brain PET/CT and at least 24 months of clinical/instrumental follow-up. Patients were retrospectively evaluated by a multidisciplinary team (MDT = Neurologist, Psychologist, Radiologist, Nuclear Medicine Physician, Laboratory Clinic) at the G. Giglio Institute in Cefalù, Italy. Starting from the cerebral segmentations applied by SPM on the main cortical macro-areas of each patient, Pyradiomics was used for the feature extraction process; subsequently, an innovative descriptive-inferential mixed sequential approach and a machine learning algorithm (i.e., discriminant analysis) were used to obtain the best diagnostic performance in prediction of amyloid deposition and the final diagnosis of AD. RESULTS A total of 11 radiomics features significantly predictive of cortical beta-amyloid deposition (n = 6) and AD (n = 5) were found. Among them, two higher-order features (original_glcm_Idmn and original_glcm_Id), extracted from the limbic enthorinal cortical area (ROI-1) in the FDG-PET/CT images, predicted the positivity of Amyloid-PET/CT scans with maximum values of sensitivity (SS), specificity (SP), precision (PR) and accuracy (AC) of 84.92%, 75.13%, 73.75%, and 79.56%, respectively. Conversely, for the prediction of the clinical-instrumental final diagnosis of AD, the best performance was obtained by two higher-order features (original_glcm_MCC and original_glcm_Maximum Probability) extracted from ROI-2 (frontal cortex) with a SS, SP, PR and AC of 75.16%, 80.50%, 77.68%, and 78.05%, respectively, and by one higher-order feature (original_glcm_Idmn) extracted from ROI-3 (medial Temporal cortex; SS = 80.88%, SP = 76.85%, PR = 75.63%, AC = 78.76%. CONCLUSIONS The results obtained in this preliminary study support advanced segmentation of cortical areas typically involved in early AD on FDG PET/CT brain images, and radiomics analysis for the identification of specific high-order features to predict Amyloid deposition and final diagnosis of AD.
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Affiliation(s)
- Pierpaolo Alongi
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (G.A.)
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, 90015 Cefalù, Italy;
- Correspondence:
| | - Riccardo Laudicella
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, 90015 Cefalù, Italy;
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging Nuclear Medicine Unit, University of Messina, 98122 Messina, Italy; (F.P.); (F.M.); (S.B.)
- Ri.Med Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.C.); (P.G.)
| | - Francesco Panasiti
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging Nuclear Medicine Unit, University of Messina, 98122 Messina, Italy; (F.P.); (F.M.); (S.B.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (A.S.); (G.R.)
| | - Albert Comelli
- Ri.Med Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.C.); (P.G.)
| | - Paolo Giaccone
- Ri.Med Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.C.); (P.G.)
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Annachiara Arnone
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Fabio Minutoli
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging Nuclear Medicine Unit, University of Messina, 98122 Messina, Italy; (F.P.); (F.M.); (S.B.)
| | - Natale Quartuccio
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (G.A.)
| | - Chiara Cupidi
- Neurology Unit, Fondazione Istituto G. Giglio, 90015 Cefalù, Italy; (C.C.); (L.M.E.G.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (G.A.)
| | - Tommaso Piccoli
- Unit of Neurology, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy;
| | | | - Sergio Baldari
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging Nuclear Medicine Unit, University of Messina, 98122 Messina, Italy; (F.P.); (F.M.); (S.B.)
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (A.S.); (G.R.)
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10
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Cui W, Yan C, Yan Z, Peng Y, Leng Y, Liu C, Chen S, Jiang X, Zheng J, Yang X. BMNet: A New Region-Based Metric Learning Method for Early Alzheimer's Disease Identification With FDG-PET Images. Front Neurosci 2022; 16:831533. [PMID: 35281501 PMCID: PMC8908419 DOI: 10.3389/fnins.2022.831533] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/11/2022] [Indexed: 12/21/2022] Open
Abstract
18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, existing FDG-PET-based researches are still insufficient for the identification of early MCI (EMCI) and late MCI (LMCI). Compared with methods based other modalities, current methods with FDG-PET are also inadequate in using the inter-region-based features for the diagnosis of early AD. Moreover, considering the variability in different individuals, some hard samples which are very similar with both two classes limit the classification performance. To tackle these problems, in this paper, we propose a novel bilinear pooling and metric learning network (BMNet), which can extract the inter-region representation features and distinguish hard samples by constructing the embedding space. To validate the proposed method, we collect 898 FDG-PET images from Alzheimer's disease neuroimaging initiative (ADNI) including 263 normal control (NC) patients, 290 EMCI patients, 147 LMCI patients, and 198 AD patients. Following the common preprocessing steps, 90 features are extracted from each FDG-PET image according to the automatic anatomical landmark (AAL) template and then sent into the proposed network. Extensive fivefold cross-validation experiments are performed for multiple two-class classifications. Experiments show that most metrics are improved after adding the bilinear pooling module and metric losses to the Baseline model respectively. Specifically, in the classification task between EMCI and LMCI, the specificity improves 6.38% after adding the triple metric loss, and the negative predictive value (NPV) improves 3.45% after using the bilinear pooling module. In addition, the accuracy of classification between EMCI and LMCI achieves 79.64% using imbalanced FDG-PET images, which illustrates that the proposed method yields a state-of-the-art result of the classification accuracy between EMCI and LMCI based on PET images.
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Affiliation(s)
- Wenju Cui
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Caiying Yan
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Yunsong Peng
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yilin Leng
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Chenlu Liu
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Shuangqing Chen
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Xi Jiang
- School of Life Sciences and Technology, The University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Zheng
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaodong Yang
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
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11
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Jiang J, Wang M, Alberts I, Sun X, Li T, Rominger A, Zuo C, Han Y, Shi K, Initiative FTADN. Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease. Eur J Nucl Med Mol Imaging 2022; 49:2163-2173. [PMID: 35032179 DOI: 10.1007/s00259-022-05687-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/11/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. METHOD FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. RESULTS The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. CONCLUSION The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.
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Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China.
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Xiaoming Sun
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Taoran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- Human Phenome Institute, Fudan University, Shanghai, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.
- School of Biomedical Engineering, Hainan University, Haikou, China.
- National Clinical Research Center for Geriatric Disorders, Beijing, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
- Department of Informatics, Technische Universität München, Munich, Germany
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12
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Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm. Clin Oncol (R Coll Radiol) 2021; 34:114-127. [PMID: 34872823 DOI: 10.1016/j.clon.2021.11.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/01/2021] [Accepted: 11/17/2021] [Indexed: 02/06/2023]
Abstract
AIMS Despite the promising results achieved by radiomics prognostic models for various clinical applications, multiple challenges still need to be addressed. The two main limitations of radiomics prognostic models include information limitation owing to single imaging modalities and the selection of optimum machine learning and feature selection methods for the considered modality and clinical outcome. In this work, we applied several feature selection and machine learning methods to single-modality positron emission tomography (PET) and computed tomography (CT) and multimodality PET/CT fusion to identify the best combinations for different radiomics modalities towards overall survival prediction in non-small cell lung cancer patients. MATERIALS AND METHODS A PET/CT dataset from The Cancer Imaging Archive, including subjects from two independent institutions (87 and 95 patients), was used in this study. Each cohort was used once as training and once as a test, followed by averaging of the results. ComBat harmonisation was used to address the centre effect. In our proposed radiomics framework, apart from single-modality PET and CT models, multimodality radiomics models were developed using multilevel (feature and image levels) fusion. Two different methods were considered for the feature-level strategy, including concatenating PET and CT features into a single feature set and alternatively averaging them. For image-level fusion, we used three different fusion methods, namely wavelet fusion, guided filtering-based fusion and latent low-rank representation fusion. In the proposed prognostic modelling framework, combinations of four feature selection and seven machine learning methods were applied to all radiomics modalities (two single and five multimodalities), machine learning hyper-parameters were optimised and finally the models were evaluated in the test cohort with 1000 repetitions via bootstrapping. Feature selection and machine learning methods were selected as popular techniques in the literature, supported by open source software in the public domain and their ability to cope with continuous time-to-event survival data. Multifactor ANOVA was used to carry out variability analysis and the proportion of total variance explained by radiomics modality, feature selection and machine learning methods was calculated by a bias-corrected effect size estimate known as ω2. RESULTS Optimum feature selection and machine learning methods differed owing to the applied radiomics modality. However, minimum depth (MD) as feature selection and Lasso and Elastic-Net regularized generalized linear model (glmnet) as machine learning method had the highest average results. Results from the ANOVA test indicated that the variability that each factor (radiomics modality, feature selection and machine learning methods) introduces to the performance of models is case specific, i.e. variances differ regarding different radiomics modalities and fusion strategies. Overall, the greatest proportion of variance was explained by machine learning, except for models in feature-level fusion strategy. CONCLUSION The identification of optimal feature selection and machine learning methods is a crucial step in developing sound and accurate radiomics risk models. Furthermore, optimum methods are case specific, differing due to the radiomics modality and fusion strategy used.
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13
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Zhou P, Zeng R, Yu L, Feng Y, Chen C, Li F, Liu Y, Huang Y, Huang Z. Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging. Front Aging Neurosci 2021; 13:764872. [PMID: 34764864 PMCID: PMC8576572 DOI: 10.3389/fnagi.2021.764872] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the older people. Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. To discriminate MCI patients at risk of AD from stable MCI, we propose a novel deep-learning radiomics (DLR) model based on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance. Methods: 18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging Initiative database (ADNI) were collected, including 168 patients with MCI who converted to AD within 3 years and 187 patients with MCI without conversion within 3 years. These subjects were randomly partitioned into 90 % for the training/validation group and 10 % for the independent test group. The proposed DLR approach consists of three steps: base DL model pre-training, network features extraction, and integration of DLR+C, where a convolution network serves as a feature encoder, and a support vector machine (SVM) operated as the classifier. In comparative experiments, we compared our DLR+C method with four other methods: the standard uptake value ratio (SUVR) method, Radiomics-ROI method, Clinical method, and SUVR + Clinical method. To guarantee the robustness, 10-fold cross-validation was processed 100 times. Results: Under the DLR model, our proposed DLR+C was advantageous and yielded the best classification performance in the diagnosis of conversion with the accuracy, sensitivity, and specificity of 90.62 ± 1.16, 87.50 ± 0.00, and 93.39 ± 2.19%, respectively. In contrast, the respective accuracy of the other four methods reached 68.38 ± 1.27, 73.31 ± 6.93, 81.09 ± 1.97, and 85.35 ± 0.72 %. These results suggested the DLR approach could be used successfully in the prediction of conversion to AD, and that our proposed DLR-combined clinical information was effective. Conclusions: This study showed DLR+C could provide a novel and valuable method for the computer-assisted diagnosis of conversion to AD from MCI. This DLR+C method provided a quantitative biomarker which could predict conversion to AD in MCI patients.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Zhongxiong Huang
- Department of PET-CT Center, Chenzhou No.1 People's Hospital, Chenzhou, China
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14
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Amini M, Nazari M, Shiri I, Hajianfar G, Deevband MR, Abdollahi H, Arabi H, Rahmim A, Zaidi H. Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma. Phys Med Biol 2021; 66. [PMID: 34544053 DOI: 10.1088/1361-6560/ac287d] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 09/20/2021] [Indexed: 12/23/2022]
Abstract
We developed multi-modality radiomic models by integrating information extracted from18F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. For feature-level fusion, radiomic features were extracted individually from CT and PET images and concatenated. Alternatively, radiomic features extracted separately from CT and PET images were averaged. For image-level fusion, wavelet fusion was utilized and tuned with two parameters, namely CT weight and Wavelet Band Pass Filtering Ratio. Clinical and combined clinical + radiomic models were developed. Gray level discretization was performed at 3 different levels (16, 32 and 64) and 225 radiomics features were extracted. Overall survival (OS) was considered as the endpoint. For feature reduction, correlated (redundant) features were excluded using Spearman's correlation, and best combination of top ten features with highest concordance-indices (via univariate Cox model) were selected in each model for further multivariate Cox model. Moreover, prognostic score's median, obtained from the training cohort, was used intact in the testing cohort as a threshold to classify patients into low- versus high-risk groups, and log-rank test was applied to assess differences between the Kaplan-Meier curves. Overall, while models based on feature-level fusion strategy showed limited superiority over single-modalities, image-level fusion strategy significantly outperformed both single-modality and feature-level fusion strategies. As such, the clinical model (C-index = 0.656) outperformed all models from single-modality and feature-level strategies, but was outperformed by certain models from image-level fusion strategy. Our findings indicated that image-level fusion multi-modality radiomics models outperformed single-modality, feature-level fusion, and clinical models for OS prediction of NSCLC patients.
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Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mohammad Reza Deevband
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, School of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver BC, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, CH-1211 Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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15
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Matsuoka T, Ueno D, Ismail Z, Rubinstein E, Uchida H, Mimura M, Narumoto J. Neural Correlates of Mild Behavioral Impairment: A Functional Brain Connectivity Study Using Resting-State Functional Magnetic Resonance Imaging. J Alzheimers Dis 2021; 83:1221-1231. [PMID: 34420972 PMCID: PMC8543254 DOI: 10.3233/jad-210628] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background: Mild behavioral impairment (MBI) is associated with accelerated cognitive decline and greater risk of dementia. However, the neural correlates of MBI have not been completely elucidated. Objective: The study aimed to investigate the correlation between cognitively normal participants and participants with amnestic mild cognitive impairment (aMCI) using resting-state functional magnetic resonance imaging. Methods: The study included 30 cognitively normal participants and 13 participants with aMCI (20 men and 23 women; mean age, 76.9 years). The MBI was assessed using the MBI checklist (MBI-C). Region of interest (ROI)-to-ROI analysis was performed to examine the correlation between MBI-C scores and functional connectivity (FC) of the default mode network, salience network, and frontoparietal control network (FPCN). Age, Mini-Mental State Examination score, sex, and education were used as covariates. A p-value of 0.05, with false discovery rate correction, was considered significant. Results: A negative correlation was observed between the MBI-C total score and FC of the left posterior parietal cortex with the right middle frontal gyrus. A similar result was obtained for the MBI-C affective dysregulation domain score. Conclusion: FPCN dysfunction was detected as a neural correlate of MBI, especially in the affective dysregulation domain. This dysfunction may be associated with cognitive impairment in MBI and conversion of MBI to dementia; however, further longitudinal data are needed to examine this relationship.
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Affiliation(s)
- Teruyuki Matsuoka
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto, Japan
| | - Daisuke Ueno
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto, Japan
| | - Zahinoor Ismail
- Departments of Psychiatry, Clinical Neurosciences, and Community Health Sciences, Hotchkiss Brain Institute and O'Brien Institute for Public Health, University of Calgary, Calgary, Canada
| | - Ellen Rubinstein
- Department of Sociology and Anthropology, North Dakota State University, Fargo, ND, USA
| | - Hiroyuki Uchida
- Department of Neuropsychiatry, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Jin Narumoto
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto, Japan
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16
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Lu P, Colliot O. Multilevel Survival Modeling with Structured Penalties for Disease Prediction from Imaging Genetics data. IEEE J Biomed Health Inform 2021; 26:798-808. [PMID: 34329174 DOI: 10.1109/jbhi.2021.3100918] [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: 11/06/2022]
Abstract
This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibiting mild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is in contrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a L2-penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer's disease (AD) among patients with mild cognitive impairment (MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in the prediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases.
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17
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Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
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18
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Feng Q, Ding Z. MRI Radiomics Classification and Prediction in Alzheimer's Disease and Mild Cognitive Impairment: A Review. Curr Alzheimer Res 2021; 17:297-309. [PMID: 32124697 DOI: 10.2174/1567205017666200303105016] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 02/03/2020] [Accepted: 03/01/2020] [Indexed: 01/18/2023]
Abstract
BACKGROUND Alzheimer's Disease (AD) is a progressive neurodegenerative disease that threatens the health of the elderly. Mild Cognitive Impairment (MCI) is considered to be the prodromal stage of AD. To date, AD or MCI diagnosis is established after irreversible brain structure alterations. Therefore, the development of new biomarkers is crucial to the early detection and treatment of this disease. At present, there exist some research studies showing that radiomics analysis can be a good diagnosis and classification method in AD and MCI. OBJECTIVE An extensive review of the literature was carried out to explore the application of radiomics analysis in the diagnosis and classification among AD patients, MCI patients, and Normal Controls (NCs). RESULTS Thirty completed MRI radiomics studies were finally selected for inclusion. The process of radiomics analysis usually includes the acquisition of image data, Region of Interest (ROI) segmentation, feature extracting, feature selection, and classification or prediction. From those radiomics methods, texture analysis occupied a large part. In addition, the extracted features include histogram, shapebased features, texture-based features, wavelet features, Gray Level Co-Occurrence Matrix (GLCM), and Run-Length Matrix (RLM). CONCLUSION Although radiomics analysis is already applied to AD and MCI diagnosis and classification, there still is a long way to go from these computer-aided diagnostic methods to the clinical application.
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Affiliation(s)
- Qi Feng
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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19
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Kim JP, Kim J, Jang H, Kim J, Kang SH, Kim JS, Lee J, Na DL, Kim HJ, Seo SW, Park H. Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach. Sci Rep 2021; 11:6954. [PMID: 33772041 PMCID: PMC7997887 DOI: 10.1038/s41598-021-86114-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 02/23/2021] [Indexed: 02/01/2023] Open
Abstract
Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.
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Affiliation(s)
- Jun Pyo Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jonghoon Kim
- grid.264381.a0000 0001 2181 989XDepartment of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Korea
| | - Hyemin Jang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jaeho Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.256753.00000 0004 0470 5964Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
| | - Sung Hoon Kang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Ji Sun Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jongmin Lee
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Duk L. Na
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon-si, Korea
| | - Hyunjin Park
- grid.410720.00000 0004 1784 4496Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea ,grid.264381.a0000 0001 2181 989XSchool of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon-si, Republic of Korea
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Qi Y, Xu M, Wang W, Wang YY, Liu JJ, Ren HX, Liu MM, Li RL, Li HJ. Early prediction of putamen imaging features in HIV-associated neurocognitive impairment syndrome. BMC Neurol 2021; 21:106. [PMID: 33750319 PMCID: PMC7941706 DOI: 10.1186/s12883-021-02114-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 02/15/2021] [Indexed: 12/21/2022] Open
Abstract
Background To explore the correlation between the volume of putamen and brain cognitive impairment in patients with HIV and to predict the feasibility of early-stage HIV brain cognitive impairment through radiomics. Method Retrospective selection of 90 patients with HIV infection, including 36 asymptomatic neurocognitive impairment (ANI) patients and 54 pre-clinical ANI patients in Beijing YouAn Hospital. All patients received comprehensive neuropsychological assessment and MRI scanning. 3D Slicer software was used to acquire volume of interest (VOI) and radiomics features. Clinical variables and volume of putamen were compared between patients with ANI and pre-clinical ANI. The Kruskal Wallis test was used to analysis multiple comparisons between groups. The relationship between cognitive scores and VOI was compared using linear regression. For radiomics, principal component analysis (PCA) was used to reduce model overfitting and calculations and then a support vector machine (SVM) was used to build a binary classification model. For model performance evaluation, we used an accuracy, sensitivity, specificity and receiver operating characteristic curve (ROC). Result There were no significant differences in clinical variables between ANI group and pre-clinical-ANI group (P>0.05). The volume of bilateral putamen was significantly different between AHI group and pre-clinical group (P<0.05), but there was only a trend in the left putamen between ANI-treatment group and pre-clinical treatment group(P = 0.063). Reduced cognitive scores in Verbal Fluency, Attention/Working Memory, Executive Functioning, memory and Speed of Information Processing were negatively correlated with the increased VOI (P<0.05), but the correlation was relatively low. In diagnosing the ANI from pre-clinical ANI, the mean area under the ROC curves (AUC) were 0.85 ± 0.22, the mean sensitivity and specificity were 63.12 ± 5.51 and 94.25% ± 3.08%. Conclusion The volumes of putamen in patients with ANI may be larger than patients with pre-clinical ANI, the change of the volume of the putamen may have a certain process; there is a relationship between putamen and cognitive impairment, but the exact mechanism is unclear. Radiomics may be a useful tool for predicting early stage HAND in patients with HIV.
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Affiliation(s)
- Yu Qi
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China
| | - Man Xu
- Information and Communication Engineering Department Beijing University of Posts and Telecommunications, Beijing, China
| | - Wei Wang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China
| | - Yuan-Yuan Wang
- Department of Radiology, Beijing Second Hospital, Beijing, China
| | - Jiao-Jiao Liu
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China
| | - Hai-Xia Ren
- Information and Communication Engineering Department Beijing University of Posts and Telecommunications, Beijing, China
| | - Ming-Ming Liu
- Physical Examination Center, Cang zhou Central Hospital, Cang zhou, China
| | - Rui-Li Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.
| | - Hong-Jun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.
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21
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Engedal K, Barca ML, Høgh P, Bo Andersen B, Winther Dombernowsky N, Naik M, Gudmundsson TE, Øksengaard AR, Wahlund LO, Snaedal J. The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia. Dement Geriatr Cogn Disord 2021; 49:38-47. [PMID: 32610316 DOI: 10.1159/000508392] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 05/01/2020] [Indexed: 12/21/2022] Open
Abstract
INTRODUCTION The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHODS From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined. RESULTS Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve. CONCLUSION We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.
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Affiliation(s)
- Knut Engedal
- Norwegian Advisory Unit for Aging and Health, Vestfold Health Trust, Tønsberg, Norway, .,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway,
| | - Maria Lage Barca
- Norwegian Advisory Unit for Aging and Health, Vestfold Health Trust, Tønsberg, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Peter Høgh
- Department of Neurology, Regional Dementia Research Center, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Bo Andersen
- Department of Neurology, Danish Dementia Research Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Nanna Winther Dombernowsky
- Department of Neurology, Danish Dementia Research Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Mala Naik
- Department of Geriatric Medicine, Haraldsplass Deaconess Hospital, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
| | | | | | - Lars-Olof Wahlund
- Section for Clinical Geriatrics, NVS Department, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
| | - Jon Snaedal
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
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22
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Li TR, Wu Y, Jiang JJ, Lin H, Han CL, Jiang JH, Han Y. Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study. Front Cell Dev Biol 2020; 8:605734. [PMID: 33344457 PMCID: PMC7744815 DOI: 10.3389/fcell.2020.605734] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022] Open
Abstract
Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into "converters" and "nonconverters" according to individuals' future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer's Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7-95.9% and 87.1-90.8% in the validation set and 81.9-89.1% and 83.2-83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-β deposition and predicted future cognitive decline (areas under the curve 0.649-0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.
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Affiliation(s)
- Tao-Ran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yue Wu
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Juan-Juan Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Hua Lin
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Chun-Lei Han
- Turku PET Centre and Turku University Hospital, Turku, Finland
| | - Jie-Hui Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
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23
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Matsuoka T, Oya N, Yokota H, Akazawa K, Yamada K, Narumoto J. Pineal volume reduction in patients with mild cognitive impairment who converted to Alzheimer's disease. Psychiatry Clin Neurosci 2020; 74:587-593. [PMID: 32609399 DOI: 10.1111/pcn.13103] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/09/2020] [Accepted: 06/26/2020] [Indexed: 12/22/2022]
Abstract
AIM Pineal parenchymal volume (PPV) reduction is one of the predisposing factors for Alzheimer's disease (AD). Therefore, PPV could be used as a predictor of developing AD in clinical settings. We investigated whether PPV in patients with mild cognitive impairment (MCI) was correlated with conversion of these patients to AD. METHODS A total of 237 patients with MCI underwent brain magnetic resonance imaging. A two-sample t-test was used to compare PPV at baseline in MCI patients who converted to AD (MCI-C) with those who did not convert (MCI-NC). Logistic regression analysis with forced entry was used to identify predictors of AD, with variables of PPV, age, sex, education, APOE-ε4 alleles, Mini Mental State Examination score, and total intracranial volume at baseline. Two-way repeated-measures analysis of variance was conducted to compare PPV at baseline and at the last examination in the MCI-C and MCI-NC groups. RESULTS PPV in the MCI-C group was significantly lower than that in the MCI-NC group. In logistic regression analysis, two independent predictors of AD were identified: Mini Mental State Examination and PPV. Two-way repeated-measures analysis of variance revealed a significant group effect, but no time effect. CONCLUSION The pineal volume is a predictor of AD conversion, and pineal volume reduction in AD starts early when patients are still in the MCI stage. Thus, pineal volume reduction might be useful as a predictor of developing AD in clinical settings.
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Affiliation(s)
- Teruyuki Matsuoka
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nozomu Oya
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hajime Yokota
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Kentaro Akazawa
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jin Narumoto
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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24
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Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK. Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Roadmap for Moving Forward. Korean J Radiol 2020; 21:1345-1354. [PMID: 33169553 PMCID: PMC7689149 DOI: 10.3348/kjr.2020.0715] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/23/2020] [Accepted: 08/15/2020] [Indexed: 12/15/2022] Open
Abstract
Objective To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. Materials and Methods PubMed MEDLINE and EMBASE were searched using the terms ‘cognitive impairment’ or ‘Alzheimer’ or ‘dementia’ and ‘radiomic’ or ‘texture’ or ‘radiogenomic’ for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. Results The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. Conclusion The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.
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Affiliation(s)
- So Yeon Won
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jinna Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Seung Koo Lee
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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25
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Wang M, Yan Z, Xiao SY, Zuo C, Jiang J. A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behav Neurol 2020; 2020:2825037. [PMID: 32908613 PMCID: PMC7450311 DOI: 10.1155/2020/2825037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 07/17/2020] [Accepted: 08/10/2020] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer's disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately. METHODS In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up. Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features. Three different classification strategies were implemented to interrogate the predictive performance. To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge. RESULTS As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features. Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933). The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus. CONCLUSION Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD. The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression.
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Affiliation(s)
- Min Wang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Shu-yun Xiao
- Department of Brain and Mental Disease, Shanghai Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
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26
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Aksu A, Karahan Şen NP, Acar E, Çapa Kaya G. Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics. Nucl Med Mol Imaging 2020; 54:241-248. [PMID: 33088353 DOI: 10.1007/s13139-020-00659-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 07/17/2020] [Accepted: 07/23/2020] [Indexed: 01/14/2023] Open
Abstract
Purpose The aim of this study was to evaluate the ability of 18F-FDG PET/CT texture analysis to predict the exact pathological outcome of thyroid incidentalomas. Methods 18F-FDG PET/CT images between March 2010 and September 2018 were retrospectively reviewed in patients with focal 18F-FDG uptake in the thyroid gland and who underwent fine needle aspiration biopsy from this area. The focal uptake in the thyroid gland was drawn in 3D with 40% SUVmax threshold. Features were extracted from volume of interest (VOI) using the LIFEx package. The features obtained were compared in benign and malignant groups, and statistically significant variables were evaluated by receiver operating curve (ROC) analysis. The correlation between the variables with area under curve (AUC) value over 0.7 was examined; variables with correlation coefficient less than 0.6 were evaluated with machine learning algorithms. Results Sixty patients (70% train set, 30% test set) were included in the study. In univariate analysis, a statistically significant difference was observed in 6 conventional parameters, 5 first-, and 16 second-order features between benign and malignant groups in train set (p < 0.05). The feature with the highest benign-malignant discriminating power was GLRLMRLNU (AUC:0.827). AUC value of SUVmax was calculated as 0.758. GLRLMRLNU and SUVmax were evaluated to build a model to predict the exact pathology outcome. Random forest algorithm showed the best accuracy and AUC (78.6% and 0.849, respectively). Conclusion In the differentiation of benign-malignant thyroid incidentalomas, GLRLMRLNU and SUVmax combination may be more useful than SUVmax to predict the outcome.
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Affiliation(s)
- Ayşegül Aksu
- Department of Nuclear Medicine, School of Medicine, Dokuz Eylul University, İzmir, Turkey
| | | | - Emine Acar
- Department of Nuclear Medicine, Kent Hospital, İzmir, Turkey.,Department of Translational Oncology, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
| | - Gamze Çapa Kaya
- Department of Nuclear Medicine, School of Medicine, Dokuz Eylul University, İzmir, Turkey
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27
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Nakagawa T, Ishida M, Naito J, Nagai A, Yamaguchi S, Onoda K. Prediction of conversion to Alzheimer's disease using deep survival analysis of MRI images. Brain Commun 2020; 2:fcaa057. [PMID: 32954307 PMCID: PMC7425528 DOI: 10.1093/braincomms/fcaa057] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/18/2020] [Accepted: 04/15/2020] [Indexed: 12/24/2022] Open
Abstract
The prediction of the conversion of healthy individuals and those with mild cognitive impairment to the status of active Alzheimer’s disease is a challenging task. Recently, a survival analysis based upon deep learning was developed to enable predictions regarding the timing of an event in a dataset containing censored data. Here, we investigated whether a deep survival analysis could similarly predict the conversion to Alzheimer’s disease. We selected individuals with mild cognitive impairment and cognitively normal subjects and used the grey matter volumes of brain regions in these subjects as predictive features. We then compared the prediction performances of the traditional standard Cox proportional-hazard model, the DeepHit model and our deep survival model based on a Weibull distribution. Our model achieved a maximum concordance index of 0.835, which was higher than that yielded by the Cox model and comparable to that of the DeepHit model. To our best knowledge, this is the first report to describe the application of a deep survival model to brain magnetic resonance imaging data. Our results demonstrate that this type of analysis could successfully predict the time of an individual’s conversion to Alzheimer’s disease.
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Affiliation(s)
- Tomonori Nakagawa
- Department of Neurology, Masuda Red Cross Hospital, Masuda 698-8501, Japan
| | - Manabu Ishida
- Department of Neurology, Shimane University, Izumo 693-8501, Japan.,ERISA Corporation, Matsue 690-0816, Japan
| | | | - Atsushi Nagai
- Department of Neurology, Shimane University, Izumo 693-8501, Japan
| | - Shuhei Yamaguchi
- Department of Neurology, Shimane University, Izumo 693-8501, Japan
| | - Keiichi Onoda
- Department of Neurology, Shimane University, Izumo 693-8501, Japan.,Department of Psychology, Otemon Gakuin University, Osaka 567-8502, Japan
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Wang M, Jiang J, Yan Z, Alberts I, Ge J, Zhang H, Zuo C, Yu J, Rominger A, Shi K. Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. Eur J Nucl Med Mol Imaging 2020; 47:2753-2764. [PMID: 32318784 PMCID: PMC7567735 DOI: 10.1007/s00259-020-04814-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 04/06/2020] [Indexed: 01/10/2023]
Abstract
Purpose Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual’s risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual’s risk of conversion from MCI to AD. Methods FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual’s metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell’s concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics. Results The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model). Conclusion The KLSE indicator identifies abnormal brain networks predicting an individual’s risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker. Electronic supplementary material The online version of this article (10.1007/s00259-020-04814-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China. .,Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China.
| | - Zhuangzhi Yan
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Jingjie Ge
- Department of Nuclear Medicine, PET Center, Huashan Hospital, Fudan University, 518 Wuzhong Dong Road, Shanghai, 201103, China
| | - Huiwei Zhang
- Department of Nuclear Medicine, PET Center, Huashan Hospital, Fudan University, 518 Wuzhong Dong Road, Shanghai, 201103, China
| | - Chuantao Zuo
- Department of Nuclear Medicine, PET Center, Huashan Hospital, Fudan University, 518 Wuzhong Dong Road, Shanghai, 201103, China. .,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| | - Jintai Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland.,Department of Informatics, Technische Universität München, Munich, Germany
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Liu P, Wang H, Zheng S, Zhang F, Zhang X. Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging. Front Neurol 2020; 11:248. [PMID: 32322236 PMCID: PMC7156586 DOI: 10.3389/fneur.2020.00248] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/13/2020] [Indexed: 12/12/2022] Open
Abstract
Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD. Methods: T2-weighted images from 69 PD patients and 69 age- and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group. Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986–0.9833) and 0.7732 (95% CI: 0.6292–0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066–0.9469) and 0.7143 (95% CI: 0.5540–0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05). Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.
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Affiliation(s)
- Panshi Liu
- Department of Radiology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Han Wang
- Medical Imaging Center, Taian Central Hospital, Taian, China
| | - Shilei Zheng
- Department of Radiology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Fan Zhang
- Department of Neurology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Xianglin Zhang
- Department of Radiology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre A, Lista C, Costantino G, Frisoni G, Virgili G, Filippini G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 2020; 3:CD009628. [PMID: 32119112 PMCID: PMC7059964 DOI: 10.1002/14651858.cd009628.pub2] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. OBJECTIVES To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. SEARCH METHODS On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. SELECTION CRITERIA We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. DATA COLLECTION AND ANALYSIS Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. MAIN RESULTS We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate. AUTHORS' CONCLUSIONS The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.
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Affiliation(s)
- Gemma Lombardi
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Giada Crescioli
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Enrica Cavedo
- Pitie‐Salpetriere Hospital, Sorbonne UniversityAlzheimer Precision Medicine (APM), AP‐HP47 boulevard de l'HopitalParisFrance75013
| | - Ersilia Lucenteforte
- University of PisaDepartment of Clinical and Experimental MedicineVia Savi 10PisaItaly56126
| | - Giovanni Casazza
- Università degli Studi di MilanoDipartimento di Scienze Biomediche e Cliniche "L. Sacco"via GB Grassi 74MilanItaly20157
| | | | - Chiara Lista
- Fondazione I.R.C.C.S. Istituto Neurologico Carlo BestaNeuroepidemiology UnitVia Celoria, 11MilanoItaly20133
| | - Giorgio Costantino
- Ospedale Maggiore Policlinico, Università degli Studi di MilanoUOC Pronto Soccorso e Medicina D'Urgenza, Fondazione IRCCS Ca' GrandaMilanItaly
| | | | - Gianni Virgili
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Graziella Filippini
- Carlo Besta Foundation and Neurological InstituteScientific Director’s Officevia Celoria, 11MilanItaly20133
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Fan Y, Feng M, Wang R. Application of Radiomics in Central Nervous System Diseases: a Systematic literature review. Clin Neurol Neurosurg 2019; 187:105565. [DOI: 10.1016/j.clineuro.2019.105565] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 10/12/2019] [Accepted: 10/13/2019] [Indexed: 01/01/2023]
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Wu Y, Jiang JH, Chen L, Lu JY, Ge JJ, Liu FT, Yu JT, Lin W, Zuo CT, Wang J. Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:773. [PMID: 32042789 DOI: 10.21037/atm.2019.11.26] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD. Methods In this retrospective multicohort study, we collected 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times. Results Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively. Conclusions This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.
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Affiliation(s)
- Yue Wu
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Jie-Hui Jiang
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Li Chen
- Department of Medical Ultrasound, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jia-Ying Lu
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jing-Jie Ge
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Feng-Tao Liu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jin-Tai Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Anhui Medical University, Wuxi 214000, China
| | - Chuan-Tao Zuo
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
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Wang M, Yan Z, Jiang J. Brain metabolic connectome classify mild cognitive impairment into Alzheimer's dementia . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:32-35. [PMID: 31945838 DOI: 10.1109/embc.2019.8857104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Identifying whether patients with mild cognitive impairment (MCI) are converting to Alzheimer's disease (AD) is clinically important, but there are still controversies and doubts. We aimed to develop a novel connectome approach which could accurately and precisely predict whether MCI patients are converted to AD using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). In our study, FDG-PET images were acquired from 84 patients with MCI who converted to AD within 48 months and 109 patients with MCI without conversion within 48 months from the Alzheimer's Disease Neuroimaging Initiative database. The experimental results showed that the classification performance about whether an MCI patient would convert to AD were 92.1%, 87.1%, 94.4% and 0.95 (Accuracy, Sensitivity, Specificity and AUC). The abnormality of functional connection was located at Middle frontal gyrus, Posterior cingulate gyrus, Precentral gyrus, Precuneus and Temporal lobe. These finding showed the brain connectome as a practical approach for developing predictive neuroimaging biomarker.
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