1
|
Lin S, Xue M, Sun J, Xu C, Wang T, Lian J, Lv M, Yang P, Sheng C, Cheng Z, Wang W. MRI Radiomics Nomogram for Predicting Disease Transition Time and Risk Stratification in Preclinical Alzheimer's Disease. Acad Radiol 2024:S1076-6332(24)00646-9. [PMID: 39332990 DOI: 10.1016/j.acra.2024.08.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Revised: 08/20/2024] [Accepted: 08/30/2024] [Indexed: 09/29/2024]
Abstract
RATIONALE AND OBJECTIVES Accurate prediction of the progression of preclinical Alzheimer's disease (AD) is crucial for improving clinical management and disease prognosis. The objective of this study was to develop and validate clinical-radimoics integrated model to predict the time to progression (TTP) and disease risk stratification of preclinical AD. MATERIALS AND METHODS A total of 244 cases (mean age: 73.8 ± 5.5 years, 120 women) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly divided into the training cohort (n = 172) and validation cohort (n = 72) using a 7:3 ratio. Clinical factors were identified by univariate and multivariate COX regression. Radiomics features were extracted from GM, WM and CSF of T1WI images and selected by Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO). Using selected clinical factors and radiomics features, the clinical, radimocis and clinical-radiomics nomogram models were developed for predicting the TTP. The performance of each model was assessed by C-index. The risk stratification ability and predicting efficacy of the clinical-radiomics model were utilizing the Kaplan-Meier curve and receiver operator characteristic (ROC) curve. RESULTS The C-index of clinical, radimocis and clinical-radiomics models were 0.852 (95% confidence interval[CI]:0.810-0.893), 0.863 (95%CI:0.816-0.910) and 0.903 (95%:0.870-0.936) in the training cohort and 0.725 (95%CI:0.630-0.820), 0.788 (95%CI:0.678-0.898), 0.813(95%CI:0.734-0.892) in the validation cohort. The AUCs of the multi-predictor nomogram at 1-, 3-, 5- and 7-year were 0.894, 0.908, 0.930, 0.979 in the training cohort and 0.671, 0.726, 0.839, 0.931 in the validation cohort. CONCLUSION In this study, we constructed a clinical-radimoics integrated model to predict the progression of preclinical AD and stratified the risk of disease progression in preclinical AD.
Collapse
Affiliation(s)
- Shuai Lin
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ming Xue
- Department of Radiology, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jiali Sun
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chang Xu
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianqi Wang
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | | | - Min Lv
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ping Yang
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chenjun Sheng
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zijian Cheng
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wei Wang
- Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China.
| |
Collapse
|
2
|
Dounavi M, Mak E, Operto G, Muniz‐Terrera G, Bridgeman K, Koychev I, Malhotra P, Naci L, Lawlor B, Su L, Falcon C, Ritchie K, Ritchie CW, Gispert JD, O'Brien JT. Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population. Hum Brain Mapp 2024; 45:e26798. [PMID: 39081128 PMCID: PMC11289425 DOI: 10.1002/hbm.26798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 06/06/2024] [Accepted: 07/10/2024] [Indexed: 08/03/2024] Open
Abstract
Brain atrophy and cortical thinning are typically observed in people with Alzheimer's disease (AD) and, to a lesser extent, in those with mild cognitive impairment. In asymptomatic middle-aged apolipoprotein ε4 (ΑPOE4) carriers, who are at higher risk of future AD, study reports are discordant with limited evidence of brain structural differences between carriers and non-carriers of the ε4 allele. Alternative imaging markers with higher sensitivity at the presymptomatic stage, ideally quantified using typically acquired structural MRI scans, would thus be of great benefit for the detection of early disease, disease monitoring and subject stratification. In the present cross-sectional study, we investigated textural properties of T1-weighted 3T MRI scans in relation to APOE4 genotype, age and sex. We pooled together data from the PREVENT-Dementia and ALFA studies focused on midlife healthy populations with dementia risk factors (analysable cohort: 1585 participants; mean age 56.2 ± 7.4 years). Voxel-based and texture (examined features: contrast, entropy, energy, homogeneity) based morphometry was used to identify areas of volumetric and textural differences between APOE4 carriers and non-carriers. Textural maps were generated and were subsequently harmonised using voxel-wise COMBAT. For all analyses, APOE4, sex, age and years of education were used as model predictors. Interactions between APOE4 and age were further examined. There were no group differences in regional brain volume or texture based on APOE4 carriership or when age × APOE4 interactions were examined. Older people tended to have a less homogeneous textural profile in grey and white matter and a more homogeneous profile in the ventricles. A more heterogeneous textural profile was observed for females in areas such as the ventricles, frontal and parietal lobes and for males in the brainstem, cerebellum, precuneus and cingulate. Overall, we have shown the absence of volumetric and textural differences between APOE4 carriers and non-carriers at midlife and have established associations of textural features with ageing and sex.
Collapse
Affiliation(s)
- Maria‐Eleni Dounavi
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
| | - Elijah Mak
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
| | - Gregory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - Graciela Muniz‐Terrera
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
- Heritage College of Osteopathic MedicineOhio UniversityAthensOhioUSA
| | - Katie Bridgeman
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
| | | | - Paresh Malhotra
- Division of Brain ScienceImperial College Healthcare NHS TrustUK
| | - Lorina Naci
- Institute of Neuroscience, Trinity College Dublin, University of DublinIreland
| | - Brian Lawlor
- Institute of Neuroscience, Trinity College Dublin, University of DublinIreland
| | - Li Su
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
- Department of NeuroscienceUniversity of SheffieldSheffieldUK
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - Karen Ritchie
- INSERM and University of MontpellierMontpellierFrance
| | - Craig W. Ritchie
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - John T. O'Brien
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
| |
Collapse
|
3
|
Mu X, Cui C, Liao J, Wu Z, Hu L. Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics. EJNMMI REPORTS 2024; 8:19. [PMID: 38945980 PMCID: PMC11214937 DOI: 10.1186/s41824-024-00206-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/22/2024] [Indexed: 07/02/2024]
Abstract
BACKGROUND This study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS In our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression. RESULTS Voxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD. CONCLUSION Our findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.
Collapse
Affiliation(s)
- Xuxu Mu
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Caozhe Cui
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Jue Liao
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Zhifang Wu
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Lingzhi Hu
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China.
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Yuan Z, Li X, Hao Z, Tang Z, Yao X, Wu T. Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network. Sci Rep 2024; 14:11994. [PMID: 38796518 PMCID: PMC11127948 DOI: 10.1038/s41598-024-62712-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 05/21/2024] [Indexed: 05/28/2024] Open
Abstract
This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals), mild cognitive impairment (238 patients), and AD (111 patients) in this study. An improved multifeature squeeze-and-excitation-dilated residual network (MFSE-DRN) was proposed for two important AD predictions: clinical scores and conversion probability. The model was characterized as three modules: squeeze-and-excitation-dilated residual block (SE-DRB), multifusion pooling (MF-Pool), and multimodal feature fusion. To assess its performance, the proposed model was compared with two other novel models: ranking convolutional neural network (RCNN) and 3D vision geometrical group network (3D-VGGNet). Our method showed the best performance in the two AD predicted tasks. For the clinical scores prediction, the root-mean-square errors (RMSEs) and mean absolute errors (MAEs) of mini-mental state examination (MMSE) and AD assessment scale-cognitive 11-item (ADAS-11) were 1.97, 1.46 and 4.20, 3.19 within 6 months; 2.48, 1.69 and 4.81, 3.44 within 12 months; 2.67, 1.86 and 5.81, 3.83 within 24 months; 3.02, 2.03 and 5.09, 3.43 within 36 months, respectively. At the AD conversion probability prediction, the prediction accuracies within 12, 24, and 36 months reached to 88.0, 85.5, and 88.4%, respectively. The AD predication would play a great role in clinical applications.
Collapse
Affiliation(s)
- Zengbei Yuan
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Xinlin Li
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zezhou Hao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zhixian Tang
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| |
Collapse
|
6
|
Zhao J, Jiao Y, Wang H, Song P, Gao Z, Bing X, Zhang C, Ouyang A, Yao J, Wang S, Jiang H. Radiomic features of the hippocampal based on magnetic resonance imaging in the menopausal mouse model linked to neuronal damage and cognitive deficits. Brain Imaging Behav 2024; 18:368-377. [PMID: 38102441 PMCID: PMC11156756 DOI: 10.1007/s11682-023-00808-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/01/2023] [Indexed: 12/17/2023]
Abstract
Estrogen deficiency in the early postmenopausal phase is associated with an increased long-term risk of cognitive decline or dementia. Non-invasive characterization of the pathological features of the pathological hallmarks in the brain associated with postmenopausal women (PMW) could enhance patient management and the development of therapeutic strategies. Radiomics is a means to quantify the radiographic phenotype of a diseased tissue via the high-throughput extraction and mining of quantitative features from images acquired from modalities such as CT and magnetic resonance imaging (MRI). This study set out to explore the correlation between radiomics features based on MRI and pathological features of the hippocampus and cognitive function in the PMW mouse model. Ovariectomized (OVX) mice were used as PWM models. MRI scans were performed two months after surgery. The brain's hippocampal region was manually annotated, and the radiomic features were extracted with PyRadiomics. Chemiluminescence was used to evaluate the peripheral blood estrogen level of mice, and the Morris water maze test was used to evaluate the cognitive ability of mice. Nissl staining and immunofluorescence were used to quantify neuronal damage and COX1 expression in brain sections of mice. The OVX mice exhibited marked cognitive decline, brain neuronal damage, and increased expression of mitochondrial complex IV subunit COX1, which are pathological phenomena commonly observed in the brains of AD patients, and these phenotypes were significantly correlated with radiomics features (p < 0.05, |r|>0.5), including Original_firstorder_Interquartile Range, Original_glcm_Difference Average, Original_glcm_Difference Average and Wavelet-LHH_glszm_Small Area Emphasis. Meanwhile, the above radiomics features were significantly different between the sham-operated and OVX groups (p < 0.01) and were associated with decreased serum estrogen levels (p < 0.05, |r|>0.5). This initial study indicates that the above radiomics features may have a role in the assessment of the pathology of brain damage caused by estrogen deficiency using routinely acquired structural MR images.
Collapse
Affiliation(s)
- Jie Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yan Jiao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Hui Wang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Peiji Song
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhen Gao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xue Bing
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chunling Zhang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Aimei Ouyang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jian Yao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, No.725, South Wanping Road, Shanghai, 200032, China.
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.
| |
Collapse
|
7
|
Maniam S, Maniam S. Screening Techniques for Drug Discovery in Alzheimer's Disease. ACS OMEGA 2024; 9:6059-6073. [PMID: 38371787 PMCID: PMC10870277 DOI: 10.1021/acsomega.3c07046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.
Collapse
Affiliation(s)
- Sandra Maniam
- Department
of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia
| | - Subashani Maniam
- School
of Science, STEM College, RMIT University, Melbourne, Victoria 3001, Australia
| |
Collapse
|
8
|
Zhu XW, Liu SB, Ji CH, Liu JJ, Huang C. Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function. Front Neurol 2024; 15:1352423. [PMID: 38370526 PMCID: PMC10870793 DOI: 10.3389/fneur.2024.1352423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Background Previous studies mainly focused on risk factors in patients with mild cognitive impairment (MCI) or dementia. The aim of the study was to provide basis for preventing MCI in cognitive normal populations. Methods The data came from a longitudinal retrospective study involving individuals with brain magnetic resonance imaging scans, clinical visits, and cognitive assessment with interval of more than 3 years. Multiple machine-learning technologies, including random forest, support vector machine, logistic regression, eXtreme Gradient Boosting, and naïve Bayes, were used to establish a prediction model of a future risk of MCI through a combination of clinical and image variables. Results Among these machine learning models; eXtreme Gradient Boosting (XGB) was the best classification model. The classification accuracy of clinical variables was 65.90%, of image variables was 79.54%, of a combination of clinical and image variables was 94.32%. The best result of the combination was an accuracy of 94.32%, a precision of 96.21%, and a recall of 93.08%. XGB with a combination of clinical and image variables had a potential prospect for the risk prediction of MCI. From clinical perspective, the degree of white matter hyperintensity (WMH), especially in the frontal lobe, and the control of systolic blood pressure (SBP) were the most important risk factor for the development of MCI. Conclusion The best MCI classification results came from the XGB model with a combination of both clinical and imaging variables. The degree of WMH in the frontal lobe and SBP control were the most important variables in predicting MCI.
Collapse
Affiliation(s)
- Xia Wei Zhu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
| | - Si Bo Liu
- Intensive Care Unit, Dalian Municipal Central Hospital Affiliated Dalian University of Technology, Dalian, China
| | - Chen Hua Ji
- Department of General Medicine, Dalian Municipal Central Hospital Affiliated to Dalian University of Technology, Dalian, China
| | - Jin Jie Liu
- Department of General Medicine, Dalian Municipal Central Hospital Affiliated to Dalian University of Technology, Dalian, China
| | - Chao Huang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
| |
Collapse
|
9
|
Chen J, Yang J, Shen D, Wang X, Lin Z, Chen H, Cui G, Zhang Z. A Predictive Model of the Progression to Alzheimer's Disease in Patients with Mild Cognitive Impairment Based on the MRI Enlarged Perivascular Spaces. J Alzheimers Dis 2024; 101:159-173. [PMID: 39177602 DOI: 10.3233/jad-240523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Background Mild cognitive impairment (MCI) is a heterogeneous condition that can precede various forms of dementia, including Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of progressing to AD is of major clinical relevance. Enlarged perivascular spaces (EPVS) on MRI are linked to cognitive decline, but their predictive value for MCI to AD progression is unclear. Objective This study aims to assess the predictive value of EPVS for MCI to AD progression and develop a predictive model combining EPVS grading with clinical and laboratory data to estimate conversion risk. Methods We analyzed 358 patients with MCI from the ADNI database, consisting of 177 MCI-AD converters and 181 non-converters. The data collected included demographic information, imaging data (including perivascular spaces grade), clinical assessments, and laboratory test results. Variable selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO) method, followed by logistic regression to develop predictive model. Results In the univariate logistic regression analysis, both moderate (OR = 5.54, 95% CI [3.04-10.18]) and severe (OR = 25.04, 95% CI [10.07-62.23]) enlargements of the centrum semiovale perivascular space (CSO-PVS) were found to be strong predictors of disease progression. LASSO analyses yielded 12 variables, refined to six in the final model: APOE4 genotype, ADAS11 score, CSO-PVS grade, and volumes of entorhinal, fusiform, and midtemporal regions, with an AUC of 0.956 in the training and 0.912 in the validation cohort. Conclusions Our predictive model, emphasizing EPVS assessment, provides clinicians with a practical tool for early detection and management of AD risk in MCI patients.
Collapse
Affiliation(s)
- Jun Chen
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jingwen Yang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Dayong Shen
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xi Wang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Zihao Lin
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hao Chen
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Guiyun Cui
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Zuohui Zhang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| |
Collapse
|
10
|
Wu Y, Wang X, Gu C, Zhu J, Fang Y. Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals. Age Ageing 2023; 52:afad182. [PMID: 37740920 PMCID: PMC10518045 DOI: 10.1093/ageing/afad182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Indexed: 09/25/2023] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations. METHODS Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance. RESULTS The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors. CONCLUSION Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.
Collapse
Affiliation(s)
- Yafei Wu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Xing Wang
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Chenming Gu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Junmin Zhu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Ya Fang
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China
| |
Collapse
|
11
|
Ho NH, Jeong YH, Kim J. Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay. Sci Rep 2023; 13:11243. [PMID: 37433809 PMCID: PMC10336016 DOI: 10.1038/s41598-023-37500-7] [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: 11/02/2022] [Accepted: 06/22/2023] [Indexed: 07/13/2023] Open
Abstract
Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer's disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ([Formula: see text]). Moreover, our performance was equivalent to that of contemporary research.
Collapse
Affiliation(s)
- Ngoc-Huynh Ho
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
| | - Yang-Hyung Jeong
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea.
| | - Jahae Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
- Department of Nuclear Medicine, Chonnam National University Hospital, Gwangju, 61469, South Korea
| |
Collapse
|
12
|
Wearn A, Raket LL, Collins DL, Spreng RN. Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease. Brain Commun 2023; 5:fcad195. [PMID: 37465755 PMCID: PMC10351670 DOI: 10.1093/braincomms/fcad195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/09/2023] [Accepted: 07/04/2023] [Indexed: 07/20/2023] Open
Abstract
Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T1-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-β- (n = 406); cognitively unimpaired amyloid-β+ (n = 213); mild cognitive impairment amyloid-β+ (n = 347); and Alzheimer's disease dementia amyloid-β+ (n = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-β- (n = 345); cognitively unimpaired amyloid-β+ (n = 173); mild cognitive impairment amyloid-β+ (n = 301); and Alzheimer's disease dementia amyloid-β+ (n = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-β- and cognitively unimpaired amyloid-β+ older adults (Cohen's d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30-1.22, Padj < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change (d = 0.32-0.63, Padj < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.
Collapse
Affiliation(s)
- Alfie Wearn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
| | - Lars Lau Raket
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund SE-221 00, Sweden
- Novo Nordisk A/S, Søborg 2860, Denmark
| | - D Louis Collins
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada H3A 2B4
| | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada H3A 2B4
- Departments of Psychology and Psychiatry, McGill University, Montreal, QC, Canada H3A 2B4
- Douglas Mental Health University Institute, Verdun, QC, Canada H4H 1R3
| | | |
Collapse
|
13
|
Leandrou S, Lamnisos D, Bougias H, Stogiannos N, Georgiadou E, Achilleos KG, Pattichis CS. A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features. Front Aging Neurosci 2023; 15:1149871. [PMID: 37358951 PMCID: PMC10285704 DOI: 10.3389/fnagi.2023.1149871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023] Open
Abstract
Introduction Alzheimer's disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD. Methods In this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated. Results The model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively. Discussion These directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD.
Collapse
Affiliation(s)
| | | | | | - Nikolaos Stogiannos
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland
- Division of Midwifery and Radiography, City, University of London, London, United Kingdom
- Medical Imaging Department, Corfu General Hospital, Corfu, Greece
| | | | - K. G. Achilleos
- Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus
| | - Constantinos S. Pattichis
- Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus
- CYENS Centre of Excellence, Nicosia, Cyprus
| | | |
Collapse
|
14
|
Wang XL, Zhai RQ, Li ZM, Li HQ, Lei YT, Zhao FF, Hao XX, Wang SY, Wu YH. Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis. Front Aging Neurosci 2023; 15:1168840. [PMID: 37181620 PMCID: PMC10172508 DOI: 10.3389/fnagi.2023.1168840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/07/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction The aim of this study is to establish a prognostic risk model based on ferroptosis to prognosticate the severity of Alzheimer's disease (AD) through gene expression changes. Methods The GSE138260 dataset was initially downloaded from the Gene expression Omnibus database. The ssGSEA algorithm was used to evaluate the immune infiltration of 28 kinds of immune cells in 36 samples. The up-regulated immune cells were divided into Cluster 1 group and Cluster 2 group, and the differences were analyzed. The LASSO regression analysis was used to establish the optimal scoring model. Cell Counting Kit-8 and Real Time Quantitative PCR were used to verify the effect of different concentrations of Aβ1-42 on the expression profile of representative genes in vitro. Results Based on the differential expression analysis, there were 14 up-regulated genes and 18 down-regulated genes between the control group and Cluster 1 group. Cluster 1 and Cluster 2 groups were differentially analyzed, and 50 up-regulated genes and 101 down-regulated genes were obtained. Finally, nine common differential genes were selected to establish the optimal scoring model. In vitro, CCK-8 experiments showed that the survival rate of cells decreased significantly with the increase of Aβ1-42 concentration compared with the control group. Moreover, RT-qPCR showed that with the increase of Aβ1-42 concentration, the expression of POR decreased first and then increased; RUFY3 was firstly increased and then decreased. Discussion The establishment of this research model can help clinicians make decisions on the severity of AD, thus providing better guidance for the clinical treatment of Alzheimer's disease.
Collapse
Affiliation(s)
- Xiao-Li Wang
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
| | - Rui-Qing Zhai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhi-Ming Li
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
| | - Hong-Qiu Li
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
| | - Ya-Ting Lei
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
| | - Fang-Fang Zhao
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
| | - Xiao-Xiao Hao
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
| | - Sheng-Yuan Wang
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
- *Correspondence: Sheng-Yuan Wang,
| | - Yong-Hui Wu
- Department of Occupational Health, Public Health College, Harbin Medical University, Harbin, China
- Yong-Hui Wu,
| |
Collapse
|
15
|
Li H, Liu H, Lutz MW, Luo S. Novel Genetic Variants in TP37, PIK3R1, CALM1, and PLCG2 of the Neurotrophin Signaling Pathway Are Associated with the Progression from Mild Cognitive Impairment to Alzheimer's Disease. J Alzheimers Dis 2023; 91:977-987. [PMID: 36530083 PMCID: PMC9905310 DOI: 10.3233/jad-220680] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a common neurodegenerative disease and mild cognitive impairment (MCI) is considered as the prodromal stage of AD. Previous studies showed that changes in the neurotrophin signaling pathway could lead to cognitive decline in AD. However, the association of single nucleotide polymorphisms (SNPs) in genes that are involved in this pathway with AD progression from MCI remains unclear. OBJECTIVE We investigated the associations between SNPs involved in the neurotrophin signaling pathway with AD progression. METHODS We performed single-locus analysis to identify neurotrophin-signaling-related SNPs associated with the AD progression using 767 patients from the Alzheimer's Disease Neuroimaging Initiative study and 1,373 patients from the National Alzheimer's Coordinating Center study. We constructed polygenic risk scores (PRSs) using the identified independent non-APOE SNPs and evaluated its prediction performance on AD progression. RESULTS We identified 25 SNPs significantly associated with AD progression with Bayesian false-discovery probability ≤0.8. Based on the linkage disequilibrium clumping and expression quantitative trait loci analysis, we found 6 potentially functional SNPs that were associated with AD progression independently. The PRS analysis quantified the combined effects of these SNPs on longitudinal cognitive assessments and biomarkers from cerebrospinal fluid and neuroimaging. The addition of PRSs to the prediction model for 3-year progression to AD from MCI significantly increased the predictive accuracy. CONCLUSION Genetic variants in the specific genes of the neurotrophin signaling pathway are predictors of AD progression. eQTL analysis supports that these SNPs regulate expression of key genes involved in the neurotrophin signaling pathway.
Collapse
Affiliation(s)
- Huiyue Li
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Hongliang Liu
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Michael W. Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | | |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022; 14:cancers14143349. [PMID: 35884409 PMCID: PMC9321521 DOI: 10.3390/cancers14143349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Abstract Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
Collapse
|
18
|
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.
Collapse
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
| | | |
Collapse
|
19
|
Du Y, Zhang S, Fang Y, Qiu Q, Zhao L, Wei W, Tang Y, Li X. Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease. Front Aging Neurosci 2022; 13:789099. [PMID: 35153721 PMCID: PMC8826454 DOI: 10.3389/fnagi.2021.789099] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Late-onset Alzheimer’s disease (LOAD) and early-onset Alzheimer’s disease (EOAD) are different subtypes of AD. This study aimed to build and validate radiomics models of the hippocampus for EOAD and young controls (YCs), LOAD and old controls (OCs), as well as EOAD and LOAD. Methods: Thirty-six EOAD patients, 36 LOAD patients, 36 YCs, and 36 OCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were enrolled and allocated to training and test sets of the EOAD-YC groups, LOAD-OC groups, and EOAD-LOAD groups. Independent external validation sets including 15 EOAD patients, 15 LOAD patients, 15 YCs, and 15 OCs from Shanghai Mental Health Center were constructed, respectively. Bilateral hippocampal segmentation and feature extraction were performed for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. Support vector machine (SVM) models were constructed based on the identified features to distinguish EOAD from YC subjects, LOAD from OC subjects, and EOAD from LOAD subjects. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the models. Results: Three, three, and four features were selected for EOAD and YC subjects, LOAD and OC subjects, and EOAD and LOAD subjects, respectively. The AUC and accuracy of the SVM model were 0.90 and 0.77 in the test set and 0.91 and 0.87 in the validation set for EOAD and YC subjects, respectively; for LOAD and OC subjects, the AUC and accuracy were 0.94 and 0.86 in the test set and 0.92 and 0.78 in the validation set, respectively. For the SVM model of EOAD and LOAD subjects, the AUC was 0.87 and the accuracy was 0.79 in the test set; additionally, the AUC was 0.86 and the accuracy was 0.77 in the validation set. Conclusion: The findings of this study provide insights into the potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD. This study is the first to show that SVM classification analysis based on hippocampal radiomic features is a valuable method for clinical applications in EOAD.
Collapse
|
20
|
Zhu JD, Huang CW, Chang HI, Tsai SJ, Huang SH, Hsu SW, Lee CC, Chen HJ, Chang CC, Yang AC. Functional MRI and ApoE4 genotype for predicting cognitive decline in amyloid-positive individuals. Ther Adv Neurol Disord 2022; 15:17562864221138154. [DOI: 10.1177/17562864221138154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 10/24/2022] [Indexed: 11/21/2022] Open
Abstract
Background: In light of advancements in machine learning techniques, many studies have implemented machine learning approaches combined with data measures to predict and classify Alzheimer’s disease. Studies that predicted cognitive status with longitudinal follow-up of amyloid-positive individuals remain scarce, however. Objective: We developed models based on voxel-wise functional connectivity (FC) density mapping and the presence of the ApoE4 genotype to predict whether amyloid-positive individuals would experience cognitive decline after 1 year. Methods: We divided 122 participants into cognitive decline and stable cognition groups based on the participants’ change rates in Mini-Mental State Examination scores. In addition, we included 68 participants from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database as an external validation data set. Subsequently, we developed two classification models: the first model included 99 voxels, and the second model included 99 voxels and the ApoE4 genotype as features to train the models by Wide Neural Network algorithm with fivefold cross-validation and to predict the classes in the hold-out test and ADNI data sets. Results: The results revealed that both models demonstrated high accuracy in classifying the two groups in the hold-out test data set. The model for FC demonstrated good performance, with a mean F1-score of 0.86. The model for FC combined with the ApoE4 genotype achieved superior performance, with a mean F1-score of 0.90. In the ADNI data set, the two models demonstrated stable performances, with mean F1-scores of 0.77 in the first and second models. Conclusion: Our findings suggest that the proposed models exhibited promising accuracy for predicting cognitive status after 1 year in amyloid-positive individuals. Notably, the combination of FC and the ApoE4 genotype increased prediction accuracy. These findings can assist clinicians in predicting changes in cognitive status in individuals with a high risk of Alzheimer’s disease and can assist future studies in developing precise treatment and prevention strategies.
Collapse
Affiliation(s)
- Jun-Ding Zhu
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chi-Wei Huang
- Department of Neurology, Cognition and Aging Center, Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Hsin-I Chang
- Department of Neurology, Cognition and Aging Center, Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shu-Hua Huang
- Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Shih-Wei Hsu
- Department of NeuroRadiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chen-Chang Lee
- Department of NeuroRadiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Hong-Jie Chen
- Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chiung-Chih Chang
- Cognition and Aging Center, Institute for Translational Research in Biomedicine, Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niau-Sung District, Kaohsiung 833, Taiwan
| | - Albert C. Yang
- Institute of Brain Science/Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Beitou District, Taipei 112, Taiwan
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| |
Collapse
|
21
|
Karami V, Nittari G, Traini E, Amenta F. An Optimized Decision Tree with Genetic Algorithm Rule-Based Approach to Reveal the Brain's Changes During Alzheimer's Disease Dementia. J Alzheimers Dis 2021; 84:1577-1584. [PMID: 34719494 DOI: 10.3233/jad-210626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia. OBJECTIVE This study introduces a new CADS for deep learning of magnetic resonance image (MRI) data to identify changes in the brain during Alzheimer's disease (AD) dementia. METHODS The proposed algorithm employed a decision tree with genetic algorithm rule-based optimization to classify input data which were extracted from MRI. This pipeline is applied to the healthy and AD subjects of the Open Access Series of Imaging Studies (OASIS). RESULTS Final evaluation of the CADS and its comparison with other systems supported the potential of the proposed model as a novel tool for investigating the progression of AD and its great ability as an innovative computerized help to facilitate the decision-making procedure for the diagnosis of AD. CONCLUSION The one-second time response, together with the identified high accurate performance, suggests that this system could be useful in future cognitive and computational neuroscience studies.
Collapse
Affiliation(s)
- Vania Karami
- Department of Neurology and Neurosurgery, MontrealNeurological Institute-Hospital (MNI), McGill University, Montreal, Canada
| | - Giulio Nittari
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
| | - Enea Traini
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
| | - Francesco Amenta
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
| |
Collapse
|