Login Register Cart 0
Generic placeholder image

当代阿耳茨海默病研究

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe Translate in English
General Review Article

阿尔茨海默病和轻度认知障碍的MRI放射学分类与预测：综述

卷 17, 期 3, 2020

页: [297 - 309] 页: 13

弟呕挨: 10.2174/1567205017666200303105016

价格: $65

摘要

背景：阿尔茨海默氏病（AD）是一种威胁老年人健康的进行性神经退行性疾病。轻度认知障碍（MCI）被认为是AD的前驱阶段。迄今为止，AD或MCI诊断是在不可逆的大脑结构改变后建立的。因此，开发新的生物标志物对于这种疾病的早期发现和治疗至关重要。目前，已有一些研究表明，放射学分析可以作为AD和MCI的良好诊断和分类方法。 目的：对文献进行了广泛的回顾，以探索放射线分析法在AD患者，MCI患者和正常对照（NC）的诊断和分类中的应用。 结果：最终选择了30项完成的MRI放射学研究。放射线分析的过程通常包括图像数据的获取，感兴趣区域（ROI）分割，特征提取，特征选择以及分类或预测。从那些放射学方法中，纹理分析占据了很大一部分。此外，提取的特征包括直方图，基于形状的特征，基于纹理的特征，小波特征，灰度共生矩阵（GLCM）和运行长度矩阵（RLM）。 结论：尽管放射组学分析已经用于AD和MCI的诊断和分类，但是从这些计算机辅助诊断方法到临床应用还有很长的路要走。

关键词: 阿尔茨海默氏病，轻度认知障碍，磁共振成像，放射线影像学，质地分析，分类。

« Previous
[1]
Morris JC, Storandt M, Miller JP, et al. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol 2001; 58(3): 397-405.
[http://dx.doi.org/10.1001/archneur.58.3.397] [PMID: 11255443]
[2]
Dubois B, Feldman HH, Jacova C, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 2014; 13(6): 614-29.
[http://dx.doi.org/10.1016/S1474-4422(14)70090-0] [PMID: 24849862]
[3]
Philippe L, Emmanuel RV, Ralph L, Sara C, etal.Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48(4): 441-6.
[http://dx.doi.org/10.1016/j.ejca.2011.11.036] [PMID: 22257792]
[4]
Wang G, He L, Yuan C, Huang Y, Liu Z, Liang C. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma. Eur J Radiol 2018; 98: 100-6.
[http://dx.doi.org/10.1016/j.ejrad.2017.11.007] [PMID: 29279146]
[5]
Kickingereder P, Burth S, Wick A, et al. Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016; 280(3): 880-9.
[http://dx.doi.org/10.1148/radiol.2016160845] [PMID: 27326665]
[6]
Chaddad A, Desrosiers C, Toews M. Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age. Sci Rep 2017; 7: 45639.
[http://dx.doi.org/10.1038/srep45639] [PMID: 28361913]
[7]
Feng Q, Chen Y, Liao Z, et al. Corpus callosum radiomics-based classification model in Alzheimer’s disease: A case-control study. Front Neurol 2018; 9: 618.
[http://dx.doi.org/10.3389/fneur.2018.00618] [PMID: 30093881]
[8]
Sørensen L, Igel C, Liv Hansen N, et al. Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing.Early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp 2016; 37(3): 1148-61.
[http://dx.doi.org/10.1002/hbm.23091] [PMID: 26686837]
[9]
Sun H, Chen Y, Huang Q, et al. Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: A radiomics analysis. Radiology 2018; 287(2): 620-30.
[http://dx.doi.org/10.1148/radiol.2017170226] [PMID: 29165048]
[10]
Chaddad A, Desrosiers C, Hassan L, Tanougast C. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci 2017; 18(1): 52.
[http://dx.doi.org/10.1186/s12868-017-0373-0] [PMID: 28821235]
[11]
de Oliveira MS, Balthazar ML, D’Abreu A, et al. MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer disease. AJNR Am J Neuroradiol 2011; 32(1): 60-6.
[http://dx.doi.org/10.3174/ajnr.A2232] [PMID: 20966061]
[12]
Sørensen L, Igel C, Pai A, et al. Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle flagship study of ageing.Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. Neuroimage Clin 2016; 13(C): 470-82.
[PMID: 28119818]
[13]
Feng F, Wang P, Zhao K. Zhou B, Yao H, Meng Q, et al.Radiomic features of hippocampal subregions in Alzheimer’s disease and amnestic mild cognitive impairment. Front Aging Neurosci 2018; 10: 290.
[http://dx.doi.org/10.3389/fnagi.2018.00290]
[14]
Hett K, Ta V-T, Manjón JV, Coupé P. Initiative AsDN, Eds. Adaptive fusion of texture-based grading: application to Alzheimer’s disease detection. Proceedings of the International Workshop on Patch-based Techniques in Medical Imaging.
[http://dx.doi.org/10.1007/978-3-319-67434-6_10]
[15]
Zhang J, Yu C, Jiang G, Liu W, Tong L. 3D texture analysis on MRI images of Alzheimer’s disease. Brain Imaging Behav 2012; 6(1): 61-9.
[http://dx.doi.org/10.1007/s11682-011-9142-3] [PMID: 22101754]
[16]
Ni G, Li-Xin T, Jian H, Feng Z, Xia L, Finbarr OS, et al.Contourlet-based hippocampal magnetic resonance imaging texture features for multivariant classification and prediction of Alzheimer’s disease. Metab Brain Dis 2018; 33(6): 1899-909.
[PMID: 30178281]
[17]
Nemmi F, Saint-Aubert L, Adel D, et al. Insight on AV-45 binding in white and grey matter from histogram analysis: a study on early Alzheimer’s disease patients and healthy subjects. Eur J Nucl Med Mol Imaging 2014; 41(7): 1408-18.
[http://dx.doi.org/10.1007/s00259-014-2728-4] [PMID: 24573658]
[18]
Freeborough PA, Fox NC. MR image texture analysis applied to the diagnosis and tracking of Alzheimer’s disease. IEEE Trans Med Imaging 1998; 17(3): 475-9.
[http://dx.doi.org/10.1109/42.712137] [PMID: 9735911]
[19]
Liu J, Wang J, Hu B, Wu FX, Pan Y. Alzheimer’s disease classification based on individual hierarchical networks constructed with 3D texture features. IEEE Trans Nanobioscience 2017; 99: 1-1.
[20]
Alam S, Kwon GR, Kim JI, Park CS. Twin SVM-based classification of alzheimer’s disease using complex dual-tree wavelet principal coefficients and LDA. J Healthc Eng 2017; 2017 8750506
[21]
Beheshti I, Maikusa N, Daneshmand M, Matsuda H, Demirel H, Anbarjafari G. Japanese-Alzheimer’s Disease Neuroimaging Initiative. Classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion using histogram-based analysis of patient-specific anatomical brain connectivity networks. J Alzheimers Dis 2017; 60(1): 295-304.
[http://dx.doi.org/10.3233/JAD-161080] [PMID: 28800325]
[22]
Jha D, Kim JI, Kwon GR. Diagnosis of Alzheimer’s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network. J Healthc Eng 2017; 2017(1) 9060124
[http://dx.doi.org/10.1155/2017/9060124] [PMID: 29065663]
[23]
Nie K, Shi L, Chen Q, et al. Rectal cancer: Assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 2016; 22(21): 5256-64.
[http://dx.doi.org/10.1158/1078-0432.CCR-15-2997] [PMID: 27185368]
[24]
Milchenko M, Snyder AZ, LaMontagne P, et al. Heterogeneous optimization framework: Reproducible preprocessing of multi-spectral clinical MRI for neuro-oncology imaging research. Neuroinformatics 2016; 14(3): 305-17.
[http://dx.doi.org/10.1007/s12021-016-9296-7] [PMID: 26910516]
[25]
de Flores R, La Joie R, Chételat G. Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience 2015; 309: 29-50.
[http://dx.doi.org/10.1016/j.neuroscience.2015.08.033] [PMID: 26306871]
[26]
Di Paola M, Luders E, Di Iulio F, et al. Callosal atrophy in mild cognitive impairment and Alzheimer’s disease: different effects in different stages. Neuroimage 2010; 49(1): 141-9.
[http://dx.doi.org/10.1016/j.neuroimage.2009.07.050] [PMID: 19643188]
[27]
Lepage M, Habib R, Tulving E. Hippocampal PET activations of memory encoding and retrieval: the HIPER model. Hippocampus 1998; 8(4): 313-22.
[http://dx.doi.org/10.1002/(SICI)1098-1063(1998)8:4<313:AID-HIPO1>3.0.CO;2-I] [PMID: 9744418]
[28]
Giulietti G, Torso M, Serra L, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI).Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and alzheimer’s disease patients. J Magn Reson Imaging 2018.
[http://dx.doi.org/10.1002/jmri.25947] [PMID: 29356183]
[29]
Hwang EJ, Kim HG, Kim D, et al. Texture analyses of quantitative susceptibility maps to differentiate Alzheimer’s disease from cognitive normal and mild cognitive impairment. Med Phys 2016; 43(8): 4718-28.
[http://dx.doi.org/10.1118/1.4958959] [PMID: 27487889]
[30]
Ruiz E, Ramírez J, Górriz JM, Casillas J. Alzheimer’s Disease Neuroimaging Initiative. Initiative AsDN. Alzheimer’s disease computer-aided diagnosis: Histogram-based analysis of regional MRI volumes for feature selection and classification. J Alzheimers Dis 2018; 65(3): 819-42.
[http://dx.doi.org/10.3233/JAD-170514] [PMID: 29966190]
[31]
Beheshti I, Maikusa N, Matsuda H, Demirel H, Anbarjafari G. Japanese-Alzheimer’s Disease Neuroimaging Initiative. Histogram-based feature extraction from individual gray matter similarity-matrix for Alzheimer’s disease classification. J Alzheimers Dis 2017; 55(4): 1571-82.
[http://dx.doi.org/10.3233/JAD-160850] [PMID: 27886012]
[32]
Martinez-Torteya A, Rodriguez-Rojas J, Celaya-Padilla JM, Galván-Tejada JI, Treviño V, Tamez-Peña J. Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression. J Med Imaging (Bellingham) 2014; 1(3) 031005
[http://dx.doi.org/10.1117/1.JMI.1.3.031005] [PMID: 26158047]
[33]
Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol 2016; 61(13): R150-66.
[http://dx.doi.org/10.1088/0031-9155/61/13/R150] [PMID: 27269645]
[34]
Wu C, Guo S, Hong Y, Xiao B, Wu Y, Zhang Q. Alzheimer’s Disease Neuroimaging Initiative.Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks. Quant Imaging Med Surg 2018; 8(10): 992-1003.
[http://dx.doi.org/10.21037/qims.2018.10.17] [PMID: 30598877]
[35]
Liu M, Cheng D, Wang K, Wang Y. Alzheimer’s Disease Neuroimaging Initiative. Initiative AsDN. Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 2018; 16(3-4): 295-308.
[http://dx.doi.org/10.1007/s12021-018-9370-4] [PMID: 29572601]
[36]
Luk CC, Ishaque A, Khan M, et al. Alzheimer’s Disease Neuroimaging InitiativeAlzheimer’s disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment. Alzheimers Dement (Amst) 2018; 10: 755-63.
[http://dx.doi.org/10.1016/j.dadm.2018.09.002] [PMID: 30480081]
[37]
Cui R, Liu M. Alzheimer’s Disease Neuroimaging Initiative.RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput Med Imaging Graph 2019; 73: 1-10.
[http://dx.doi.org/10.1016/j.compmedimag.2019.01.005] [PMID: 30763637]
[38]
Li F, Liu M. Alzheimer’s Disease Neuroimaging Initiative.Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Comput Med Imaging Graph 2018; 70: 101-10.
[http://dx.doi.org/10.1016/j.compmedimag.2018.09.009] [PMID: 30340094]
[39]
Spasov S, Passamonti L, Duggento A, Liò P, Toschi N. Alzheimer’s Disease Neuroimaging Initiative.A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. Neuroimage 2019; 189: 276-87.
[http://dx.doi.org/10.1016/j.neuroimage.2019.01.031] [PMID: 30654174]
[40]
Jha D, Kwon GR, Kim JI. Diagnosis of Alzheimer's disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network. J Healthcare Engin 2017. 21: 2017(1): 1-13.
[41]
Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, et al. Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J Alzheimers Dis 2017; (13): 1-15.
[PMID: 28731432]
[42]
Li Y, Jiang J, Shen T, Wu P, Zuo C. Eds.Radiomics features as predictors to distinguish fast and slow progression of Mild Cognitive Impairment to Alzheimer’s disease. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[http://dx.doi.org/10.1109/EMBC.2018.8512273]
[43]
Zhou H, Jiang J, Lu J, Wang M, Zhang H, Zuo C. et al.Dual-model Radiomic biomarkers predict development of Mild Cognitive Impairment progression to Alzheimer’s disease. Front Neurosci 2019; 12: 1045.
[PMID: 30686995]
[44]
Lin W, Tong T, Gao Q, et al. Alzheimer’s Disease Neuroimaging Initiative. Convolutional neural networks-Based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Front Neurosci 2018; 12: 777.
[http://dx.doi.org/10.3389/fnins.2018.00777] [PMID: 30455622]
[45]
Shen T, Li Y, Wu P, Zuo C, Yan Z. Eds Decision supporting model for one-year conversion probability from MCI to AD using CNN and SVM. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[http://dx.doi.org/10.1109/EMBC.2018.8512398]
[46]
Feng Q, Song Q, Wang M, et al. Hippocampus radiomic biomarkers for the diagnosis of amnestic mild cognitive impairment: A machine learning method. Front Aging Neurosci 2019; 11: 323.
[http://dx.doi.org/10.3389/fnagi.2019.00323] [PMID: 31824302]
[47]
Hao X, Bao Y, Guo Y, et al. Alzheimer’s Disease Neuroimaging Initiative.Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer’s disease. Med Image Anal 2020; 60 101625
[http://dx.doi.org/10.1016/j.media.2019.101625] [PMID: 31841947]
[48]
Zhou H, Jiang J, Lu J, Wang M, Zhang H, Zuo C. Alzheimer’s Disease Neuroimaging Initiative.Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer’s Disease. Front Neurosci 2019; 12: 1045.
[http://dx.doi.org/10.3389/fnins.2018.01045] [PMID: 30686995]
[49]
Li Y, Jiang J, Lu J, Jiang J, Zhang H, Zuo C. Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment. Ther Adv Neurol Disorder 2019; 12 1756286419838682
[http://dx.doi.org/10.1177/1756286419838682] [PMID: 30956687]
[50]
Oh K, Chung Y-C, Kim KW, Kim W-S, Oh I-S. Classification and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci Rep 2019; 9(1): 18150.
[http://dx.doi.org/10.1038/s41598-019-54548-6] [PMID: 31796817]
[51]
Liu Y, Li Z, Ge Q, Lin N, Xiong M. Deep feature selection and causal analysis of alzheimer’s disease. Front Neurosci 2019; 13: 1198.
[http://dx.doi.org/10.3389/fnins.2019.01198] [PMID: 31802999]

Rights & Permissions Print Cite
Article Metrics
69
2
1
© 2024 Bentham Science Publishers | Privacy Policy