MRI Radiomics Classification and Prediction in Alzheimer’s Disease and Mild Cognitive Impairment: A Review

Author(s): Qi Feng, Zhongxiang Ding*

Journal Name: Current Alzheimer Research

Volume 17 , Issue 3 , 2020


  Journal Home
Translate in Chinese
Become EABM
Become Reviewer
Call for Editor

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.

Keywords: Alzheimer’s disease, mild cognitive impairment, MR imaging, radiomics, texture analysis, classification.

[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 & PermissionsPrintExport Cite as

Article Details

VOLUME: 17
ISSUE: 3
Year: 2020
Published on: 17 May, 2020
Page: [297 - 309]
Pages: 13
DOI: 10.2174/1567205017666200303105016
Price: $65

Article Metrics

PDF: 35
HTML: 3