Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A1A01071648).
References
- Hofman A, Rocca WA, Brayne C, Breteler MM, Clarke M, Cooper B, et al. The prevalence of dementia in Europe: a collaborative study of 1980-1990 findings. Eurodem Prevalence Research Group. Int J Epidemiol 1991;20:736-748 https://doi.org/10.1093/ije/20.3.736
- Veitch DP, Weiner MW, Aisen PS, Beckett LA, Cairns NJ, Green RC, et al. Understanding disease progression and improving Alzheimer's disease clinical trials: recent highlights from the Alzheimer's disease neuroimaging initiative. Alzheimers Dement 2019;15:106-152 https://doi.org/10.1016/j.jalz.2018.08.005
- Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6:734-746 https://doi.org/10.1016/S1474-4422(07)70178-3
- Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol 2014;13:614-629 https://doi.org/10.1016/S1474-4422(14)70090-0
- Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement 2018;14:535-562
- Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 2010;9:119-128 https://doi.org/10.1016/S1474-4422(09)70299-6
- Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563-577 https://doi.org/10.1148/radiol.2015151169
- Kang D, Park JE, Kim YH, Kim JH, Oh JY, Kim J, et al. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol 2018;20:1251-1261 https://doi.org/10.1093/neuonc/noy021
- Kickingereder P, Burth S, Wick A, Gotz M, Eidel O, Schlemmer HP, 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;280:880-889 https://doi.org/10.1148/radiol.2016160845
- Park YW, Choi YS, Ahn SS, Chang JH, Kim SH, Lee SK. Radiomics MRI phenotyping with machine learning to predict the grade of lower-grade gliomas: a study focused on nonenhancing tumors. Korean J Radiol 2019;20:1381-1389 https://doi.org/10.3348/kjr.2018.0814
- Park YW, Han K, Ahn SS, Choi YS, Chang JH, Kim SH, et al. Whole-tumor histogram and texture analyses of DTI for evaluation of IDH1-mutation and 1p/19q-codeletion status in World Health Organization grade II gliomas. AJNR Am J Neuroradiol 2018;39:693-698 https://doi.org/10.3174/ajnr.A5569
- Chaddad A, Desrosiers C, Niazi T. Deep radiomic analysis of MRI related to Alzheimer's disease. IEEE Access 2018;6:58213-58221 https://doi.org/10.1109/ACCESS.2018.2871977
- Maani R, Yang YH, Kalra S. Voxel-based texture analysis of the brain. PLoS One 2015;10:e0117759
- Zhang J, Yu C, Jiang G, Liu W, Tong L. 3D texture analysis on MRI images of Alzheimer's disease. Brain Imaging Behav 2012;6:61-69 https://doi.org/10.1007/s11682-011-9142-3
- Rajeesh J, Moni RS, Gopalakrishnan T. Discrimination of Alzheimer's disease using hippocampus texture features from MRI. Asian Biomedicine 2012;6:87-94
- Freeborough PA, Fox NC. MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease. IEEE Trans Med Imaging 1998;17:475-479 https://doi.org/10.1109/42.712137
- Feng Q, Chen Y, Liao Z, Jiang H, Mao D, Wang M, et al. Corpus callosum radiomics-based classification model in Alzheimer's disease: a case-control study. Front Neurol 2018;9:618
- Cai JH, He Y, Zhong XL, Lei H, Wang F, Luo GH, et al. Magnetic resonance texture analysis in Alzheimer's disease. Acad Radiol 2020 Feb 10 [Epub]. https://doi.org/10.1016/j.acra.2020.01.006
- 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
- Li H, Habes M, Wolk DA, Fan Y; Alzheimer's Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Study of Aging. A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data. Alzheimers Dement 2019;15:1059-1070 https://doi.org/10.1016/j.jalz.2019.02.007
- Lee S, Lee H, Kim KW; Alzheimer's Disease Neuroimaging Initiative. Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume. J Psychiatry Neurosci 2020;45:7-14 https://doi.org/10.1503/jpn.180171
- Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14:749-762 https://doi.org/10.1038/nrclinonc.2017.141
- Waterton JC, Pylkkanen L. Qualification of imaging biomarkers for oncology drug development. Eur J Cancer 2012;48:409-415 https://doi.org/10.1016/j.ejca.2011.11.037
- Sanduleanu S, Woodruff HC, De Jong EEC, Van Timmeren JE, Jochems A, Dubois L, et al. Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol 2018;127:349-360 https://doi.org/10.1016/j.radonc.2018.03.033
- Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, et al. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 2020;30:523-536 https://doi.org/10.1007/s00330-019-06360-z
- Park JE, Kim HS, Kim D, Park SY, Kim JY, Cho SJ, et al. A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features. BMC Cancer 2020;20:29
- Oppedal K, Eftestol T, Engan K, Beyer MK, Aarsland D. Classifying dementia using local binary patterns from different regions in magnetic resonance images. Int J Biomed Imaging 2015;2015:572567
- Ranjbar S, Velgos SN, Dueck AC, Geda YE, Mitchell JR; Alzheimer's Disease Neuroimaging Initiative. Brain MR radiomics to differentiate cognitive disorders. J Neuropsychiatry Clin Neurosci 2019;31:210-219 https://doi.org/10.1176/appi.neuropsych.17120366
- Sorensen L, Igel C, Liv Hansen N, Osler M, Lauritzen M, Rostrup E, et al. Early detection of Alzheimer's disease using MRI hippocampal texture. Hum Brain Mapp 2016;37:1148-1161 https://doi.org/10.1002/hbm.23091
- Ben Bouallegue F, Vauchot F, Mariano-Goulart D, Payoux P. Diagnostic and prognostic value of amyloid PET textural and shape features: comparison with classical semi-quantitative rating in 760 patients from the ADNI-2 database. Brain Imaging Behav 2019;13:111-125 https://doi.org/10.1007/s11682-018-9833-0
- Hett K, Ta VT, Manjon JV, Coupe P; Alzheimer's Disease Neuroimaging Initiative. Adaptive fusion of texture-based grading for Alzheimer's disease classification. Comput Med Imaging Graph 2018;70:8-16 https://doi.org/10.1016/j.compmedimag.2018.08.002
- Rohini P, Sundar S, Ramakrishnan S. Characterization of Alzheimer conditions in MR images using volumetric and sagittal brainstem texture features. Comput Methods Programs Biomed 2019;173:147-155 https://doi.org/10.1016/j.cmpb.2019.03.003
- Tozer DJ, Zeestraten E, Lawrence AJ, Barrick TR, Markus HS. Texture analysis of T1-weighted and fluid-attenuated inversion recovery images detects abnormalities that correlate with cognitive decline in small vessel disease. Stroke 2018;49:1656-1661 https://doi.org/10.1161/STROKEAHA.117.019970
- De Oliveira MS, Balthazar ML, D'abreu A, Yasuda C, Damasceno B, Cendes F, 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;32:60-66 https://doi.org/10.3174/ajnr.A2232
- Lopez-Gomez C, Ortiz-Ramon R, Molla-Olmos E, Moratal D; Alzheimer's Disease Neuroimaging Initiative, Initiative AsDN. ALTEA: a software tool for the evaluation of new biomarkers for Alzheimer's disease by means of textures analysis on magnetic resonance images. Diagnostics (Basel) 2018;8:47
- Hwang EJ, Kim HG, Kim D, Rhee HY, Ryu CW, Liu T, et al. Texture analyses of quantitative susceptibility maps to differentiate Alzheimer's disease from cognitive normal and mild cognitive impairment. Med Phys 2016;43:4718
- 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
- Gao N, Tao LX, Huang J, Zhang F, Li X, O'Sullivan F, et al. Contourlet-based hippocampal magnetic resonance imaging texture features for multivariant classification and prediction of Alzheimer's disease. Metab Brain Dis 2018;33:1899-1909 https://doi.org/10.1007/s11011-018-0296-1
- Martinez-Torteya A, Rodriguez-Rojas J, Celaya-Padilla JM, Galvan-Tejada JI, Trevino V, Tamez-Pena 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;1:031005
- Feng Q, Song Q, Wang M, Pang P, Liao Z, Jiang H, et al. Hippocampus radiomic biomarkers for the diagnosis of amnestic mild cognitive impairment: a machine learning method. Front Aging Neurosci 2019;11:323
- Vaithinathan K, Parthiban L; Alzheimer's Disease Neuroimaging Initiative. A novel texture extraction technique with T1 weighted MRI for the classification of Alzheimer's disease. J Neurosci Methods 2019;318:84-99 https://doi.org/10.1016/j.jneumeth.2019.01.011
- 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 Disord 2019;12:1756286419838682
- Achterberg HC, Sorensen L, Wolters FJ, Niessen WJ, Vernooij MW, Ikram MA, et al. The value of hippocampal volume, shape, and texture for 11-year prediction of dementia: a population-based study. Neurobiol Aging 2019;81:58-66 https://doi.org/10.1016/j.neurobiolaging.2019.05.007
- Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 2008;27:685-691 https://doi.org/10.1002/jmri.21049
- Saykin AJ, Shen L, Yao X, Kim S, Nho K, Risacher SL, et al. Genetic studies of quantitative MCI and AD phenotypes in ADNI: progress, opportunities, and plans. Alzheimers Dement 2015;11:792-814 https://doi.org/10.1016/j.jalz.2015.05.009
- Den Heijer T, Oudkerk M, Launer LJ, Van Duijn CM, Hofman A, Breteler MM. Hippocampal, amygdalar, and global brain atrophy in different apolipoprotein E genotypes. Neurology 2002;59:746-748 https://doi.org/10.1212/WNL.59.5.746
- Moffat SD, Szekely CA, Zonderman AB, Kabani NJ, Resnick SM. Longitudinal change in hippocampal volume as a function of apolipoprotein E genotype. Neurology 2000;55:134-136 https://doi.org/10.1212/WNL.55.1.134
- Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019;20:1124-1137 https://doi.org/10.3348/kjr.2018.0070
- Boccardi M, Gallo V, Yasui Y, Vineis P, Padovani A, Mosimann U, et al. The biomarker-based diagnosis of Alzheimer's disease. 2-lessons from oncology. Neurobiol Aging 2017;52:141-152 https://doi.org/10.1016/j.neurobiolaging.2017.01.021