Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern
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Objective Alzheimer’s disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient’s severity of neurodegeneration independent from the patient’s clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).Methods We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer’s which is a commonly used segmentation software.Results Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6–8 hours).Conclusion Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
Abstract
Objective Alzheimer’s disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient’s severity of neurodegeneration independent from the patient’s clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).Methods We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer’s which is a commonly used segmentation software.Results Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6–8 hours).Conclusion Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
참고문헌 (42)
- Oboudiyat C, "Alzheimer’s disease", Semin Neurol, 33, pp.313~329 (2013)
- Lane CA, "Alzheimer’s disease", Eur J Neurol, 25, pp.59~70 (2018)
- DeTure MA, "The neuropathological diagnosis of Alzheimer’s disease", Mol Neurodegener, 14, pp.32~ (2019)
- Robillard A, "Clinical diagnosis of dementia", Alzheimers Dement, 3, pp.292~298 (2007)
- Sperling RA, "Toward defining the preclinical stages of Alzheimer’s disease : recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease", Alzheimers Dement, 7, pp.280~292 (2011)
- Marquez F, "Neuroimaging biomarkers for Alzheimer’s disease", Mol Neurodegener, 14, pp.21~ (2019)
- Jack Jr CR, "NIA-AA research framework : toward a biological definition of Alzheimer’s disease", Alzheimers Dementia, 14, pp.535~562 (2018)
- de Flores R, "Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease", Neuroscience, 309, pp.29~50 (2015)
- Eskildsen SF, "Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression", Neurobiol Aging, 36(Suppl 1), pp.S23~S31 (2015)
- 박미나, "Structural MR Imaging in the Diagnosis of Alzheimer’s Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives", Korean Journal of Radiology, 17(6), pp.827~845 (2016), 대한영상의학회[DOI]
- 문석우, "Changes in the Hippocampal Volume and Shape in Early-Onset Mild Cognitive Impairment", PSYCHIATRY INVESTIGATION, 15(5), pp.531~537 (2018), 대한신경정신의학회
- Weiner MW, "The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception", Alzheimers Dement, 8, pp.S1~S68 (2012)
- Erickson BJ, "Machine learning for medical imaging", Radiographics, 37, pp.505~515 (2017)
- Bratić B, "Machine learning for predicting cognitive diseases : methods, data sources and risk factors", J Med Syst, 42, pp.243~ (2018)
- Bryan RN, "Machine Learning Applied to Alzheimer Disease" (2016), Radiological Society of North America
- Kim J, "Automated discrimination of dementia spectrum disorders using extreme learning machine and structural t1 mri features", Annu Int Conf IEEE Eng Med Biol Soc, 2017, pp.1990~1993 (2017)
- Mirzaei G, "Imaging and machine learning techniques for diagnosis of Alzheimer’s disease", Rev Neurosci, 27, pp.857~870 (2016)
- Pellegrini E, "Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review", Alzheimers Dement(Amst), 10, pp.519~535 (2018)
- Salvatore C, "Frontiers for the early diagnosis of AD by means of MRI brain imaging and support vector machines", Curr Alzheimer Res, 13, pp.509~533 (2016)
- Sorensen L, "Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination", J Neurosci Methods, 302, pp.66~74 (2018)
- Zhao M, "Trace ratio linear discriminant analysis for medical diagnosis : a case study of dementia", IEEE Signal Process Lett, 20, pp.431~434 (2013)
- Dimitriadis SI, "Random forest feature selection, fusion and ensemble strategy : combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer’s disease patients : from the alzheimer’s disease neuroimaging initiative(ADNI)database", J Neurosci Methods, 302, pp.14~23 (2018)
- Sarica A, "Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease : a systematic review", Front Aging Neurosci, 9, pp.329~ (2017)
- Lebedeva AK, "MRI-based classification models in prediction of mild cognitive impairment and dementia in late-life depression", Front Aging Neurosci, 9, pp.13~ (2017)
- Kang DW, "Impact of amyloid burden on regional functional synchronization in the cognitively normal older adults", Sci Rep, 7, pp.1~9 (2017)
- 한창태, "Thalamic Shape and Cognitive Performance in Amnestic Mild Cognitive Impairment", PSYCHIATRY INVESTIGATION, 13(5), pp.504~510 (2016), 대한신경정신의학회[DOI]
- Fischl B, "FreeSurfer", NeuroImage, 62, pp.774~781 (2012)
- Liaw A, "Classification and regression by randomForest", R News, 2, pp.18~22 (2002)
- Buitinck L, "API design for machine learning software: experiences from the scikit-learn project", arXiv preprint arXiv:1309.0238, ~
- Jin Bong Choi, "The Effect of Daily Low Dose Tadalafil on Cerebral Perfusion and Cognition in Patients with Erectile Dysfunction and Mild Cognitive Impairment", Clinical Psychopharmacology and Neuroscience, 17(3), pp.432~437 (2019), 대한정신약물학회[DOI]
- Ji Won Han, "Overview of the Korean Longitudinal Study on Cognitive Aging and Dementia", PSYCHIATRY INVESTIGATION, 15(8), pp.767~774 (2018), 대한신경정신의학회[DOI]
- Mak E, "Progressive cortical thinning and subcortical atrophy in dementia with Lewy bodies and Alzheimer’s disease", Neurobiol Aging, 36, pp.1743~1750 (2015)
- Fan LY, "The relation between brain amyloid deposition, cortical atrophy, and plasma biomarkers in amnesic mild cognitive impairment and Alzheimer’s disease", Front Aging Neurosci, 10, pp.175~ (2018)
- Won Sang Jung, "Diagnostic Validity of an Automated Probabilistic Tractography in Amnestic Mild Cognitive Impairment", Clinical Psychopharmacology and Neuroscience, 16(2), pp.144~152 (2018), 대한정신약물학회[DOI]
- Palumbo L, "Evaluation of the intra-and inter-method agreement of brain MRI segmentation software packages : a comparison between SPM12 and FreeSurfer v6. 0", Physica Medica, 64, pp.261~272 (2019)
- Ahmed MR, "Neuroimaging and machine learning for dementia diagnosis : recent advancements and future prospects", IEEE Rev Biomed Eng, 12, pp.19~33 (2018)
- Álvarez JD, "An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders", BMC Bioinformatics, 20, pp.491~ (2019)
- Lee JS, "Machine learning-based individual assessment of cortical atrophy pattern in Alzheimer’s disease spectrum : development of the classifier and longitudinal evaluation", Sci Rep, 8, pp.4161~ (2018)
- Portet F, "Mild cognitive impairment(MCI)in medical practice : a critical review of the concept and new diagnostic procedure. Report of the MCI Working Group of the European Consortium on Alzheimer’s Disease", J Neurol Neurosurg Psychiatry, 77, pp.714~718 (2006)
- Cure S, "Systematic literature review and meta-analysis of diagnostic test accuracy in Alzheimer’s disease and other dementia using autopsy as standard of truth", J Alzheimers Dis, 42, pp.169~182 (2014)
- Moscoso A, "Prediction of Alzheimer’s disease dementia with MRI beyond the short-term : Implications for the design of predictive models", NeuroImage Clin, 23, pp.101837~ (2019)
- Chandra A, "Magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment", J Neurol, 266, pp.1293~1302 (2019)