A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features

Stephanos Leandrou; Demetris Lamnisos; Haralabos Bougias; Nikolaos Stogiannos; Eleni Georgiadou; K G Achilleos; Constantinos S Pattichis; Alzheimer’s Disease Neuroimaging Initiative

doi:10.3389/fnagi.2023.1149871

A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features

Front Aging Neurosci. 2023 Jun 7:15:1149871. doi: 10.3389/fnagi.2023.1149871. eCollection 2023.

Authors

Stephanos Leandrou¹, Demetris Lamnisos¹, Haralabos Bougias², Nikolaos Stogiannos^{3

4

5}, Eleni Georgiadou⁶, K G Achilleos⁷, Constantinos S Pattichis^{7

8}; Alzheimer’s Disease Neuroimaging Initiative

Affiliations

¹ School of Sciences, European University Cyprus, Nicosia, Cyprus.
² University Hospital of Ioannina, Ioannina, Greece.
³ Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland.
⁴ Division of Midwifery and Radiography, City, University of London, London, United Kingdom.
⁵ Medical Imaging Department, Corfu General Hospital, Corfu, Greece.
⁶ Metaxa Anticancer Hospital, Athens, Greece.
⁷ Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus.
⁸ CYENS Centre of Excellence, Nicosia, Cyprus.

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.

Keywords: Alzheimer’s disease; MRI; explainability and interpretability; machine learning (ML); radiomic.

Grants and funding

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number: W81XWH-12-2-0012). ADNI was funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc., Biogen; Bristol-Myers Squibb Company; CereSpir, Inc., Cogstate; Eisai Inc., Elan Pharmaceuticals, Inc., Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd.,; and its affiliated company Genentech, Inc.,; Fujirebio; GE HealthCare; IXICO Ltd., Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc., Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc., Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.