Contourlet-based hippocampal magnetic resonance imaging texture features for multivariant classification and prediction of Alzheimer’s disease

• Published in: Metabolic brain disease 2018-12, Vol.33 (6), p.1899-1909
• Main Authors: Gao, Ni, Tao, Li-Xin, Huang, Jian, Zhang, Feng, Li, Xia, O’Sullivan, Finbarr, Chen, Si-Peng, Tian, Si-Jia, Mahara, Gehendra, Luo, Yan-Xia, Gao, Qi, Liu, Xiang-Tong, Wang, Wei, Liang, Zhi-Gang, Guo, Xiu-Hua
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer's disease; Biochemistry; Biomedical and Life Sciences; Biomedicine; Classification; Cognitive ability; Conversion; Emission measurements; Fluorine; Gaussian process; Hippocampus; Magnetic resonance imaging; Mathematical models; Medical imaging; Metabolic Diseases; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Oncology; Original Article; Parameters; Patients; Positron emission; Positron emission tomography; Predictions; Regional development; Resonance; Texture
• ISSN: 0885-7490; 1573-7365
• DOI: 10.1007/s11011-018-0296-1

The study is aimed to assess whether the addition of contourlet-based hippocampal magnetic resonance imaging (MRI) texture features to multivariant models improves the classification of Alzheimer’s disease (AD) and the prediction of mild cognitive impairment (MCI) conversion, and to evaluate whether Gaussian process (GP) and partial least squares (PLS) are feasible in developing multivariant models in this context. Clinical and MRI data of 58 patients with probable AD, 147 with MCI, and 94 normal controls (NCs) were collected. Baseline contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters based on MRI, and regional CMgl measurement based on fluorine-18 fluorodeoxyglucose-positron emission tomography were included to develop GP and PLS models to classify different groups of subjects. GPR1 model, which incorporated MRI texture features and was based on GPG, performed better in classifying different groups of subjects than GPR2 model, which used the same algorithm and had the same data as GPR1 except that MRI texture features were excluded. PLS model, which included the same variables as GPR1 but was based on the PLS algorithm, performed best among the three models. GPR1 accurately predicted 82.2% (51/62) of MCI convertors confirmed during the 2-year follow-up period, while this figure was 53 (85.5%) for PLS model. GPR1 and PLS models accurately predicted 58 (79.5%) vs. 61 (83.6%) of 73 patients with stable MCI, respectively. For seven patients with MCI who converted to NCs, PLS model accurately predicted all cases (100%), while GPR1 predicted six (85.7%) cases. The addition of contourlet-based MRI texture features to multivariant models can effectively improve the classification of AD and the prediction of MCI conversion to AD. Both GPR and LPS models performed well in the classification and predictive process, with the latter having significantly higher classification and predictive accuracies. Advances in knowledge: We combined contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters, and regional CMgl measurement to develop models using GP and PLS algorithms to classify AD patients.