Quantitative texture analysis in MR imaging in the assessment of Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease which is clinically characterized by cognitive impairment and memory loss. Anatomically, AD initially affects specific structures within the Medial Temporal Lobe (MTL), which are essential for declarative memory. A definitive diagnosis of AD relies on post-mortem biopsy therefore, clinical assessment and cognitive tests are currently used. However, these tests are not sensitive to detect AD in an early stage. The aim of this research was to investigate the usefulness of quantitative Magnetic Resonance Imaging (MRI) and specifically of texture features in the assessment of Mild Cognitive Impairment (MCI) which is the pre-dementia stage and AD. Firstly, two types of magnetic fields where investigated in order to examine whether, a stronger MR magnetic field would benefit quantitative imaging analysis derived from texture features. Secondly, texture features were extracted from the entorhinal cortex and evaluated in the diagnosis and prediction of MCI and AD. To the best of our knowledge this is the first research that investigated how the MR field strength affects texture features and used entorhinal cortex texture features on the assessment of AD. The main results of this PhD showed that (1) texture features could provide more sensitive measures when they are extracted from stronger MRI magnetic field, such as 3T, compared to 1.5T. From a disease classification and prediction perspective, (2) entorhinal cortex texture features provide better classification between Normal Controls (NC), MCI and AD subjects, and (3) better prediction of the conversion from MCI to AD. In conclusion, this research has shown for the first time in the literature that entorhinal cortex texture features from MRI could contribute towards the early classification of AD

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's disease: A Methodological Review

Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging especially in Mild Cognitive Impairment (MCI) subjects. Quantitative structural Magnetic Resonance Imaging (MRI) acquisition methods in combination with Computer-Aided Diagnosis (CAD) are currently being used for the assessment AD. These acquisitions methods include: i) Voxel-based Morphometry (VBM), ii) volumetric measurements in specific Regions of Interest (ROIs), iii) cortical thickness measurements, iv) shape analysis and v) texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following 3 groups: Normal Controls (NC), MCI and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the Medial Temporal Lobe (MTL) especially in the entorhinal cortex, whereas with disease progression both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus

An overview of quantitative magnetic resonance imaging analysis studies in the assessment of alzheimer’s disease

Medical image analysis and visualization, can contribute in quantitative and qualitative analysis of Magnetic Resonance Imaging (MRI) towards an earlier diagnosis of Alzheimer’s disease (AD). Moreover, the early detection of Mild Cognitive Impairment (MCI) has recently attracted a lot of attention. The main objective of this paper is to present a survey of recent key papers focused on the classification of MCI and AD and the prediction of conversion from MCI to AD using volume, shape and texture analysis. The most frequent anatomical features used in the assessment of AD, is the hippocampus, the cortex and the local concentration of grey matter. Shape analysis can identify the signs of early hippocampal atrophy, whereas volume analysis evaluates the structure as a whole. Shape analysis seems to be a more accurate technique both in classification of patients and in prognostic prediction. Compared to volume, shape and voxel based morphometry (VBM) techniques, texture analysis can be used to identify the microstructural changes before the larger-scale morphological characteristics which are detected by the other aforementioned techniques. We concluded that quantitative MRI measurements can be used as an in vivo surrogate for the classification of patients and furthermore, for the tracking the Alzheimer’s disease progression

Hippocampal and entorhinal cortex volume changes in Alzheimer's disease patients and mild cognitive impairment subjects.

Hippocampal and entorhinal cortex as scanned in Magnetic Resonance Imaging (MRI), are two of the most commonly used Regions of Interest (ROIs) for the assessment of Alzheimer’s disease (AD). Both structures are used for the classification between Normal Controls (NC), Mild Cognitive Impairment (MCI) and AD subjects and for the disease prognosis. The objective of this study was to evaluate how the volume of these two structures changes between the following groups: NC vs AD, NC vs MCI, MCI vs MCI converters (MCIc - subjects who had converted to AD within 48 months), and AD vs MCIc subjects. Both structures were significantly reduced in volume for MCIc and AD subjects compared to NC. For both MCI and MCIc groups, the atrophy rate was correlated for both structures. In AD subjects, entorhinal cortex was more affected by atrophy. In conclusion, structural MRI and volumetric measurements of the hippocampus and entorhinal cortex can be used as early signs for the assessment of AD, and this is in agreement with previous studies

Corrigendum: Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex

[This corrects the article DOI: 10.3389/fnagi.2020.00176.]

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

IntroductionAlzheimer’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.MethodsIn 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.ResultsThe 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.DiscussionThese 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

Comparison of 1.5 T and 3 T MRI hippocampus texture features in the assessment of Alzheimer's disease

Objective: Many studies evaluated how the Magnetic Resonance Imaging (MRI) field strength affects the effectiveness to detect neurodegenerative changes of Alzheimer's disease (AD), derived from atrophy or thickness. To the best of our knowledge, no study evaluated before how tissue texture changes are affected. In this research, hippocampus texture features extracted from 1.5 T and 3 T MRI are evaluated how are affected by the magnetic field strength. Methods: MR imaging data from 14 Normal Controls (NC), 14 with Mild Cognitive Impairment (MCI), 11 MCI converters (MCIc) and 10 CE subjects scanned at 1.5 T and 3 T were included. Haralick's texture features were extracted from the hippocampus, along with hippocampal and amygdala volumes and cortical thickness. One-way ANOVA, paired-samples and Wilcoxon signed t-tests were used to evaluate if there were significant differences between the features. Results: 3 T texture features were significantly different for NC vs AD, NC vs MCI and MCI vs AD, whereas, 1.5 T for MCI vs AD only. Amygdala and hippocampal volumes, showed significant differences for NC vs AD for both MRI strengths, whereas cortical thickness for MCI vs MCIc for the 3 T. Paired sample t-test and Wilcoxon signed-rank test revealed significant differences for Angular Second Moment (ASM), contrast, correlation, variance, sum variance and entropy, the amygdala volume and cortical thickness. Between NC vs MCI, 3 T texture revealed higher Area Under Curve (AUC). Conclusion: 3 T texture revealed significant differences for more features compared to 1.5 T, whereas, atrophy and thickness had similar results. Significance: 3 T texture changes provide earlier diagnosis compared to 1.5 T volume or texture changes

Polymorphisms of Caspase 8 and Caspase 9 gene and colorectal cancer susceptibility and prognosis

Purpose: Caspase-8 (CASP8) and caspase-9 (CASP9) play crucial roles in regulating apoptosis, and their functional polymorphisms may alter cancer risk. Our aim was to investigate the association between CASP8 and CASP9 gene polymorphisms and colorectal cancer (CRC) susceptibility. Methods: A case-control study at 402 CRC patients and 480 healthy controls was undertaken in order to investigate the association between the genotype and allelic frequencies of CASP8 -652 6N ins/del and CASP9 -1263 A>G polymorphisms and the CRC susceptibility. The polymerase chain reaction (PCR) restriction fragment length polymorphism method was used and the incidence of polymorphisms on messenger RNA (mRNA) expression levels was detected by quantitative reverse-transcriptase PCR in CRC tissues. Results: No statistical significant association was observed between CASP8 -652 6N ins/del polymorphism frequencies and CRC susceptibility. CASP9 -1263 G allele was observed to be significant associated with reduced risk of CRC. Homozygotes for the -1263 GG CASP9 genotype, and hetrozygotes for the -1263 AG genotype expressed 6.64- and 3.69-fold higher mRNA levels of Caspase-9, respectively compared to the -1263 AA genotype cases. No significant association was observed between CASP9 -1263 A>G polymorphism and tumor characteristics. The CASP9 -1263 GG genotype was associated with increased overall survival in CRC patients. Conclusion: The CASP9 -1263 A>G polymorphism was observed to play a protective role in CRC predisposition, while the CASP9 -1263 GG genotype may confer a better prognosis at CRC patients. © 2011 Springer-Verlag