Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer's Disease: A Systematic Review of Studies in the Field

Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis

OBJECTIVE

FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls.

METHODS

18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomics analysis was performed on the whole brain after normalization to an optimized template. After selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, the features obtained were added to a neural network model to find the accuracy in classifying HC and demented patients. Forty subjects not included in the training were used to test the models. The results of the three models (radiomics, 3D-CNN, combined model) were compared with each other.

RESULTS

Four radiomics features were selected. The sensitivity was 100% for the three models, but the specificity was higher with radiomics and combined one (100% vs. 85%). Moreover, the classification scores were significantly higher using the combined model in both normal and demented subjects.

CONCLUSION

The combination of radiomics features and 3D-CNN in a single model, applied to the whole brain 18FDG PET study, increases the accuracy in demented patients.

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging

Purpose

Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs).

Method

This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models.

Result

Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

Conclusion

The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

Early Detection of Alzheimer's Disease in Postmenopausal Women Using Thalamic Subnuclear Volumetry

Alzheimer's disease (AD) and aging are intrinsically interconnected with each other and are mediated by molecular, cellular, and biological systems. In particular, a specific pattern of brain volume atrophy is the most profound risk factor for cognitive impairment, including AD, that is directly linked to aging. Thus, this study aimed to investigate knowledge on the early detection of AD in postmenopausal women, focusing on the volume changes of the subcortical regions, including the thalamic subnuclei, in women with AD vs. postmenopausal women. Twenty-one women with AD and twenty-one postmenopausal women without AD underwent magnetic resonance imaging (MRI). Women with AD showed significantly reduced volumes in the hippocampus, thalamus, and amygdala compared with postmenopausal women (p < 0.05, FWE-corrected). After adjustments for age, the right hippocampal volume was found to be significantly lower in the women with AD, but the volumes of the thalamus and amygdala were relatively unaffected. The women with AD exhibited significantly reduced volume in the right laterodorsal nucleus of the thalamus compared with the postmenopausal women (p < 0.05, Bonferroni-corrected). Our findings suggest that the reduced volume of both the right laterodorsal thalamic nucleus and right hippocampus may serve as a potential biomarker for the early detection of AD in postmenopausal women.