The Latest Articles on the Applications of Radiomics in Neurodegenerative Diseases
Our staff editors continue to share exciting, interesting, and thought-provoking reading material in the recommended articles series.
This week, we would like to share several latest articles on the applications of radiomics in neurodegenerative diseases.
Title: Research and Application Progress of Radiomics in Neurodegenerative Diseases
Authors: Junbang Feng, Ying Huang, Xiaocai Zhang, Qingning Yang, Yi Guo, Yuwei Xia, Chao Peng, Chuanming Li
Type: Review Article
Abstract:
Neurodegenerative diseases refer to degenerative diseases of the nervous system caused by neuronal degeneration and apoptosis. Usually, the onset of the disease is insidious, and the progression is slow, which can last for several years to decades. Clinical symptoms only appear in the later stages of pathological changes when the degree of nerve cell loss reaches or exceeds a certain threshold. Traditional electrophysiological and medical imaging techniques lack valuable indicators and markers. Therefore, early diagnosis and differentiation are very difficult. Radiomics is a new medical imaging technology merged in recent years, which can extract a large number of invisible features from raw image data with high throughput, and quantitatively analyze the pathological and physiological changes. It demonstrates important potential value in the diagnosis, grading, and prognosis evaluation of NDs. This review provides an overview of the research progress of radiomics in neurodegenerative diseases, emphasizing the process principles of radiomics and its application in the diagnosis, classification, and prediction of these diseases. This helps to deepen the understanding of neurodegenerative diseases and promote early diagnosis and treatment in clinical practice.
Access this article: https://doi.org/10.1016/j.metrad.2024.100068
Title: A review of artificial intelligence methods for Alzheimer's disease diagnosis: Insights from neuroimaging to sensor data analysis
Authors: Ikram Bazarbekov, Abdul Razaque, Madina Ipalakova, Joon Yoo, Zhanna Assipova, Ali Almisreb
Type: Review Article
Abstract
Alzheimer's disease is the most common cause of dementia, gradually impairing memory, intellectual, learning, and organizational capacities. An individual's capacity to perform fundamental daily tasks is greatly impacted. This review examines the advancements in diagnosing Alzheimer's disease (AD) using artificial intelligence (AI) methods and machine learning (ML) algorithms. The review introduces the importance of diagnosing AD accurately and the potential benefits of using AI techniques and machine learning algorithms for this purpose. The review is based on various state-of-the-art data sources including MRI data, PET imaging, EEG and MEG signals, and data from various sensors. The state-of-the-art radiomics approaches are explored to extract a wide range of information from medical images using data-characterization algorithms. These features can show temporal patterns and qualities that are not visible to the human eye. A novel data source (handwriting data) is thoroughly investigated and coupled with AI algorithms for the precise and early detection of cognitive loss associated with Alzheimer's disease. The paper discusses research directions, prospects, and future advances, as well as the proposed notion of employing a Robopen with an MPU-9250 sensor connected via Arduino. Finally, the review concludes with a summary of its significant findings and their clinical implications.
Access this article: https://doi.org/10.1016/j.bspc.2024.106023
Title: Predicting amyloid positivity from FDG-PET images using radiomics: A parsimonious model
Authors: Ramin Rasi, Albert Guvenis, Alzheimer's Disease Neuroimaging Initiative
Type: Research Article
Abstract:
Background and Objective
Amyloid plaques are one of the physical hallmarks of Alzheimer's disease. The objective of this study is to predict amyloid positivity non-invasively from FDG-PET images using a radiomics approach.
Methods
We obtained FDG-PET images of 301 individuals from various groups, including control normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD), from the ADNI database. Following the utilization of the CSF Aβ1-42 (192) and Standardized Uptake Value Ratio (SUVR) (1.11) thresholds derived from Florbetapir scans, the subjects were categorized into two categories: those with a positive amyloid status (n=185) and those with a negative amyloid status (n=116). The process of segmenting the entire brain into 95 classes using the DKT-atlas was utilized. Following that, we obtained 120 characteristics for each of the 95 regions of interest (ROIs). We employed eight feature selection methods to analyze the features. Additionally, we utilized eight different classifiers on the 20 most significant features extracted from each feature selection method. Finally, in order to improve interpretability, we selected the most important features and ROIs.
Result
We found that the GNB classifier and the LASSO feature selection method had the best performance with an average accuracy of (AUC=0.924) while using 18 features on 15 ROIs. We were then able to reduce the model to three regions (Hippocampus, inferior parietal, and isthmus cingulate) and three gray-level based features (AUC=0.853).
Conclusion
The FDG-PET images which serve to study metabolic activity can be used to predict amyloid positivity without the use of invasive methods or another PET tracer and study. The proposed method has superior prediction accuracy with respect to similar studies reported in the literature using other imaging modalities. Only three brain regions had a high impact on amyloid positivity results.
Access this article: https://doi.org/10.1016/j.cmpb.2024.108098
Title: Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders
Authors: Lixin Du, Shubham Roy, Pan Wang, Zhigang Li, Xiaoting Qiu, Yinghe Zhang, Jianpeng Yuan, Bing Gu
Type: Review Article
Abstract:
Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.
Access this article: https://doi.org/10.1016/j.arr.2024.102230
Title: Neuroimaging of Parkinson's disease by quantitative susceptibility mapping
Authors: Xiaojun Guan, Marta Lancione, Scott Ayton, Petr Dusek, Christian Langkammer, Minming Zhanga
Type: Research Article
Abstract:
Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.
Access this article: https://doi.org/10.1016/j.neuroimage.2024.120547
