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Das SR, Ilesanmi A, Wolk DA, Gee JC. Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ? Magn Reson Med Sci 2024; 23:367-376. [PMID: 38880615 PMCID: PMC11234947 DOI: 10.2463/mrms.rev.2024-0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.
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Affiliation(s)
- Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ademola Ilesanmi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - James C. Gee
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Mao S, Huang X, Chen R, Zhang C, Diao Y, Li Z, Wang Q, Tang S, Guo S. STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data. Brief Bioinform 2024; 25:bbae051. [PMID: 38385881 PMCID: PMC10883420 DOI: 10.1093/bib/bbae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.
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Affiliation(s)
- Shanjun Mao
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Xiao Huang
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Runjiu Chen
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Chenyang Zhang
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Yizhu Diao
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Zongjin Li
- Central University of Finance and Economics
| | - Qingzhe Wang
- Shanghai Institute for Advanced Studies, University of Science and Technology of China
| | - Shan Tang
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Lushan Road, Changsha 410000, China
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Shahidi R, Baradaran M, Asgarzadeh A, Bagherieh S, Tajabadi Z, Farhadi A, Korani SS, Khalafi M, Shobeiri P, Sadeghsalehi H, Shafieioun A, Yazdanifar MA, Singhal A, Sotoudeh H. Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis. Aging Clin Exp Res 2023; 35:2333-2348. [PMID: 37801265 DOI: 10.1007/s40520-023-02565-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/13/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. METHODS AND MATERIALS A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. RESULTS We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. CONCLUSION MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.
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Affiliation(s)
- Ramin Shahidi
- School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Mansoureh Baradaran
- Department of Radiology, Imam Ali Hospital, North Khorasan University of Medical Science, Bojnurd, Iran
| | - Ali Asgarzadeh
- Students Research Committee, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Sara Bagherieh
- Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zohreh Tajabadi
- Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Akram Farhadi
- Faculty of Health, Bushehr University of Medical Sciences, Bushehr, Iran
| | | | - Mohammad Khalafi
- Department of Radiology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Hamidreza Sadeghsalehi
- Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University Of Medical Sciences, Tehran, Iran
| | - Arezoo Shafieioun
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Aparna Singhal
- Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Alabama, USA
| | - Houman Sotoudeh
- Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Alabama, USA.
- O'Neal Comprehensive Cancer Center, UAB, The University of Alabama at Birmingham, JTN 333, 619 19th St S, Birmingham, AL, 35294, USA.
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