Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.
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 us...
| Publicado en: | Aging Clinical & Experimental Research Vol. 35; no. 11; pp. 2333 - 2349 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173458729&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173458729 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15940667 2WMP jtl: Aging Clinical & Experimental Research issn: 15940667 maglogo: N pubinfo: dt: Nov2023 vid: 35 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173458729 173359548 173458729 173458729 10.1007/s40520-023-02565-x 173458729 ppf: 2333 ppct: 16 formats: tig: atl: Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis. aug: au: Shahidi, Ramin Baradaran, Mansoureh Asgarzadeh, Ali Bagherieh, Sara Tajabadi, Zohreh Farhadi, Akram Korani, Setayesh Sotoudehnia Khalafi, Mohammad Shobeiri, Parnian Sadeghsalehi, Hamidreza Shafieioun, Arezoo Yazdanifar, Mohammad Amin Singhal, Aparna Sotoudeh, Houman affil: https://ror.org/02y18ts25 School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran sug: subj: Alzheimer's Disease Classification Alzheimer's Disease Diagnosis Cognition Disorders Diagnosis Magnetic Resonance Imaging Methods Sensitivity and Specificity Human Systematic Review Meta Analysis Algorithms Biological Markers PubMed Medline Embase Scales Descriptive Statistics Confidence Intervals ab: 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. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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