A systematic analysis of diagnostic performance for Alzheimer's disease using structural MRI. Issue 1 (9th March 2022)

• Record Type: Journal Article
• Authors: Wu, Jiangping; Zhao, Kun; Li, Zhuangzhuang; Wang, Dong; Ding, Yanhui; Wei, Yongbin; Zhang, Han; Liu, Yong
• Is Part Of: Psychoradiology. Volume 2:Issue 1(2022)
• Page Start: 1
• Page End: 9
• Publication Date: 2022-03-09
• Subjects: Alzheimer's disease -- diagnosis -- heterogeneity -- sMRI -- meta-analysis; Brain -- Imaging -- Periodicals; Nervous system -- Radiography -- Periodicals; 616.804757
• DOI: 10.1093/psyrad/kkac001
• Languages: English
• ISSNs: 2634-4416

Abstract

Abstract: Background: Alzheimer's disease (AD) is one of the most common neurodegenerative disorders in the elderly. Although numerous structural magnetic resonance imaging (sMRI) studies have reported diagnostic models that could distinguish AD from normal controls (NCs) with 80–95% accuracy, limited efforts have been made regarding the clinically practical computer-aided diagnosis (CAD) system for AD. Objective: To explore the potential factors that hinder the clinical translation of the AD-related diagnostic models based on sMRI. Methods: To systematically review the diagnostic models for AD based on sMRI, we identified relevant studies published in the past 15 years on PubMed, Web of Science, Scopus, and Ovid. To evaluate the heterogeneity and publication bias among those studies, we performed subgroup analysis, meta-regression, Begg's test, and Egger's test. Results: According to our screening criterion, 101 studies were included. Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies, accompanied by significant heterogeneity. Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI, which included but was not limited to the following aspects: (i) different datasets; (ii) different machine learning models, e.g. traditional machine learning or deep learning model; (iii) different cross-validation methods, e.g. k -fold cross-validation leads to higher accuracies than leave-one-out cross-validation, but both overestimate the accuracy when compared to validation in independent samples; (iv) different sample sizes; and (v) the publication times. We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD. Conclusions: Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI. However, considering the many factors hindering clinical radiology practice, there would still be a long way to go to improve.