Wu Jiangping, Zhao Kun, Li Zhuangzhuang, Wang Dong, Ding Yanhui, Wei Yongbin, Zhang Han, Liu Yong
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
Psychoradiology. 2022 Mar 9;2(1):287-295. doi: 10.1093/psyrad/kkac001. eCollection 2022 Mar.
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.
To explore the potential factors that hinder the clinical translation of the AD-related diagnostic models based on sMRI.
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.
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. -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.
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.
阿尔茨海默病（AD）是老年人中最常见的神经退行性疾病之一。尽管众多结构磁共振成像（sMRI）研究报告了能够以80% - 95%的准确率将AD与正常对照（NC）区分开来的诊断模型，但针对AD的临床实用计算机辅助诊断（CAD）系统的研究却相对较少。
探讨基于sMRI的AD相关诊断模型临床转化的潜在阻碍因素。
为系统评价基于sMRI的AD诊断模型，我们在PubMed、Web of Science、Scopus和Ovid上检索了过去15年发表的相关研究。为评估这些研究之间的异质性和发表偏倚，我们进行了亚组分析、meta回归、Begg检验和Egger检验。
根据我们的筛选标准，共纳入101项研究。我们的结果表明，近期发表的研究在区分AD与NC方面获得了较高的诊断准确率，但同时存在显著的异质性。meta分析表明，许多因素导致了使用sMRI诊断AD时高诊断准确率的异质性，这些因素包括但不限于以下方面：（i）不同的数据集；（ii）不同的机器学习模型，如传统机器学习或深度学习模型；（iii）不同的交叉验证方法，例如 - 折交叉验证比留一法交叉验证具有更高的准确率，但与独立样本验证相比，两者都高估了准确率；（iv）不同的样本量；以及（v）发表时间。我们推测这些复杂的变量可能是开发AD早期诊断临床适用系统的不利因素。
我们的研究结果证明，先前的研究报告了使用sMRI通过不同模型区分AD与NC的有前景的结果。然而，考虑到阻碍临床放射学实践的诸多因素，仍有很长的路要走才能取得改进。
