Structural MRI–based Computer-aided Diagnosis Models for Alzheimer Disease: Insights into Misclassifications and Diagnostic Limitations

邦费罗尼校正 神经影像学 子群分析 医学 计算机辅助设计 认知障碍 疾病 人工智能 内科学 计算机科学 荟萃分析 统计 数学 精神科 生物 生物化学
作者
Xiaopeng Kang,Jiaji Lin,Kun Zhao,Shaozhen Yan,Pindong Chen,Dawei Wang,Hongxiang Yao,Bo Zhou,Chunshui Yu,Pan Wang,Zhengluan Liao,Yan Chen,Xi Zhang,Ying Han,Jie Lu,Yong Liu,for the Alzheimer’s Disease Neuroimaging Initiative
出处
期刊:Radiology [Radiological Society of North America]
卷期号:7 (6): e240508-e240508 被引量:2
标识
DOI:10.1148/ryai.240508
摘要

Purpose To examine common patterns among different computer-aided diagnosis (CAD) models for Alzheimer disease (AD) using structural MRI data and to characterize the clinical and imaging features associated with their misclassifications. Materials and Methods This retrospective study used 3258 baseline structural MRI scans from five multisite datasets and two multidisease datasets collected between September 2005 and December 2019. The 3D Nested Hierarchical Transformer (3DNesT) model and other CAD techniques were used for AD classification using 10-fold cross-validation and cross-dataset validation. Subgroup analysis of CAD-misclassified individuals compared clinical and neuroimaging biomarkers using independent t tests with Bonferroni correction. Results This study included 1391 patients with AD (mean age, 72.1 years ± 9.2 [SD]; 757 female), 205 with other neurodegenerative diseases (mean age, 64.9 years ± 9.9; 117 male), and 1662 healthy controls (mean age, 70.6 years ± 7.6; 935 female). The 3DNesT model achieved 90.0% ± 2.3 cross-validation accuracy and 82.2%, 90.1%, and 91.6% accuracy in three external datasets. Further analysis suggested that the false-negative subgroup (n = 223) exhibited minimal atrophy and better cognitive performance on the Mini-Mental State Examination (MMSE) than the true-positive subgroup (MMSE score in false-negative subgroup, 21.4 ± 4.4; true-positive subgroup, 19.7 ± 5.7; P value family-wise error [PFWE] < .001), despite displaying similar levels of amyloid β (false-negative subgroup, 705.9 pg/mL; true-positive subgroup, 665.7 pg/mL; PFWE = .99) and tau (false-negative subgroup, 352.4 pg/mL; true-positive subgroup, 371.0 pg/mL; PFWE = .99) burden. Conclusion A subgroup of patients with false-negative classification for Alzheimer disease exhibited atypical structural MRI patterns and clinical measures, fundamentally limiting the diagnostic performance of CAD models based solely on structural MRI. Keywords: MR Imaging, Dementia, Computer Applications-3D, Alzheimer's Disease, Computer-aided Diagnosis, Misclassification, Atypical AD Supplemental material is available for this article. © RSNA, 2025 See also commentary by Nasrallah in this issue.
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