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

Journal: Radiology. Artificial intelligence
Published Date: Jul 30, 2025

Abstract

Purpose To examine common patterns among different computer-aided diagnosis (CAD) models for Alzheimer's disease (AD) using structural MRI data and to characterize the clinical and imaging features associated with their misclassifications. Materials and Methods This retrospective study utilized 3258 baseline structural MRIs 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 utilized for AD classification using 10-fold cross-validation and cross-dataset validation. Subgroup analysis of CAD-misclassified individuals compared clinical/neuroimaging biomarkers using independent tests with Bonferroni correction. Results This study included 1391 patients with AD (mean age, 72.1 ± 9.2 years, 757 female), 205 with other neurodegenerative diseases (mean age, 64.9 ± 9.9 years, 117 male), and 1662 healthy controls (mean age, 70.6 ± 7.6 years, 935 female). The 3DNesT model achieved 90.1 ± 2.3% crossvalidation accuracy and 82.2%, 90.1%, and 91.6% in three external datasets. Further analysis suggested that false negative (FN) subgroup ( = 223) exhibited minimal atrophy and better cognitive performance than true positive (TP) subgroup (MMSE, FN, 21.4 ± 4.4; TP, 19.7 ± 5.7; < 0.001), despite displaying similar levels of amyloid beta (FN, 705.9 ± 353.9; TP, 665.7 ± 305.8; = 0.47), Tau (FN, 352.4 ± 166.8; TP, 371.0 ± 141.8; = 0.47) burden. Conclusion FN subgroup exhibited atypical structural MRI patterns and clinical measures, fundamentally limiting the diagnostic performance of CAD models based solely on structural MRI. ©RSNA, 2025.

Authors

  • Xiaopeng Kang
    School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Jiaji Lin
    Department of Neurology, the Second Affiliated Hospital of Air Force Medical University, Xi'an, China.
  • Kun Zhao
    Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University Tianjin 300072 P. R. China kunzhao@tju.edu.cn.
  • Shaozhen Yan
    Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45, Changchun Street, Xicheng District, Beijing, 100053, China.
  • Pindong Chen
    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Hongxiang Yao
    Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
  • Bo Zhou
    Department of Neurology, The Third People's Hospital of Yibin, Yibin, China.
  • Chunshui Yu
    Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
  • Pan Wang
  • Zhengluan Liao
    Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China.
  • Yan Chen
    Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Xi Zhang
    The First Clinical Medical College, Guangxi University of Chinese Medicine, Nanning 530001, China.
  • Ying Han
    Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.
  • Jie Lu
    Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
  • Yong Liu
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.

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