Structural MRI-based Computer-aided Diagnosis Models for Alzheimer Disease: Insights into Misclassifications and Diagnostic Limitations.
Authors
Affiliations (15)
Affiliations (15)
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- Department of Neurology, the Second Affiliated Hospital of Air Force Medical University, Xi'an, China.
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China.
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China.
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China.
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China.
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
- National Clinical Research Centre for Geriatric Disorders, Beijing, China.
- Centre of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.
- Alzheimer's Disease Neuroimaging Initiative investigators are listed at https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract
<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> 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 <i>t</i> 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 (<i>n</i> = 223) exhibited minimal atrophy and better cognitive performance than true positive (TP) subgroup (MMSE, FN, 21.4 ± 4.4; TP, 19.7 ± 5.7; <i>P<sub>FWE</sub></i> < 0.001), despite displaying similar levels of amyloid beta (FN, 705.9 ± 353.9; TP, 665.7 ± 305.8; <i>P<sub>FWE</sub></i> = 0.47), Tau (FN, 352.4 ± 166.8; TP, 371.0 ± 141.8; <i>P<sub>FWE</sub></i> = 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.