Multimodal Radiomics of Precisely Segmented Hippocampal Subfields: Iron Deposition and Structural Biomarkers for Early Diagnosis of Alzheimer's Disease.
Authors
Affiliations (6)
Affiliations (6)
- Medical College, Guizhou University, Guiyang 550000, China; Department of Radiology, Guizhou International Science & Technology Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang 550002, China.
- Department of Radiology, Guizhou International Science & Technology Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang 550002, China; College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University (Zunyi First People's Hospital), Zunyi 563000, China.
- Department of Radiology, Guizhou International Science & Technology Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang 550002, China.
- GE Healthcare, MR Research China, Beijing, China.
- Department of Radiology, Guizhou International Science & Technology Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang 550002, China. Electronic address: [email protected].
Abstract
Profiling imaging biomarkers of prodromal Alzheimer's disease (AD) against AD dementia may aid earlier diagnosis, yet approaches jointly capturing iron-related pathology and hippocampal subfield heterogeneity remain scarce. We developed a hippocampal-subfield multimodal radiomics framework integrating quantitative susceptibility mapping (QSM) and 3D T1-weighted MRI. A primary cohort of 92 participants (50 prodromal AD, 42 AD dementia) and an independent external cohort of 30 (15/15) were included. Twenty-four hippocampal subfields were segmented on super-resolution T1 images and propagated to co-registered QSM for feature extraction. Radiomic features were condensed into a radiomics score (Rad-score) via a training-only selection pipeline. Using the Rad-score as the sole predictor, a support vector machine (SVM) classifier was trained. On the external cohort, the SVM achieved an area under the receiver operating characteristic curve of 0.85 and an accuracy of 0.83. The predictive signature was dominated by QSM texture features in Cornu Ammonis 1 and the granule cell layer of the dentate gyrus, complemented by T1 first-order heterogeneity. Modality ablation suggested potential-but not definitive-complementarity of multimodal integration. This framework shows promise for AD stage classification and warrants further validation in larger independent cohorts.