Glymphatic system (GS) function and hippocampal microstructural changes are promising imaging markers of Alzheimer’s disease (AD). This study aims to investigate the effectiveness of combining diffusion tensor image analysis along the perivascular space (DTI-ALPS) with hippocampal radiomics for diagnosing AD, and to develop an innovative multivariable model integrating hippocampal radiomics and clinical biomarkers for predicting mild cognitive impairment (MCI) progression. We included three cohorts from two databases retrospectively, using an internal (n = 210) and an external dataset (n = 430) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The ALPS index was employed to measure GS function, and 3D-T1WI hippocampal radiomics features were extracted to construct machine learning models for classifying and diagnosing AD. Conversion of MCI to AD was assessed through integrating the hippocampal radiomics features, ALPS index, and AD-related clinical biomarkers. The ALPS index was lower in patients with AD than in healthy controls (HCs) in both the internal and external cohorts (p < 0.001). The combined hippocampal radiomics features and ALPS index model demonstrated good performance in AD classification. The multivariable prediction model of MCI progression to AD achieved an area under the curve of 0.97 and 0.92 for the training and testing cohorts, respectively. Integrated ALPS index and hippocampal-based radiomics features can improve diagnostic performance in patients with AD, showing predictive capability for identifying the MCI conversion.
Mao et al. (Thu,) studied this question.
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