Nano-radiomics of molecular MRI for early amyloid associated patterns in an Alzheimer's disease mouse model via an...

BACKGROUND: The combination of nanoparticle contrast agents with advanced image analysis techniques like radiomics offers a powerful new approach for disease detection and characterization. This study presents a novel automated pipeline for segmentation and nano-radiomic analysis of nanoprobe-enhanced images. We demonstrate the effectiveness of this pipeline in an Alzheimer’s disease (AD) mouse model, showing improved detection of amyloid pathology compared to conventional methods. METHODS: The study used the magnetic resonance imaging (MRI) data collected from double transgenic (TG) AD mice, including 13 mice aged 6–8 month old with lower amyloid burden and 36 mice aged 11–18 month old with higher amyloid burden. Wild type (WT) mice served as controls. Three contrast doses were administered to older mice, while one dose was applied to younger mice. A UNet-based model was trained on mouse brain scans to automatically segment two specific regions, and the results were compared to those from semi-automatic segmentation (i.e. manual brain atlas registration to MRIs with automatic region extraction). 89 radiomic features (RFs) per region were computed, followed by genotype classification using machine learning and sensitivity analyses. Image-based conventional metric (percentage change between pre- and post-contrast MRI intensity signal) was compared to radiomics-based approaches. RESULTS: The UNet-based model with SWIN transformer performed the best, achieving Dice coefficients between 0.80 and 0.95. Non-parametric statistical tests showed no significant difference in segmentation performance based on amyloid burden, nanoparticle contrast, or contrast doses. RF-based classification performance was similar between automatic and semi-automatic approaches. In older mice with good-quality segmentation, the automatic approach outperformed the semi-automatic method across all dose-specific test sets (all 1.0) but not the validation set (0.90 vs. 1.0). The top classifier from automatic approach also required 2x less RFs. In younger mice, classification performance varied with segmentation approach and inclusion of fair-quality segmentation. Yet the top classifiers typically required only one or two RFs. CONCLUSIONS: Nano-radiomic analysis of targeted nanoparticle contrast-enhanced MRIs outperformed conventional imaging metrics for early detection of pathological amyloid deposition in an AD mouse model. Automated segmentation achieved performance comparable to the semi-automatic method, while improving efficiency.