Dr. Eric Tanifum Profile

Dr. Eric Tanifum

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Proceedings Article | 2 April 2024 Poster + Paper

Standardized MR nano-radiomics for early detection and amyloid burden classification in Alzheimer’s disease

Esther Ngan, Andrew Badachhape, Eric Tanifum, Ananth Annapragada, Ketan Ghaghada, Zbigniew Starosolski

Proceedings Volume 12930, 1293026 (2024) https://doi.org/10.1117/12.3008263

KEYWORDS: Magnetic resonance imaging, Radiomics, Alzheimer disease, Brain, Animal model studies, Image segmentation, Neuroimaging, Cerebral cortex

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Extracellular deposits of amyloid-β (Aβ) aggregates are pathological hallmarks of Alzheimer’s disease (AD). In our previous work, we showed that an amyloid-targeted liposomal gadolinium (Gd) contrast agent, ADx-001, demonstrated dose-related varying performance (accuracy 50% - 100%) for in vivo MRI-based detection of amyloid plaques in a mouse model of AD. The goal of this study was to determine if nano-radiomics (radiomic analysis of nanoparticle contrast-enhanced images) could improve performance in differentiating amyloid-positive transgenic (TG) APP/PSEN1 mice and age-matched amyloid-negative Wild Type (WT) mice. Nanoparticle contrast-enhanced MRI (nCE-MRI) was performed using a T1w-SE sequence in wild type (amyloid negative) and transgenic APP/PSEN1 mice (amyloid positive). The effect of ADx-001 dose and plaque burden on the performance of radiomics was determined. nCE-MRI was performed at three ADx-001 dose levels (0.10, 0.15, 0.20 mmol Gd/kg) in mice with high plaque burden and single ADx-001 dose level (0.20 mmol Gd/kg) in mice with low plaque burden. Following semi-automatic registration and segmentation of brain atlas on mouse MR images, two sets of radiomic features (RFs), including the RFs recommended by Image Biomarker Standardization Initiative, were calculated and evaluated for their performance in classifying TG and WT mice. Linear and nonlinear classifiers using RFs were examined to improve the model performance. 5-fold cross-validation was performed to confirm the accuracy of group separation. Nano-radiomic analysis in mice with high plaque burden achieved superb classification performance in terms of accuracy, sensitivity, and specificity, with one universal classifier for all dose levels of ADx-001. In comparison, conventional MR metric of signal enhancement demonstrated dose-related varying performance with suboptimal accuracy (⪅0.7) at lower dose levels. In mice with low plaque burden, radiomic analysis outperformed conventional MR metric for detection of amyloid pathology. In conclusion, nano-radiomics exhibited excellent performance for early detection and amyloid burden classification in a mouse model of Alzheimer’s disease.

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