Exploring the Potential of Radiomics Features of the Hippocampus in Alzheimer’s Disease Considering Standard versus Parallel Imaging

Abstract

Background
Radiomics features obtained from anatomical magnetic resonance imaging (MRI) have been showing potential as biomarkers for the diagnosis of Alzheimer’s disease (AD). Nevertheless, little is known about how accelerated parallel MRI acquisitions impact such features and their potential in diagnosing AD. Therefore, this study aimed to compare the radiomics features extracted from the hippocampus considering standard and parallel imaging.

Method
A total of 235 age and gender‐matched subjects (128 cognitively normal (CN) and 107 AD) with both T1‐weighted MPRAGE (standard) and accelerated MPRAGE GRAPPA or SENSE images were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Freesurfer was used to process the images and to obtain the volumes of these regions: hippocampus, entorhinal cortex, amygdala, and inferior lateral ventricle (summed bilaterally); and caudal and rostral midfrontal, pars opercularis, pars triangularis, inferior parietal, superior parietal, supramarginal, and superior temporal gyri (all summed into one feature). Using Pyradiomics, the following radiomics features from the bilateral hippocampus were extracted: kurtosis, mean, range, contrast, elongation, flatness, and maximum 3D diameter. To compare the two acquisitions (standard vs parallel), support vector machine models were created to classify AD vs CN. To mitigate AD heterogeneity in ADNI, 5‐fold cross‐validation was used to create 5 train and test sets based on the hippocampus volumes: (each train fold has 80% of 1 st quartile AD and 80% 1 st quartile CN, and the test fold has 20% of 1 st quartile AD and 20% 1 st quartile CN volumes, and so on – randomly selected).

Result
Adding radiomics features improved the classification of AD vs CN in both acquisitions (Table 1): the positive predictive value increased from 77.2% to 90.3% in the standard, while the negative predictive value increased to 84.7% to 91.6% in the parallel acquisition. In both cases, models using all volumes surpassed those using only the hippocampus volume by 7.4% (86.0% balanced accuracy, standard) and 4.2% (85.5%, balanced accuracy, parallel) (Table 1).

Conclusion
Radiomics features of the hippocampus improve the performance of the models in both acquisitions, but with different predictive values, confirming their potential. Also, the relevance of other brain regions in AD besides the hippocampus was observed.