Handcrafted MRI radiomics of enlarged perivascular spaces and machine learning predict cognitive impairment and sleep disturbance in young adults.

Our study seeks to develop a predictive model using MRI-derived enlarged perivascular spaces (EPVSs) measurements and machine learning to assess cognitive impairment, subjective sleep quality, and excessive daytime sleepiness in young adults with long-time mobile phone use (LTMPU). We enrolled 82 participants and employed a pretrained deep learning model (VB-Net) to automatically segment EPVSs lesions across 17 brain subregions, extracting four handcrafted radiomic features – predefined based on morphological properties – per subregion (EPVSs count, volume, mean length, and mean curvature). The cohort was randomly divided into training (80%) and testing (20%) sets. Through minimum redundancy maximum relevance (mRMR) feature selection, six key biomarkers from 68 initial EPVSs metrics were identified combined with sex and age covariates. Final models were constructed using a Gaussian process (GP) classifier for cognitive impairment and decision tree (DT) algorithms for sleep quality and excessive sleepiness assessment. In testing, the GP model achieved an AUC of 0.818 (95% confidence interval [CI] 0.610-1) for cognitive impairment prediction. The DT models showed AUCs of 0.826 (95% CI: 0.616-1) for sleep quality and 0.875 (95% CI: 0.718-1) for daytime sleepiness. This automated radiomics pipeline demonstrates EPVSs morphological features as potential biomarkers for evaluating mobile phone exposure-related neurocognitive dysfunction. This automated radiomics pipeline suggests that EPVSs morphological features might be beneficial for evaluating mobile phone exposure-related neurocognitive dysfunction.